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Abstract The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer.

Background: Despite its significant prevalence worldwide, atopy and allergic diseases continue to need more studies, with a need for published articles describing the prevalence of atopy and allergic diseases in Saudi society. The study aimed to determine the prevalence of atopic dermatitis diseases among college students at Imam Mohammad Ibn Saud Islamic University. Methods: The Cross-sectional study was conducted in Saudi Arabia among college students at Imam Mohammed bin Saud Islamic University in Riyadh based on self-report Asthma and Allergies questionnaires (ISSAC questionnaire, atopic dermatitis part). Results: Seven hundred ninety-seven (797) students from 11 colleges participated in the study. About half (47.6%) aged between 21 and 24, and 73.7% were females. More than one-third (34.8%) had atopic dermatitis during the last six months, while 30.7% of the participants reported having itchy rash in the past 12 months, and 33.8% were diagnosed with eczema in any life interval. Age is not one of the significant factors affecting the prevalence of atopic dermatitis. However, the prevalence of atopic dermatitis slightly increases with age (P = 0.062). Atopic dermatitis was significantly higher among female students (39.7%) than 21.0% among male students (P = 0.000). College level nor GPA had no significant impact on the prevalence of atopic dermatitis (P = 0.238 and 0.884, respectively). Conclusion: Imam Mohammad Ibn Saud Islamic University students have a high prevalence of atopic dermatitis, which may indicate a higher prevalence of allergens. Females and older participants were more liable to reported atopic dermatitis.

Remote sensing (RS) scene classification has received significant consideration because of its extensive use by the RS community. Scene classification in satellite images has widespread uses in remote surveillance, environmental observation, remote scene analysis, urban planning, and earth observations. Because of the immense benefits of the land scene classification task, various approaches have been presented recently for automatically classifying land scenes from remote sensing images (RSIs). Several approaches dependent upon convolutional neural networks (CNNs) are presented for classifying brutal RS scenes; however, they could only partially capture the context from RSIs due to the problematic texture, cluttered context, tiny size of objects, and considerable differences in object scale. This article designs a Remote Sensing Scene Classification using Dung Beetle Optimization with Enhanced Deep Learning (RSSC-DBOEDL) approach. The purpose of the RSSC-DBOEDL technique is to categorize different varieties of scenes that exist in the RSI. In the presented RSSC-DBOEDL technique, the enhanced MobileNet model is primarily deployed as a feature extractor. The DBO method could be implemented in this study for hyperparameter tuning of the enhanced MobileNet model. The RSSC-DBOEDL technique uses a multi-head attention-based long short-term memory (MHA-LSTM) technique to classify the scenes in the RSI. The simulation evaluation of the RSSC-DBOEDL approach has been examined under the benchmark RSI datasets. The simulation results of the RSSC-DBOEDL approach exhibited a more excellent accuracy outcome of 98.75 % and 95.07 % under UC Merced and EuroSAT datasets with other existing methods regarding distinct measures.

The pollution of heavy metals (HMs) is a major environmental concern for agricultural farming communities due to water scarcity, which forces farmers to use wastewater for irrigation purposes in Pakistan. Vegetables grown around the cities are irrigated with domestic and industrial wastewater from areas near mining, paint, and ceramic industries that pollute edible parts of crops with various HMs. Cadmium (Cd) is an extremely toxic metal in arable soil that enters the food chain and damages the native biota, ultimately causing a reduction in plant growth and development. However, the use of microbes and growth regulators enhances plant growth and development as well as HM immobilization into the cell wall and hinders their entry into the food chain. Thus, the integrated use of bacterial consortium along with exogenously applied jasmonic acid (JA) mitigates the adverse effect of metal stress, ultimately reducing the metal mobility into roots by soil. Therefore, the current study was conducted to check the impact of Cd-tolerant bacteria and JA on the growth, nutrient status, and uptake of Cd in the cauliflower (Brassica oleracea). Our results demonstrated that increasing concentrations of Cd negatively affect growth, physiological, and biochemical attributes, while the use of a bacterial consortium (SS7 + SS8) with JA (40 μmol L−1) significantly improved chlorophyll contents, stem fresh and dry biomass (19.7, 12.7, and 17.3%), root length and root fresh and dry weights (28.8, 15.2, and 23.0%), and curd fresh and dry weights and curd diameter (18.7, 12.6, and 15.1%). However, the maximum reduction in soil Cd, roots, and curd uptake was observed by 8, 11, and 9.3%, respectively, under integrated treatment as compared to the control. Moreover, integrating bacterial consortium and JA improves superoxide dismutase (SOD) (16.79%), peroxidase dismutase (POD) (26.96%), peroxidase (POX) (26.13%), and catalase (CAT) (26.86%). The plant nitrogen, phosphorus, and potassium contents were significantly increased in soil, roots, and curd up to 8, 11, and 9.3%, respectively. Hence, a consortium of Klebsiella strains in combination with JA is a potential phytostabilizer and it reduces the uptake of Cd from soil to roots to alleviate the adverse impact on cauliflower’s growth and productivity.

Black tea's quality and flavor are largely influenced by its processing stages, which affect its volatile and non-volatile phytochemicals. This study aimed to optimized black tea manufacturing by investigating withering time, fermentation time, and temperature's impact on sensory quality. Using a U*15 (157) uniform design, optimal conditions were determined: 14 h of withering, 5.6 h of fermentation, and a 34 °C temperature. A verification experiment analyzed the volatile and non-volatile profiles. HPLC, GC–MS, and LC-MS revealed dynamic changes in phytochemicals. Among 157 VOCs and 2642 metabolites, 19 VOCs (VIP > 1.5) were crucial for aroma, while 50 (VIP > 1.5, p

Due to the sensitive and mission-critical nature of the data collected and transferred, security in IoT-assisted UAV networks is of great significance. Intrusion detection in IoT-assisted UAV networks includes the deployment of complex monitoring systems to identify and respond to cyberattacks, physical breaches, or unauthorized access. This system employs a combination of anomaly detection and signature-based methods to find malicious or unusual activities within the network. A robust intrusion detection mechanism is essential for protecting the security and integrity of the UAVs and the data collected, ensuring that any possible vulnerabilities are promptly addressed and identified. Consequently, this study introduces an adaptive mongoose optimizer algorithm with a deep learning-based intrusion detection (AMOA-DLID) method in IoT-assisted UAV networks. The AMOA-DLID technique intends to ensure security in the IoT-assisted UAV networks via an intrusion detection process. In the presented AMOA-DLID technique, AMOA is initially applied for the feature selection process. The following sparse autoencoder (SAE) model can be exploited for the recognition of the intrusions. Lastly, the recognition rate of the SAE model can be improved by employing the Harris Hawks optimizer (HHO) technique. The detailed experimental study of the AMOA-DLID model is performed on the benchmark dataset of IDS. The extensive results portrayed that the AMOA-DLID technique reaches improved security over other models on the IoT-assisted UAV networks.

Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs is most important. Land use and land cover (LULC) classification utilizing deep learning (DL) is a common and efficient manner in remote sensing and geospatial study. It is very important in land planning, environmental monitoring, mapping, and land management. But, one of the recent approaches is problems like vulnerability to noise interference, low classification accuracy, and worse generalization ability. DL approaches, mostly Convolutional Neural Networks (CNNs) revealed impressive performance in image recognition tasks, making them appropriate for LULC classification in RSIs. Therefore, this study introduces a novel Land Use and Land Cover Classification employing the River Formation Dynamics Algorithm with Deep Learning (LULCC-RFDADL) technique on RSIs. The main objective of the LULCC-RFDADL methodology is to recognize the diverse types of LC on RSIs. In the presented LULCC-RFDADL technique, the dense EfficientNet approach is applied for feature extraction. Furthermore, the hyperparameter tuning of the Dense EfficientNet method was implemented using the RFDA technique. For the classification process, the LULCC-RFDADL technique uses the Multi-Scale Convolutional Autoencoder (MSCAE) model. At last, the seeker optimization algorithm (SOA) has been exploited for the parameter choice of the MSCAE system. The achieved outcomes of the LULCC-RFDADL algorithm were examined on benchmark databases. The simulation values show the better result of the LULCC-RFDADL methods with other approaches in terms of different metrics.

Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

Abstract Date palm stones are regarded as possible alternatives to activated carbon (AC) precursors with high potential for various environmental applications. In this research study, date palm stones derived activated carbon (DPSAC) was used as adsorbent for removing toxic remazol brilliant blue R (RBBR). The synthesis of DPSAC involved a chemical treatment using KOH and NaOH (1:1). Characterization of DPSAC revealed that it exhibited a BET surface area of 715.30 m2/g, Langmuir surface area of 1061.93 m2/g, total pore volume of 0.39 cm3/g, and average pore diameter of 2.15 nm. Adsorption uptake of RBBR increased (from 24.54 to 248.54 mg/g), whereas the removal percentage decreased (from 98.16 to 82.85%) when the initial RBBR concentration increased (from 25 to 300 mg/L). The adsorption process performed best under acidic conditions (pH 3), with an RBBR uptake of 98.33 mg/g. Because of the high R2 values (0.9906 and 0.9779) and low average errors (6.24 and 13.95%), this adsorption process followed the Freundlich isotherm and pseudo-first-order (PFO) models, respectively. The Langmuir adsorption capacity (Qm) was 319.63 mg/g. Thermodynamic parameters were − 11.34 kJ/mol for ∆H° (exothermic in nature), 0.05 kJ/mol K for ∆S° (increasing randomness level at solid–liquid interface), − 27.37 kJ/mol for ∆G° (spontaneous), and 6.84 kJ/mol for Ea (controlled by physisorption).

Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.

This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.

The neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson’s disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study’s suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.

Gesture recognition in dynamic images is challenging in computer vision, automation and medical field. Hand gesture tracking and recognition between both human and computer must have symmetry in real world. With advances in sensor technology, numerous researchers have recently proposed RGB gesture recognition techniques. In our research paper, we introduce a reliable hand gesture tracking and recognition model that is accurate despite any complex environment, it can track and recognise RGB dynamic gestures. Firstly, videos are converted into frames. After images light intensity adjustment and noise removal, images are passed through CNN for hand gesture extraction. Then from the extracted hand, features are extracted from full hand. Neural gas and locomotion thermal mapping are extracted to make the feature vector. The feature vector are then passed through the fuzzy optimiser to reduce the uncertainties and the fuzziness. The optimised features are then passed to the classifier Deep Belief Network (DBW) for the classification of the gestures. Egogesture and Jester datasets are used for the validation of proposed systems. The experimental results over Egogesture and Jester datasets demonstrate overall accuracies of 90.73% and 89.33% respectively. The experiments proves our system readability and suitability of our proposed model with the other state of the arts model.

Automated rice variety detection and classification is a task that includes automatically categorizing and identifying varieties or different types of rice based on different characteristics namely grain texture, shape, color, and size. This process is essential for quality assessment, agricultural management, and research purposes. Deep learning (DL) is a subfield of machine leaching (ML) that focuses on training an artificial neural network (ANN) with multiple layers to learn hierarchical representations of data. Convolutional Neural Network (CNN) was widely applied in image-based tasks such as rice variety detection, as they could efficiently capture visual features and patterns. In this study, we propose an Automated Rice Variety Detection and Classification using Quantum Inspired Moth Flame Optimizer with Deep Learning (ARVDC-QIMFODL) technique. The presented ARVDC-QIMFODL technique focuses on the automated identification and classification of distinct kinds of rice varieties. To accomplish this, the ARVDC-QIMFODL technique uses the Median modified wiener filter (MMWF) technique for the noise removal process. Followed by, the feature extraction process takes place by an improved ShuffleNet model. For rice variety detection and classification, the long short-term memory (LSTM) approach was applied. At last, the QIMFO algorithm-based hyperparameter selection process is performed to optimize the detection results of the LSTM system. The simulation outcome of the ARVDC-QIMFODL method is tested on a rice image dataset. An extensive set of experiments showed the remarkable efficiency of the ARVDC-QIMFODL system over other models.

Biomedical image analysis has played a pivotal role in modern healthcare by facilitating automated analysis and interpretation of medical images. Biomedical image classification is the process of automatically labelling or categorizing medical images based on their content. In recent years, this field has received considerable attention because of the abundance of bio-medical image data and the potential for deep learning (DL) algorithms to assist medical staff in identifying diseases and making treatment decisions. DL methods are mostly convolutional neural networks (CNN) has illustrated outstanding performance in analyzing and classifying biomedical images. Therefore, this study presents a new Hybrid Metaheuristics with Deep Learning based Fusion Model Biomedical Image Analysis (HMDL-MFMBIA) technique. The HMDL-MFMBIA technique initially performs image pre-processing and Swin-UNet-based segmentation. Besides, a fusion of multiple DL-based feature extractors takes place using Xception and Residual Network (ResNet) model. Moreover, a hybrid salp swarm algorithm (HSSA) was employed for the optimal hyperparameter selection of the DL models. Finally, the gated recurrent unit (GRU) algorithm can be exploited for the detection and classification of bio-medical images. A widespread of simulated is conducted to establish the enhanced biomedical image classification results of the HMDL-MFMBIA method. The simulation outcomes inferred the greater outcome of the HMDL-MFMBIA algorithm over other DL models.

Laryngeal cancer detection on throat area images is a vital application of medical image diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and detection of cancerous or abnormal tissues from the larynx, an essential part of the respiratory and vocal systems. Several machine learning (ML) and deep learning (DL) systems are executed for classifying the extraction features as both cancerous and healthy tissue. Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) have shown promise in this context. With this motivation, this study designs a new Laryngeal Cancer Detection and Classification using the Aquila Optimization Algorithm with Deep Learning (LCDC-AOADL) technique on neck region images. The purpose of the LCDC-AOADL technique is to examine the histopathological images for the recognition and classification of Laryngeal Cancer. In the presented LCDC-AOADL technique, the Inceptionv3 model is used for the feature extraction process. Besides, the LCDC-AOADL technique employed a deep belief network (DBN) model for the identification and classification of LC. Moreover, the AOA is utilized for the hyperparameter tuning of the DBN model which results in improved detection rate. The simulation analysis of the LCDC-AOADL method is validated on the benchmark Laryngeal dataset. The experimental results pointed out the enhanced detection results of the LCDC-AOADL technique over other recent approaches with a maximum accuracy of 96.02%, precision of 92.10%, recall of 91.87%, and F-score of 91.86%.

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms.

Recognition of human locomotor activities is crucial for monitoring the motion patterns. Current studies for human locomotor activities recognition focused on detecting basic motion patterns. In this study, we proposed a four-modules-based human locomotor recognition model via deep learning, which will support in identifying two signal patterns including static and kinematic motions and classifying the daily activities across different subjects. These motion patterns have been monitored through visual devices along with physical and ambient sensors to extract the complex and basic motion from distinct data forms. The four modules include processing, extraction, optimization, and recognition. Each module focuses on certain processing elements for human locomotion recognition. The processing module represents the pre-processing and segmentation stages for motion and ambient-based data along with extraction of human skeleton points from the visual data. Next, the extraction phase focuses on motion patterns identification and features extraction from the multisensors-based data. Then, the optimization module helps in prominent features selection via genetic algorithm. Furthermore, the recognition module utilized a deep learning technique called hidden Markov model to detect human locomotor activities. The average accuracy rates of 73.05% and 71.14% have been achieved for high-level and atomic-level activities over both datasets. The experimental results have shown that the proposed model outperforms the conventional multisensory systems based on deep classifiers via confidence levels for each skeleton point extracted.

Background: Children suffer from orofacial injuries during sport activities in schools. School teacher's knowledge about managing emergency during orofacial injuries will help in treatment prognosis. Aim: To assess and compare school teacher's knowledge about management of orofacial injuries at alkharj Saudi Arabia. Materials and Methods: Cross-sectional study was planned among school teacher working in various schools at alkharj. Questionnaire consisted of Demographic details such as age and year of experience and knowledge-related questions about management of orofacial injuries. Questionnaire was distributed and collected on the same day. Results: All participants were aware that sport activities results in injuries to head, orofacial structures, or teeth. 15.6% participants have undergone training. 84.4% of study participants have told they don't know to manage avulsed tooth. 92.8% of study participants are interested to get training on orofacial injury management. Conclusion: Knowledge of school teachers about management of orofacial injury is insufficient. Teachers are primary level of contact in schools during sports injuries, measures should be taken to improve their knowledge about orofacial injuries.

Human action recognition is critical because it allows machines to comprehend and interpret human behavior, which has several real-world applications such as video surveillance, robot-human collaboration, sports analysis, and entertainment. The enormous variety in human motion and appearance is one of the most challenging problems in human action recognition. Additionally, when drones are employed for video capture, the complexity of recognition gets enhanced manyfold. The challenges including the dynamic background, motion blur, occlusions, video capture angle, and exposure issues gets introduced that need to be taken care of. In this article, we proposed a system that deal with the mentioned challenges in drone recorded red-green-blue (RGB) videos. The system first splits the video into its constituent frames and then performs a focused smoothing operation on the frames utilizing a bilateral filter. As a result, the foreground objects in the image gets enhanced while the background gets blur. After that, a segmentation operation is performed using a quick shift segmentation algorithm that separates out human silhouette from the original video frame. The human skeleton was extracted from the silhouette, and key-points on the skeleton were identified. Thirteen skeleton key-points were extracted, including the head, left wrist, right wrist, left elbow, right elbow, torso, abdomen, right thigh, left thigh, right knee, left knee, right ankle, and left ankle. Using these key-points, we extracted normalized positions, their angular and distance relationship with each other, and 3D point clouds. By implementing an expectation maximization algorithm based on the Gaussian mixture model, we drew elliptical clusters over the pixels using the key-points as the central positions to represent the human silhouette. Landmarks were located on the boundaries of these ellipses and were tracked from the beginning until the end of activity. After optimizing the feature matrix using a naïve Bayes feature optimizer, the classification is performed using a deep convolutional neural network. For our experimentation and the validation of our system, three benchmark datasets were utilized i.e., the UAVGesture, the DroneAction, and the UAVHuman dataset. Our model achieved a respective action recognition accuracy of 0.95, 0.90, and 0.44 on the mentioned datasets.

With the latest advancements, hand gesture recognition is becoming an effective way of communication and gaining popularity from a research point of view. Hearing impaired people around the world need assistance, while sign language is only understood by a few people around the globe. It becomes challenging for untrained people to communicate easily, research community has tried to train systems with a variety of models to facilitate communication with hearing impaired people and also human-computer interaction. Researchers have detected gestures with numerous recognition rates; however, the recognition rate still needs improvement. As the images captured via cameras possess multiple issues, the light intensity variation makes it a challenging task to extract gestures from such images, extra information in captured images, such as noise hinders the computation time, and complex backgrounds make the extraction of gestures difficult. A novel approach is proposed in this paper for character detection and recognition. The proposed system is divided into five steps for hand gesture recognition. Firstly, images are pre-processed to reduce noise and intensity is adjusted. The pre-processed images region of interest is detected via directional images. After hand extraction, landmarks are extracted via a convex hull. Each gesture is used to extract geometric features for the proposed hand gesture recognition (HGR) system. The extracted features helped in gesture detection and recognition via the Convolutional Neural Network (CNN) classifier. The proposed approach experimentation result demonstrated over the MNIST dataset achieved a gesture recognition rate of 93.2% and 90.2% with one-third and two-third training validation systems, respectively. Also, the proposed system performance is validated on the ASL dataset, giving accuracy of 91.6% and 88.14% with one-third and two-third training validation systems, respectively. The proposed system is also compared with other conventional systems. Different emerging domains such as human-computer interaction (HCI), human-robot interaction (HRI), and virtual reality (VR) are applicable to the proposed system to fill the communication gap.

The world’s expanding populace, the variety of human social factors, and the densely populated environment make humans feel uncertain. Individuals need a safety officer who generally deals with security viewpoints for this frailty. Currently, human monitoring techniques are time-consuming, work concentrated, and incapable. Therefore, autonomous surveillance frameworks are necessary for the modern day since they are able to address these problems. Nevertheless, hardships persist. The central concerns incorporate the detachment of the foreground from the scene and the understanding of the contextual structure of the environment for efficiently identifying unusual objects. In our work, we introduced a novel framework to tackle these difficulties by presenting a semantic segmentation technique for separating a foreground object. In our work, Super-pixels are generated using an improved watershed transform and then a conditional random field is implemented to obtain multi-object segmented frames by performing pixel-level labeling. Next, the Social Force model is introduced to extract the contextual structure of the environment via the fusion of a novel chosen particular histogram of an optical stream and inner force model. After using the computed social force, multi-people tracking is performed via three-dimensional template association using percentile rank and non-maximal suppression. Next, multi-object categorization is performed via deep learning Feature Pyramid Network. Finally, by considering the contextual structure of the environment, Jaccard similarity is utilized to make the decision for abnormality detection and identify the unusual objects from the scene. The invented framework is verified through rigorous investigations, and it obtained multi-people tracking efficiency of 92.2% and 89.1% over the UCSD and CUHK Avenue datasets. However, 95.2% and 93.7% abnormality detection efficiency is accomplished over UCSD and CUHK Avenue datasets, respectively.

Autonomous vehicle detection and tracking are crucial for intelligent transportation management and control systems. Although many techniques are used to develop smart traffic systems, this article discusses vehicle detection and tracking using pixel-labeling and real-time tracking. We propose a novel smart traffic control system that segments the image using an Extreme Gradient Boost (XGBoost) classifier to extract the foreground objects. The proposed model is divided into the following steps: 1) at first, all the images are preprocessed to remove noise; 2) pixel-labeling is performed by using the XGBoost classifier to separate the background from the foreground; 3) all the pixels classified as a vehicle was extracted and converted into a binary image, then blob extraction technique is used to localize each vehicle; 4) to verify the detected vehicles Intersection over Union (IoU) score using the ground truth is calculated; 5) all verified vehicles were subjected to Visual Geometry Group (VGG) feature extraction and based on which a unique identifier was assigned to each of them to enable multi-object tracking across the image frames; 6) vehicles are counted and categorized into stationary and moving cars by detecting motion in each of them using Farneback optical flow algorithm; and 7) finally, the Simple Online and Real-time Tracker (SORT) is used for tracking. The proposed model outperforms existing state-of-the-art traffic monitoring techniques in terms of precision, achieving 0.86 for detection and 0.92 for tracking with the Karlsruher Institut for Technology Aerial Image Sequences (KIT-AIS) dataset, 0.83 for detection, and 0.87 for tracking with the Vision Meets Drone Single Object-Tracking (VisDrone) dataset. The proposed system can be used for several purposes, such as vehicle identification in traffic, traffic density detection at intersections, traffic flow conditions on the road, and providing a pedestrian way.

The advancement of computer vision technology has led to the development of sophisticated algorithms capable of accurately recognizing human actions from red-green-blue videos recorded by drone cameras. Hence, possessing an exceptional potential, human action recognition also faces many challenges including, tendency of humans to perform the same action in different ways, limited camera angles, and field of view. In this research article, a system has been proposed to tackle the forementioned challenges by using red-green-blue videos as input while the videos were recorded by drone cameras. First of all, the video was split into its constituent frames and then gamma correction was applied on each frame to obtain an optimized version of the image. Then the Felzenszwalb’s algorithm performed the segmentation to segment out human from the input image and human silhouette was generated. Utilizing the silhouette, skeleton was extracted to spot thirteen body key points. The key points were then used to perform elliptical modeling to estimate the individual boundaries of the body parts while the elliptical modeling was governed by the Gaussian mixture model-expectation maximization algorithm. The elliptical models of the body parts were utilized to spot fiducial points that if tracked, could provide very useful information about the performed action. Some other features that were extracted for this study include, the 3d point cloud feature vector, relative distance and velocity of the key-points, and their mutual angles. The features were then forwarded for optimization under a quadratic discriminant analysis and finally, a convolutional neural network was trained to perform the action classification. Three benchmark datasets including, the Drone-Action dataset, the UAV-Human dataset, and the Okutama-Action dataset were used for a comprehensive experimentation. The system outperformed the state-of-the-art approaches by securing accuracies of 80.03%, 48.60%, and 78.01% over the Drone-Action dataset, the UAV-Human dataset, and the Okutama-Action dataset respectively.

Surveillance system is acquiring an ample interest in the field of computer vision. Existing surveillance system usually relies on optical or wearable sensors for indoor and outdoor activities. These sensors give reasonable performance in a simulation environment. However, when used under realistic settings, they could cause a large number of false alarms. Moreover, in a real-world scenario, positioning a depth camera at too great a distance from the subject could compromise image quality and result in the loss of depth information. Furthermore, depth information in RGB images may be lost when converting a 3D image to a 2D image. Therefore, extensive surveillance system research is moving on fused sensors, which has greatly improved action recognition performance. By taking into account the concept of fused sensors, this paper proposed a novel idea of a modified K-Ary entropy classifier algorithm to map the arbitrary size of vectors to a fixed-size subtree pattern for graph classification and to solve complex feature selection and classification problems using RGB-D data. The main aim of this paper is to increase the space between the intra-substructure nodes of a tree through entropy accumulation. Hence, the likelihood of classifying the minority class as belonging to the majority class has been reduced. The working of the proposed model has been described as follows: First, the depth and RGB images from three benchmark datasets have been taken as the input for the model. Then, using 2.5D cloud point modeling and ridge extraction, full-body features, and point-based features have been retrieved. Finally, for the efficacy of the surveillance system, a modified K-Ary entropy accumulation classifier is optimized by the probability-based incremental learning (PBIL) algorithm has been used. In both qualitative and quantitative experimental results, the testing results have shown 95.05%, 95.56%, and 95.08% performance over SYSU-ACTION, PRECIS HAR, and Northwestern-UCLA (N-UCLA) datasets. The proposed system could apply to various real-world emerging applications like human target tracking, security-critical human event detection, perimeter security, internet security, public safety etc.

In the fields of body-worn sensors and computer vision, current research is being done to track and detect falls and activities of daily living using the automatic recognition of human actions. In the area of human–machine communication, different combinations of sensors and communication technologies are often used to capture human action. Many researchers have also worked with artificial intelligent systems to detect actions, understand scenes, and implement systems that are more efficient in human action recognition. Although effective approaches are needed to detect outdoor activities with the combination of human actions, feature extraction can be quite a complicated task in a human activity recognition system development. Thus, this paper proposed a solution to detect human activities via hybrid descriptors based on robust features and accurate results. In this study, complex backgrounds, including multiple humans in video frames, were detected. First, inertial signal and video frames are pre-processed using denoising techniques, after which the frames are used to remove the background by detecting human motions and extracting the silhouettes. Then, these silhouettes are further used to extract the human body key points to make the human skeleton. Then the time and frequency domain features are extracted for inertial signals, and geometric features are extracted for the skeleton body points. Finally, multiple feature sets are combined and fed into a zero order optimization model, after which logistic regression is utilized to recognize each action. The proposed system has been evaluated on three benchmark datasets, including, the UP Fall dataset, the University of Rzeszow Fall dataset, and the SisFall dataset and proved its significance by achieving accuracy of 91.51%, 92.98%, and 90.23%, on the aforementioned datasets respectively.

Surface sediment samples from Al-Qahma lagoon in the southern part and Al-Wajh lagoon in the northern part of the Red Sea coast of Saudi Arabia were collected by a Van Veen grab sampler to determine the characteristics, distribution, and sources of aliphatic and cyclic hydrocarbons. The total extractable organic matter (TEOM) was extracted with a dichloromethane/methanol mixture after drying and sieving the sediments and identified by gas chromatography–mass spectrometry. The TEOM comprised n-alkanes (302.6 ± 446.7 ng·g−1 and 64 ± 50 ng·g−1), hopanes (29.8 ± 132.3 ng·g−1 and 1.0 ± 2.5 ng·g−1), steranes (0.0 and traces), n-alkanoic acids (745.8 ± 799.6 ng·g−1 and 120.7 ± 92.0 ng·g−1), n-alkanols (457.4 ± 1085.6 ng·g−1 and 49.7 ± 32.3 ng·g−1), polycyclic aromatic hydrocarbons (PAHs) (54.5 ± 96.8 ng·g−1 and 7.8 ± 8.5 ng·g−1), and phthalates (185.3 ± 169.9 ng·g−1 and 67.4 ± 70.4 ng·g−1) in the Al-Qahma and Al-Wajh lagoon sediments, respectively. The percentages of the various sources relative to total aliphatic and cyclic hydrocarbon concentrations were 6.9 ± 6% for terrestrial plants, 53.7 ± 19% for algae, 10 ± 2% for microbial, 16 ± 12% for petroleum, and 13.4 ± 7 for plasticizer inputs in Al-Qahma lagoon. In Al-Wajh lagoon, they were 9.7 ± 4% for terrestrial plants, 30.8 ± 14% for algae, 25.2 ± 5% for bacteria, 11.2 ± 3% for petroleum, and 23.1 ± 11% for plasticizers.

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods.

Date palm trees generate large amounts of various types of waste, including leaf sheath fibres, which can be used as a low-cost precursor for the production of biochar, including activated carbon (AC), which can be employed for the adsorption of contaminants. In the current study, activated carbon was produced from leaf sheath fibres of date palms (LSDPFAC) by the use of chemical activation with K2CO3 combined with microwave irradiation, and it was characterised and evaluated for its adsorptive capacity of lead ions (Pb2+). The Brunauer–Emmett–Teller (BET) surface area, Langmuir surface area, total pore volume and average pore diameter of the LSDPFAC were 560.20 m2/g, 744.31 m2/g, 0.29 cm3/g and 2.47 nm, respectively. A greater adsorption of Pb2+ was observed when its concentration was higher in the solution, and the greatest adsorption capacity of 5.67 mg Pb/g was observed at the highest pH. The results of isotherm and kinetic studies demonstrated that the adsorption of Pb2+ onto the LSDPFAC was best described by the Freundlich isotherm and pseudo-second-order (PSO) models. The Langmuir ΔG° and Ea were 6.39 kJ/mol, 0.12 kJ/mol K, −31.28 kJ/mol and 15.90 kJ/mol, respectively, which demonstrated that the adsorption of Pb2+ by the LSDPFAC was endothermic, spontaneous and governed by physisorption.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Runtime validation techniques are required because complex self-adaptive systems must consistently offer acceptable behavior for important services. The runtime testing can offer further confidence that a self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for self-adaptive systems. The objective is to ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm facilitates both the execution and adaptation of runtime testing operations.

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.

Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system’s accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system’s performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject’s location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Gestures have been used for nonverbal communication for a long time, but human–computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected through the images, and the skeleton is computed through mathematical computations. From the skeleton, the features are extracted; these features include joint color cloud, neural gas, and directional active model. After that, the features are optimized, and a selective feature set is passed through the classifier recurrent neural network (RNN) to obtain the classification results with higher accuracy. The proposed model is experimentally assessed and trained over three datasets: HaGRI, Egogesture, and Jester. The experimental results for the three datasets provided improved results based on classification, and the proposed system achieved an accuracy of 92.57% over HaGRI, 91.86% over Egogesture, and 91.57% over the Jester dataset, respectively. Also, to check the model liability, the proposed method was tested on the WLASL dataset, attaining 90.43% accuracy. This paper also includes a comparison with other-state-of-the art methods to compare our model with the standard methods of recognition. Our model presented a higher accuracy rate with a markerless approach to save money and time for classifying the gestures for better interaction.

Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment’s time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms.

Textile dyes are organic compounds that can pose an environmental threat if not properly treated. They can cause many problems ranging from human health, ecosystem disturbances, and the reduction of the esthetic value of water bodies. The adsorption process using activated carbon (AC) has been proven to be effective in treating dyes in wastewater. However, the production of AC is limited by the non-renewables and relatively expensive precursor of coal. Date palm residues (DPRs) provide a good alternative for AC’s precursor due to their continuous supply, availability in a large amount, and having good physiochemical properties such as high oxygen element and fixed carbon. This study provides a review of the potential of date palm residues (DPRs) as AC in adsorbing textile dyes and the recent technological advances adopted by researchers in producing DPR-based AC. This review article focuses solely on DPR and not on other biomass waste. This study presents a background review on date palms, textile dyes, biochar, and AC, followed by production methods of AC. In the literature, DPR was carbonized between 250 and 400°C. The conventional heating process employed an activation temperature of 576.85–900°C for physical activation and a maximum of 800°C for physicochemical activation. Chemical agents used in the chemical activation of DPR included NaOH, KOH, ZnCl2, H3PO4, and CaCl2. The maximum surface area obtained for DPR-AC was 1,092.34 and 950 m2/g for physical and chemical activation, respectively. On the other hand, conditions used in microwave heating were between 540 and 700 W, which resulted in a surface area of 1,123 m2/g. Hydrothermal carbonization (HTC) utilized carbonization temperatures between 150 and 250°C with pressure between 1 and 5 MPa, thus resulting in a surface area between 125.50 and 139.50 m2/g. Isotherm and kinetic models employed in the literature are also discussed, together with the explanation of parameters accompanied by these models. The conversion of DPR into AC was noticed to be more efficient with the advancement of activation methods over the years.

The elevation is an important gradient across which the environmental variables and plant traits vary and is considered as a barrier to the recent global problem of plant invasion. However, certain invasive plants show plasticity traits to adapt and cope with the changes across the elevation. Silybum marianum (S. marianum) is one such invasive species widely spread in Khyber Pakhtunkhwa, Pakistan. Therefore, this study investigates the traits plasticity and invasive behaviors of this plant species across the elevation gradient. Plant functional traits (PFTs) and environmental variables were recorded in forty different low, middle, and high elevation sites. The plant shows a decrease in plant functional traits, i.e., above-ground plant height/plant, leaf length/leaf, leaf width/leaf, leaf dry weight/plant, vegetative dry weight/plant, and number of capitula/plant having the significance of p < 0.05. In contrast, the dry reproductive weight does not change significantly with elevation, while the root length increases across the elevation. The soil and environmental variables such as organic matter, lime percentage, and latitude significantly affected the PFTs. The importance value index of the species was also related to elevation and diversity indices, i.e., species richness, Shannon–Wiener diversity index, and evenness index, indicating that the invasion has strong effects on diversity. This study concludes that S. marianum has traits plasticity across the elevation and affects community diversity. Further investigation is required to understand the invasion and diversity parameters in a better way.

The forecast of the stock price attempts to assess the potential movement of the financial exchange’s stock value. The exact estimation of the movement of share price would contribute more to investors’ profit. This paper introduces a new stock market prediction model that includes three major phases: feature extraction, optimal feature selection, and prediction. Initially, statistical features like mean, standard deviation, variance, skewness, and kurtosis is extracted from the collected stock market data. Further, the indexed data collected are also computed concerning standard indicators like Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Index (RSI), and Rate of Change (ROC). To acquire best-predicted results, it is more crucial to select the most relevant features. Such that, the optimal features are selected from the extracted features (technical indicators based features, statistical features) by a new hybrid model referred to Red Deer Adopted Wolf Algorithm (RDAWA). Further, the selected features are subjected to the ensemble technique for predicting the stock movement. The ensemble technique involves the classifiers like Support Vector Machine (SVM), Random Forest1 (RF1), Random Forest2 (RF2), and optimized Neural Network (NN), respectively. The final predicted results are acquired from the Optimized Neural Network (NN). To make the precise prediction, the training of NN is carried out by the proposed RDAWA via fine-tuning the optimal weight. Finally, the performance of the proposed work is compared over other conventional models with respect to certain measures.

The size of neural networks in deep learning techniques is increasing and varies significantly according to the requirements of real-life applications. The increasing network size and scalability requirements pose significant challenges for a high performance implementation of deep neural networks (DNN). Conventional implementations, such as graphical processing units and application specific integrated circuits, are either less efficient or less flexible. Consequently, this article presents a system-on-chip (SoC) solution for the acceleration of DNN, where an ARM processor controls the overall execution and off-loads computational intensive operations to a hardware accelerator. The system implementation is performed on a SoC development board. Experimental results show that the proposed system achieves a speed-up of 22.3, with a network architecture size of $64\times 64$ , in comparison with the native implementation on a dual core cortex ARM-A9 processor. In order to generalize the performance of complete system, a mathematical formula is presented which allows to compute the total execution time for any architecture size. The validation is performed by taking Epileptic Seizure Recognition as the target case study. Finally, the results of the proposed solution are compared with various state-of-the-art solutions in terms of execution time, scalability, and clock frequency.

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework.

In this study, we aimed to develop mixture activated carbon (MAC) from a mixture of date palm wastes (petiole, rachis and fiber) through hydrothermal carbonization and microwave-assisted H _3 PO _4 activation and evaluate its adsorption capacity to remove Remazol brilliant blue R (RBBR) from aqueous solution. The MAC was found to be mesoporous, with an average pore diameter and BET surface area of 2.61 nm and 641.23 m ^2 g ^−1 , respectively. The zeta potential and FTIR results demonstrated that the surface of MAC was negatively charged in its natural state and filled with functional groups such as phenyl, secondary amine, alkyne, aldehyde, and hydroxy groups. The adsorption capacity and percentage removal of RBBR increased and decreased, respectively, when the initial concentration increased. Maximum removal of RBBR was achieved at pH 3 (96.69 mg g ^−1 ) and at 30 °C of solution temperature (85.79 mg g ^−1 ). Langmuir and pseudo-first-order were the best isotherm and kinetic models for describing the RBBR-MAC adsorption system, respectively. The thermodynamic parameters of ΔH°, ΔS°, ΔG°, and E _a implied that this adsorption system was exothermic, increased randomness at the liquid-solid interface, and was spontaneous and controlled by the physisorption type of sorption, respectively.

Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models.

Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634.

Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.

A brain tumor (BT) is an abnormal development of brain cells that causes damage to the nerves and blood vessels. An accurate and early diagnosis of BT is important to prevent future complications. Precise segmentation of the BT provides a basis for surgical and planning treatment to physicians. Manual detection utilizing MRI images is computationally difficult. Due to significant variation in their structure and location, viz., ambiguous boundaries and irregular shapes, computerized tumor diagnosis is still a challenging task. The application of a convolutional neural network (CNN) helps radiotherapists categorize the types of BT from magnetic resonance images (MRI). This study designs an evolutional algorithm with a deep learning-driven brain tumor MRI image classification (EADL-BTMIC) model. The presented EADL-BTMIC model aims to accurately recognize and categorize MRI images to identify BT. The EADL-BTMIC model primarily applies bilateral filtering (BF) based noise removal and skull stripping as a pre-processing stage. In addition, the morphological segmentation process is carried out to determine the affected regions in the image. Moreover, sooty tern optimization (STO) with the Xception model is exploited for feature extraction. Furthermore, the attention-based long short-term memory (ALSTM) technique is exploited for the classification of BT into distinct classes. To portray the increased performance of the EADL-BTMIC model, a series of simulations were carried out on the benchmark dataset. The experimental outcomes highlighted the enhancements of the EADL-BTMIC model over recent models.

Smart cities and E-governance in smart cities has been the emerging topic in this twenty-first century. The rapid developments and experiences in advancing and utilizing the applications of smart city for the continuous smart E-governance have evolved as an interesting topic to concentrate. According to Information and Communication Technology (ICT), the aims and paradigm of the smart city are to provide an integrated infrastructure and manage proper E-governance with a mission of next-generation cities. The smart delivery services which are opting the ICT-based E-governance are facilitated with an interactivity, say Internet through which the citizens can enjoy the best facilities of smart city. As the growing popularity of smart city and its applications, the communication between the government and the citizens is also raising the possibility of infringement attempts. To address this issue, we come up with an advanced multi-factor user authentication scheme which can be utilized for the smart E-governance applications in smart cities. The lightweight nature of our scheme and resistance to many network attacks, including low computational overhead show beyond doubt that our scheme is efficient and applicable to E-governance applications in smart city. A formal verification performed using AVISPA tool confirms the security of the proposed scheme.

Background: Road traffic accident (RTA) is one of the major causes of facial injuries apart from other body injuries. Aims and Objectives: The aim of this study was to assess the effectiveness of law enforcement system (LES) on maxillofacial injuries caused by RTAs in Riyadh. Materials and Methods: The objective was achieved by conducting a comparison of RTA - related facial injuries before and after the introduction of Saher. A retrospective study on patients admitted to Prince Sultan Medical City Oral and maxillofacial surgery department of 10 years divided equally before and after the introduction of the LES. Data were collected from the hospital's electronic database regarding the patients' age, sex, gender, date, time, and type of injury. Results: The severity and the number of injuries from RTAs were considerably reduced with the introduction of the system. Conclusion: LES plays a huge role toward controlling how road users use the roads and consequently, the number of accidents reported.

As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.