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Predicting a client’s affluence is essential in financial services. This task is the unity of the most important danger factors in groups and additional economic institutions. Typically, credit risk evaluation relies on black box models. However, these models often need to clarify the hidden information within the data. Moreover, few clear models focus on being easy to understand and accessible. This paper proposes a fractal credit risk assessment model that uses deep techniques like self-attention generative adversarial networks (SA-GAN) and deep multi-layer perceptron (DMLP). We use blockchain technology with the Brakerski–Gentry–Vaikuntanathan (BGV) encryption method to bolster safekeeping. Additionally, the scheme is designed for the Edge-of-things network, enabling communication through a LoRaWAN server. The proposed solution was tested on the German retail credit dataset. We assessed its performance using accuracy, F1 score, precision, and recall as metrics. Notably, our hybrid deep model, which combines SA-GAN with DMLP, achieved an impressive accuracy of 97.8% — outperforming existing methods in works. [ABSTRACT FROM AUTHOR]

Dermatologic oncology’s precision medicine revolutionizes skin cancer detection by integrating advanced technologies and personalized patient data. Dermatologic oncology concentrates on detecting skin cancer, utilizing modern techniques and technologies to detect and classify several kinds of cutaneous malignancies. Leveraging medical knowledge or advanced imaging approaches like dermoscopy and reflectance confocal microscopy; dermatologists effectively investigate skin cancer for subtle signs of malignancy. Furthermore, computer-aided diagnostic (CAD) systems, controlled by machine learning (ML) methods, are gradually deployed to boost diagnostic accuracy by investigating massive datasets of dermatoscopic images. It is a multi-disciplinary method that allows early recognition of skin lesions and enables precise prognostication and particular treatment approach, finally enhancing patient outcomes in dermatologic oncology. This paper presents the Fractals Snake Optimization with Deep Learning for Accurate Classification of Skin Cancer in Dermoscopy Images (SODL-ACSCDI) approach. The purpose of the SODL-ACSCDI approach is to identify and categorize the existence of skin cancer on Dermoscopic images. The SODL-ACSCDI technique applies a contrast enhancement process as the initial step. Next, the SODL-ACSCDI technique involves the SE-ResNet+FPN model for deriving intrinsic and complex feature patterns from dermoscopic images. Additionally, the SO technique can help boost the hyperparameter selection of the SE-ResNet+FPN approach. Furthermore, skin cancer classification uses the convolutional autoencoder (CAE) approach. The experimentation results of the SODL-ACSCDI technique could be examined using a dermoscopic image dataset. A wide-ranging result of the SODL-ACSCDI technique indicated a superior performance of 99.61% compared to recent models concerning various metrics. [ABSTRACT FROM AUTHOR]

Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder deep learning ensemble technique incorporating attention blocks. This innovative approach leverages the strengths of encoder-decoder architectures and attention mechanisms to enhance the precision and reliability of ear detection and segmentation. Specifically, our method employs an ensemble of two YSegNets, which significantly improves the performance over a single YSegNet. The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. By combining the outputs of two YSegNets, our approach can capture a wider range of features and reduce the risk of false positives and negatives, leading to more robust and accurate segmentation results. Experimental validation of the proposed method was conducted using a combination of data from the EarVN1.0, AMI, and Human Face datasets. The results demonstrate the effectiveness of our approach, achieving a segmentation framework accuracy of 98.93%. This high level of accuracy underscores the potential of our method for practical applications in biometric identification. The proposed innovative method demonstrates significant potential for individual recognition, particularly in scenarios involving large gatherings. When complemented by an effective surveillance system, our method can contribute to improved security and identification processes in public spaces. This research not only advances the field of ear biometrics but also provides a viable solution for biometric identification in the context of mask-wearing and other facial obstructions. [ABSTRACT FROM AUTHOR]

Consumer electronics (CE) companies have the potential to significantly contribute to the advancement of autonomous vehicles and their accompanying technology by providing security, connectivity, and efficiency. The Consumer Autonomous Vehicles market is set for significant growth, driven by growing awareness and implementation of sustainable practices using computing technologies for traffic flow optimization in smart cities. Businesses are concentrating more on eco-friendly solutions, using AI, communication networks, and sensors for autonomous city navigation, giving safer and more efficient mobility solutions in response to growing environmental concerns. Object detection is a crucial element of autonomous vehicles and complex systems, which enables them to observe and react to their surroundings in real-time. Multiple autonomous vehicles employ deep learning (DL) for detection and deploy specific sensor arrays custom-made to their use case or environment. DL processes sensory data for autonomous vehicles, enabling data-driven decisions on environmental reactions and obstacle recognition. This paper projects a Galactical Swarm Fractals Optimizer with DL-Enabled Object Detection for Autonomous Vehicles (GSODL-OOAV) model in Smart Cities. The presented GSODL-OOAV model enables the object identification for autonomous vehicles properly. To accomplish this, the GSODL-OOAV model initially employs a RetinaNet object detector to detect the objects effectively. Besides, the long short-term memory ensemble (BLSTME) technique was exploited to allot proper classes to the detected objects. A hyperparameter tuning procedure utilizing the GSO model is employed to enhance the classification efficiency of the BLSTME approach. The experimentation validation of the GSODL-OOAV technique is verified using the BDD100K database. The comparative study of the GSODL-OOAV approach illustrated a superior accuracy outcome of 99.06% over present innovative approaches. [ABSTRACT FROM AUTHOR]

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimizationbased machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches. [ABSTRACT FROM AUTHOR]

With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested in performance analysis for selecting the optimum routing system. With this motivation, this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication (COAER-UAVC) technique. The presented COAERUAVC technique establishes effective routes for communication between the UAVs. It is primarily based on the coati characteristics in nature: if attacking and hunting iguanas and escaping from predators. Besides, the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay. A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system. The experimental results verified that the COAERUAVC technique had assured improved performance over other approaches. [ABSTRACT FROM AUTHOR]

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, ehealthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively. [ABSTRACT FROM AUTHOR]

Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWORWOD) in Smart Cities. The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique involves the design of deep consensus network (DCN) to detect waste objects in the input image. For improving the overall object detection performance of the DCN model, the whale optimization algorithm (WOA) is exploited. Finally, Naïve Bayes (NB) classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of theDCNWO-RWOD technique takes place using the open access dataset. The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others.Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max normalization approach as a pre-processing stage. Next, the owl search algorithm (OSA)-based feature selection (OSA-FS) model is used to derive feature subsets. Then, beetle swarm antenna search (BSAS) algorithm with Iterative Dichotomiser 3 (ID3) classification method was implied for ASD detection and classification. The design of BSAS algorithm helps to determine the parameter values of the ID3 classifier. The performance analysis of the ASDC-OSAML model is performed using benchmark dataset. An extensive comparison study highlighted the supremacy of the ASDC-OSAML model over recent state of art approaches. [ABSTRACT FROM AUTHOR]

Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate information. To precisely extract accurate inner structural data, a multiple centroids approach is employed and defined in this paper, which defines the cluster configuration by allocating weights to each state in the cluster. The multiple EEG view fuzzy learning approach incorporates data from every single view to enhance the model's clustering performance. Also a fuzzy Density-Based Clustering model with multiple centroids (FDBC) is presented. This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm. The Experimental results validate the medical importance of the proposed clustering model. [ABSTRACT FROM AUTHOR]

In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed for clinical decision making process. On the other hand, Cardiovascular Disease (CVD) is one of the major illnesses that increase the mortality rate across the globe. Generally, wearables can be employed in healthcare systems that instigate the development of CVD detection and classification. With this motivation, the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment, abbreviated as AIDSS-CDDC technique. The proposed AIDSS-CDDC model enables the Internet of Things (IoT) devices for healthcare data collection. Then, the collected data is saved in cloud server for examination. Followed by, training and testing processes are executed to determine the patient's health condition. To accomplish this, the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection (ISCO-FS) technique. In addition, Adam optimizer with Autoencoder Gated RecurrentUnit (AE-GRU)model is employed for detection and classification of CVD. The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models. [ABSTRACT FROM AUTHOR]

Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer usingCT images. The proposedDLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature extraction. Also, long short term memory (LSTM) model was applied for classifying the existence of lung cancer in the CT images. Moreover, moth swarm optimization (MSO) algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate, batch size, and epoch count. For demonstrating the improved classifier results of the DLCADLC-BCT approach, a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem inwireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises amassive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique forWSN. The goal of theQBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques. [ABSTRACT FROM AUTHOR]

Feelings influence human beings' decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people's preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multicriteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multiattribute selection procedure that incorporates both regret and rejoice to select the best choice. We utilize the positional advantage operator concept to develop regret and rejoice mathematical equations, and prove them. The proposed MCDM procedure that incorporates these two emotional factors offers a decision-maker the flexibility to trade off some benefits in order to gain a state of psychological satisfaction. More specifically, regret and rejoice are presentedmathematically to enable the decision-maker to determine the values of regret and rejoice, and then make the decision in which the rejoice value is higher than the regret value. To test the performance of this new procedure, we apply it to three numerical examples proposed in previous works. The results are matched with those obtained by other methods such as the regret model, VIKOR, PROMETHEE I, and PROMETHEE II, thereby proving the efficacy of the new procedure. [ABSTRACT FROM AUTHOR]

Strabismus is a medical condition that is defined as the lack of coordination between the eyes. When Strabismus is detected at an early age, the chances of curing it are higher. The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming, and they always require the presence of a physician. In this paper, we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test. Our method involves extracting features from a set of training videos (training corpora) and using them to build a classifier. A decision tree (ID3) is built using labeled cases from actual strabismus diagnosis. Patterns are extracted from the corresponding videos of patients, and an association between the extracted features and actual diagnoses is established. Matching Rules from the correlation plot are used to predict diagnoses for future patients. The classifier was tested using a set of testing videos (testing corpora). The results showed 95.9% accuracy, 4.1% were light cases and could not be detected correctly from the videos, half of them were false positive and the other half was false negative. [ABSTRACT FROM AUTHOR]