Search

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.

IntroductionGlobal Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.MethodsThis paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.ResultsOn the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.DiscussionThese findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model’s use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

The Internet of Things (IoT) represents a dynamic infrastructure, leveraging sensing and network communication technology to establish ubiquitous connectivity among people, machines, and objects. Due to its end devices’ limited computing resources and storage space, it is not feasible to merely transpose traditional internet security technologies directly to IoT endpoints. Maintaining security while concurrently ensuring performance is a particularly challenging endeavor. This paper provides a review of key agreements and authentication protocols pivotal to the security of IoT. First, this survey discusses the applications that need authentication and key agreement to strengthen their security and current research on these application fields. Subsequently, this paper engages in an in-depth exploration of the phase involved in the scheme of authentication and key agreement, including an examination of the cryptographic techniques employed within these processes. This survey also thoroughly studies the scheme’s security services, potential attacks, formal analysis and informal analysis to ensure resilience against such threats. This study aims to provide a profound understanding of the recent research on authentication and key agreement in IoT applications. It strives to contribute towards strengthening security systems for IoT applications, ensuring their sustainability in the face of evolving threats.

In contemporary society, the proliferation of online hateful messages has emerged as a pressing concern, inflicting deleterious consequences on both societal fabric and individual well-being. The automatic detection of such malevolent content online using models designed to recognize it, holds promise in mitigating its harmful impact. However, the advent of “Hateful Memes” poses fresh challenges to the detection paradigm, particularly within the realm of deep learning models. These memes, constituting of a textual element associated with an image are individually innocuous but their combination causes a detrimental effect. Consequently, entities responsible for disseminating information via web browsers are compelled to institute mechanisms that regulate and automatically filter out such injurious content. Effectively identifying hateful memes demands algorithms and models endowed with robust vision and language fusion capabilities, capable of reasoning across diverse modalities. This research introduces a novel approach by leveraging the multimodal Contrastive Language-Image Pre-Training (CLIP) model, fine-tuned through the incorporation of prompt engineering. This innovative methodology achieves a commendable accuracy of 87.42%. Comprehensive metrics such as loss, AUROC, and f1 score are also meticulously computed, corroborating the efficacy of the proposed strategy. Our findings suggest that this approach presents an efficient means to regulate the dissemination of hate speech in the form of viral meme content across social networking platforms, thereby contributing to a safer online environment.

Nanotechnologists and medical researchers are working hard to develop new and innovative ways to use nanorobots as nanomedicine to improve healthcare outcomes and revolutionize the field of therapeutics. Nanotechnology has the potential to revolutionize healthcare by providing new ways of treating chronic diseases in the field of medicine. A “Gold Nano Thermo Robot” (GNTR) model has been proposed in this research article, which can be considered a nanomedicine that will deliver controlled thermal therapy to targeted malignant tissues without damaging healthy tissues. The proposed nanotherapeutic system, empowered with a nano sensor network, interbody body communication network, and Internet of nanomedical things, has been used to normalize and control hyperthermal waves in real-time that have been used to eliminate breast cancer cells using the “SEE and TREAT” technique. To generate hyperthermia, which has been irradiated by laser pulses to propose GNTR, a Coulomb explosion took place, and a huge amount of dispersed hyperthermia waves were produced. To convert the intensity of dispersed and irregular hyperthermia into a regulated and disciplined format, a Finite Difference Method has been used to develop a “Heat Control System.” A comparative analysis has been provided of the intricate relationship between the required radius of Gold Nano Thermo Robots and the volume depth of the tumor for penetration, with a keen focus on evaluating how different GNTR sizes fit or do not fit for the task of effectively treating tumors at various depths within cancer tumors. Furthermore, the effectiveness of treatment has multifaceted outcomes that have been acquired by the interplay between two critical factors, the temperature limit and therapy duration. By examining a comprehensive matrix of thermal therapy durations (ranging from 25 minutes to 60 minutes) alongside various temperature limits (ranging from 33°C to 60°C). The best fit and the best response therapy session have been verified with a temperature limit of 42 °C for 30 minutes, achieving near-complete tumor ablation with minimum harm to the healthy tissues. The complex physical effects on the Gold Nano Robots surfaces due to the Coulomb explosion procedure are also provided in the form of simulation analysis, and an explanation is given in nine panels.

In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The weight of each client is dynamically adjusted according to its contribution to the global model improvement. Experiments on public ECG datasets show that the proposed approach outperforms traditional federated learning and centralized learning methods in terms of accuracy and robustness. On the client side, the suggested federated learning (FL) approach had an accuracy of 0.93, sensitivity of 0.98, specificity of 0.82, miss classification rate of 0.07, precision of 0.06, FPR of 0.01, and FNR of 0.01. FL has 0.98 accuracy, 0.99 sensitivity, 0.91 specificity, 0.02 miss classification rate, 0.10 precision, 0.01, FPR, and 0.01 FNR on the server. The server-side federated learning approach outperforms the client-side in accuracy, sensitivity, specificity, miss classification rates, and precision. The results indicate that the proposed weighted federated learning approach is a promising solution for ECG arrhythmia classification in a distributed environment. In short, the proposed federated learning approach applied to ECG arrhythmia detection aims to address privacy concerns and improve accuracy, while still maintaining the centralized framework and advanced algorithmic approach.

Swarm intelligence (SI) is the collective behavior of several intelligent agents (IAs), and unmanned aerial vehicles (UAV) or drones are widely used as IA in SI, of which UAV-based decentralized networks are one. Since the UAV can move in different directions without any surface in the 3D environment, the problem of UAV-to-UAV (UAV2UAV) based interaction is observed in UAV-based SI processes. In a UAV-based network, each UAV needs to follow the 3D flying pattern, and if there is any problem in determining the 3D position pattern, the network loses its arrangement, which may cause chaos in the entire network system. 3D movement of biological animal is observed based on these six relative directions: right, left, up, down, back, and front. The aim of this study is to establish an IA-enabled interaction based on the mentioned 6 relative directions in the UAV-based SI process to overcome the raised issues. First, the article presents various relative directional 3D positions of a UAV and then proposes a novel UAV-to-UAV Relative Directional Interaction (UAV2UAV-RDI) model. Here, two different algorithms are performed on two different UAVs to execute an optimization-based evolutionary computation for detecting and tracking the relative directional 3D position of a UAV. Consequently, according to the findings of this research, the interaction between two UAVs occurs and one UAV can track the relative directional 3D position of the other UAV. The performance of the proposed model is compared with the three models where the proposed model performed more directionally. Additionally, we also point out the limitations of the proposed model for future improvements.

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.

Multiterminal High-Voltage Direct Current (MT-HVDC) systems offer numerous benefits compared to conventional alternating current (AC) power systems, including higher power density and improved efficiency. However, the need for adequate protection schemes for HVDC systems remains a significant obstacle to their widespread adoption. Much attention has been given to developing HVDC protection methods to address this. Moreover, the protection of MT-HVDC systems presents a challenge due to bidirectional power flow, dynamic system characteristics, and fault current characteristics that cannot be addressed using conventional methods. This paper represents a centralized protection unit based on a distance protection scheme that involves a two-stage relay process. The first stage involves fault detection by measuring voltage and current to obtain the system impedance, which is then compared to the reach point. The second stage involves identifying the fault location by selecting the correct faulty zone. This technique provides both main and backup protection. A central control unit supports the approach presented in this paper to communicate relays and update their settings. The LoRaWAN communication protocol is employed, as it provides more excellent coverage than other standardized communication technologies and can cover long distances. The proposed method is studied under different scenarios, including system contingency, simultaneous faults, fault resistances, locations, and types. The results of this technique provide the effectiveness of the proposed method. The fault can be cleared within 1.32–1.8 ms.

Abstract This study focuses on enhancing indoor air quality and thermal comfort in indoor swimming pool facilities through the investigation of ventilation system configurations. Creating a comfortable and healthy environment in these facilities is crucial for the well-being of occupants and overall operational efficiency. The performance of the ventilation system significantly influences user comfort, energy consumption, and air quality. This research aims to analyze the impact of different ventilation system configurations on indoor air quality and thermal comfort parameters using computational fluid dynamics (CFD) simulations. To achieve the research objectives, CFD simulations were conducted using ANSYS Fluent ®, a widely used commercial CFD package. The simulations involved solving the governing equations for continuity, momentum, energy, and species transport, along with employing the k-epsilon turbulence closure model. A high-resolution mesh with over 5.6 million elements accurately captured the flow regimes and related phenomena. The study investigated various aspects of ventilation system configurations, including the placement and design of inlets and outlets, airflow rates, and distribution patterns. Evaluations were made based on key performance indicators such as indoor air quality parameters, thermal comfort indices, and energy efficiency metrics. Comparisons were made between different configurations to identify the most effective strategies for enhancing indoor air quality and thermal comfort. The findings of the study demonstrate the importance of ventilation system design in achieving optimal indoor air quality and thermal comfort in indoor swimming pool facilities. The results indicate that specific configuration choices, such as the use of circular inlets in the ceiling for improved spectator comfort and rectangular inlets in the side walls for better performance in the swimming pool area, can significantly impact thermal conditions and air distribution. Additionally, the study emphasizes the need for appropriate inlet grille height to ensure adequate air mixing and thermal comfort. The outcomes of this research provide valuable insights for architects, engineers, and facility managers involved in the design, construction, and operation of indoor swimming pool facilities. By understanding the impact of different ventilation system configurations, stakeholders can make informed decisions to optimize indoor air quality, thermal comfort, and energy efficiency. Ultimately, this research contributes to the development of sustainable and comfortable indoor swimming pool environments that cater to the needs of occupants and enhance their overall experience.

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs.

The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia disease. Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. If both parents are beta Thalassemia carriers, 25% of their children will have intermediate or major beta thalassemia, which is fatal. An efficient method of beta thalassemia is prenatal screening after couples have received counselling. Identifying Thalassemia carriers involves a costly, time-consuming, and specialized test using quantifiable blood features. However, cost-effective and speedy screening methods must be developed to address this issue. The demise rate due to thalassemia development is outstandingly high around the globe. The passing rate due to thalassemia development can be reduced by following the proper procedure early; otherwise, it significantly impacts the body. A machine learning-based late fusion model proposes the detection of beta-thalassemia carriers by analyzing red blood cells. This study applied the late fusion technique to employ four machine learning algorithms. For identifying the beta-thalassemia carriers, logistics regression, Naïve Bayes, decision tree, and neural network have achieved an accuracy of 94.01%, 93.15%, 97.93%, and 98.07%, respectively, by using the features-based dataset. The late fusion-based ML model achieved an overall accuracy of 96% for detecting beta-thalassemia carriers. The proposed late fusion model performs better than previously published approaches regarding efficiency, reliability, and precision.

Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been identified by various means such as ear tags, tattoos, rumen implants, and hot brands. Being non-scientific approaches, these controls can be easily circumvented. The emerging technologies of biometric identification are extensively applied for Human recognition via thumb impression, face features, or eye retina patterns. The application of biometric recognition technology has now moved towards animals. Cattle identification with the help of muzzle patterns has shown tremendous results. For precise identification, nature has awarded a unique Muzzle pattern that can be utilized as a primary biometric feature. Muzzle pattern image scanning for biometric identification has now been extensively applied for identification. Animal recognition via Muzzle pattern image for different applications has been proliferating gradually. One of those applications includes the identification of fake insurance claims under livestock insurance. Fraudulent animal owners tend to lodge fake claims against livestock insurance with proxy animals. In this paper, we proposed the solution to avoid and/or discard fraudulent claims of livestock insurance by intelligently identifying the proxy animals. Data collection of animal muzzle patterns remained challenging. Key aspects of the proposed system include: (1) the Animal face will be detected through visual using YOLO v7 object detector. (2) After face detection, the same procedures will apply to detect muzzle point (3) the muzzle pattern is extracted and then stored in the database. The System has a mean average precision of 100% for the face and 99.43% for the nose/muzzle point of the animal. Once the animal is registered in the database, the identification process is initiated by extracting unique nose pattern features with ORB and/or SIFT. Then it is matched using the pattern matchers like BFMatcher and/or FLANNMatcher for animal identification. The proposed model is more efficient and accurate as compared to concurrent approaches. The results extracted from this research study show 100% accurate identification.

Parkinson’s Disease (PD) in the set of neuro-degenerative disorders stimulates due to the loss of dopaminergic neurons from the substantia nigra. Electroencephalogram (EEG) signals are being extensively utilized for diagnosing PD. The existing approaches extract the features using various frequency transformations that lose valuable signal information. An optimized Deep Convolutional Neural Network (CNN) inspired by the encoder part of U-Net architecture is proposed for classifying PD incorporating the resting electroencephalogram (EEG) signal dataset. The proposed model follows the U-Net architecture for extracting the features from the signals. The EEG recordings are taken from two datasets: the University of Mexico (UNM) EEGs and the University of California San Diego (UCSD) resting state dataset. The EEGs are pre-processed with a basic pre-processing pipeline, then separated into single channels, plotting each channel as a simple graph. These graphs are then fed to the proposed 23-layered convolutional neural network (CNN) for classifying PD from the normal control. Consequently, the model achieved maximum values of 93.10%, 93.18%, 93.09%, and 0.9313 of accuracy, precision, recall, and F1-score respectively for the UNM dataset, whereas, 97.90%, 98%, 97.87% and 0.9794 of accuracy, precision, recall, and F1-score respectively for UCSD dataset. The results show improved scores compared to the individual Machine Learning and CNN models applied on the same datasets.

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The Google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.

One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularity of online items. The popularity of an item can be measured by its attractiveness, i.e., the number of times it is being used. This popularity prediction can be translated to a link prediction and ranking problem, which aims to predict the link gain of the items in a user-item interaction network. User-item interactions in an online environment can be modelled as a bipartite network, where a link represents an event, reflecting a user buys or collects an item. Popularity prediction problem in temporal bipartite networks is of great interest to researchers. In this study, we propose a heuristic based model which only consider nodes collective link gain in a recent past time window of time as well as total link gain. To evaluate our model’s efficiency, we tested them on co-evolving social media items. We also evaluated the models’ performance on five information retrieval metrics (i.e., Area Under the Receiver Operating Characteristic, Kendall’s rank correlation tau, Precision, Novelty, and temporal novelty). The proposed model does not need hyper-parameter learning, which makes it the best choice for highly temporal and data streaming scenarios.

Electronic Health monitoring system has performed an essential role in managing healthcare monitoring. E-health can provide effective and valuable facilities for the patients to monitor. Though, there are protection disputes in the current E-Health system. The current e-health system, on the other hand, has security issues. Malevolent doctors may work together with cloud Storage Service Providers (CSPs) to interfere with patients' electronic health records (EHRs) or promptly leak EHR matter to other enemies for income. (EHRs). The malevolent doctors may conspire with the Patient Healthcare Monitoring Service Provider (PHMSP) to manipulate with the patients'. For profit, EHRs or directly divulge the EHR content of EHRs to other opponents. Block-chain has recently appeared as one of the most powerful methods in the protection and secrecy fields. It is assumed to be the promised security approach that will eventually replace the security challenges in existing e-health monitoring systems. Encryption in blockchain refers to technical methods that make accessing encrypted data difficult for unauthorized resources. This research proposed a blockchain-based encryption framework to provide security-based solutions using a computational intelligence methodology. The proposed approach provides better results in terms of 0.93 in the training phase and 0.91 in the validation accuracy.

Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches.

Background: Endophytic fungi are associated within plant tissues without any harmful effects. They produce a diversity of metabolites and are well known as rich source of bioactive substances. This study aimed to identify fungal isolates from different medicinal plants, evaluate their antioxidant potency and detect components responsible for antioxidant activity. Results: In the present investigation, 4 endophytic fungal isolates were obtained from different medicinal plants Rumex nervosus, Pulicaria crispa and Withania somnifera. Those isolates were characterized as Penicilluim commune, Penicilluim glaucoroseum, Aspergillus flavipes and Fusarium chlamydosporum according to morphological and phylogenetic bases. These strains were propagated on humified rice and their crude ethyl acetate extracts were assayed for their total phenols, flavonoids and antioxidant potency against DPPH free radicals. The results showed that the elevated constituent of phenolics was found in P. commune (148.24 mg/g), followed by F. chlamydosporum (124.25 mg/g), while the highest total flavonoids were determined in A. flavipes, followed by F. chlamydosporum, P. glaucoroseum, P. commune, respectively. Moreover, the maximum hydroxyl radical-scavenging assay was IC50 = 2.13 mg/mL exhibited by P. commune, followed by F. chlamydosporum (2.68 mg/mL). Weak antimicrobial and cytotoxic effects were reported by the studied strains against pathogenic isolates and (HepG2) hepatocellular carcinoma cell line. The fungal EtOAc extracts were characterized quantitatively by the incidence of different phenols, flavonoids, and phenolic acids, as detected by HPLC. Conclusions: Endophytic fungi derived from medicinal plants are a promising candidate for the wide industrial production of antioxidant agents with several useful medical and pharmaceutical applications.How to cite: Hassane A. M. A, Taha T. M, Awad M. F, et al. Radical scavenging potency, HPLC profiling and phylogenetic analysis of endophytic fungi isolated from selected medicinal plants of Saudi Arabia. Electron J Biotechnol 2022;58. https://doi.org/10.1016/j.ejbt.2022.05.001

Blockchain technology was initially implemented for cryptocurrencies in 2009, but it caught the undivided attention of multiple industries such as finance, supply chain management, healthcare, and governments. This research was set out to investigate and evaluate six benefits of blockchain for marketing: fostering disintermediation, combating click fraud, reinforcing trust and transparency, enhancing privacy protection, empowering digital marketing security, and enabling creative loyalty programs. An empirical study in the form of an online survey was conducted to examine the realization of benefits in practice. The research concluded that blockchain does indeed provide promising benefits for marketing, but that depends on whether marketers use public (permissioned) blockchain or private (permissioned) blockchain, and also the ability of the blockchain community to resolve fundamental challenges and pending issues such as scalability, speed, interoperability, and privacy, besides several many others.

Abstract The recent advancements of Internet of Things (IoT) embedded systems, wireless networks, and biosensors those have assisted in the rapid development of implanting wearable sensors are reviewed here. The applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services are also reviewed here. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain‐based approaches that can help in improving the confidentiality of IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Despite multiple security threats, there are novel cryptographic techniques, such as access control, identity authentication, and data encryption that can help in improving the security and reliability of IoMT devices.

Requirement change management plays an important role in the business world where management in the business scenario is a hard-hitting assignment because of continuously changing customer choices in respect of requirements. Giving no attention to requirement change management challenges result in consumers’ discontent. Main cause of business products is that there is no planning regarding requirement change management. Furthermore, it also affects market value. Dealing in requirement change management, it’s far important to cope with those challenges to undertake the requirement of the business consumers. This article focuses on documentation and control of quality requirement challenges by using an approach of systematic literature review. The main goal of this article is to classify critical challenges being faced by vendor companies in global software development. A total of fourteen challenges have been documented which have a severe effect on the management of quality requirements. Challenges documented like ‘Incomplete requirements’, ‘Lack of Communication & Coordination, etc. are the key challenges harming managing quality requirement changes. Among these fourteen challenges, nine challenges are marked as critical challenges whose ratios are above 25%. The identified challenges were analyzed decade wise where we categorized them into three decades i.e. first decade (1992-2002), the second decade (2003-2012), and the third decade (2013-2021).

Transforming from digitalization to digitization brings many new technologies to restructure our life and life routines. In today’s competitive world, internet infrastructures and banking industries are counted as integral components for online shopping and commercial transactions. As because, disclosure of online transactions has been allowed through internet media, that would enhance the availability of electronic payment systems. Further, this study aims to explore and investigate the relationship and impact of electronic payment methods on the sales growth with the mediating role of online shopping by targeting UAE banking Industry. This study followed the quantitative approach and a correlational design. The empirical data were collected through a survey designed on a 5-point Likert scale, 217 valid questionnaires were sent to all participants (i.e., top managers, middle managers and technicians) via emails. Different statistical analyses were performed in this study. The results of the study showed high internal consistency among the study variables as Cronbach’s Alpha values ranged from .873 to .855. Further, this study highlighted the significant relationship and direct impact between online shopping and sales growth. Meanwhile, indirect impact was confirmed by the results between online shopping and sales growth through e-payment. In this regard, results can help to identify the impact of e-payment on sales growth through online shopping and also provide advantage for this and many other similar organizational studies.

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods.

Visually impaired persons (VIPs) comprise a significant portion of the population and they are present in all corners of the world. In recent times, the technology proved its presence in every domain of life and innovative devices are assisting humans in all fields especially, artificial intelligence has dominated and outperformed the rest of the trades. VIPs need assistance in performing daily life tasks like object/obstacle detection and recognition, navigation, and mobility, particularly in indoor and outdoor environments. Moreover, the protection and safety of these people are of prime concern. Several devices and applications have been developed for the assistance of VIPs. Firstly, these devices take input from the surrounding environment through different sensors e.g. infrared radiation, ultrasonic, imagery sensor, etc. In the second stage, state of the art machine learning techniques process these signals and extract useful information. Finally, feedback is provided to the user through auditory and/or vibratory means. It is observed that most of the existing devices are constrained in their abilities. The paper presents a comprehensive comparative analysis of the state-of-the-art assistive devices for VIPs. These techniques are categorized based on their functionality and working principles. The main attributes, challenges, and limitations of these techniques have also been highlighted. Moreover, a score-based quantitative analysis of these devices is performed to highlight their feature enrichment capability for each category. It may help to select an appropriate device for a particular scenario.

Cancer accounts for a huge mortality rate due to its aggressiveness, colossal potential of metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon cancers are among the most prevalent types of cancer around the globe that can occur in both males and females. Early and accurate diagnosis of these cancers can substantially improve the quality of treatment as well as the survival rate of cancer patients. We propose a highly accurate and computationally efficient model for the swift and accurate diagnosis of lung and colon cancers as an alternative to current cancer detection methods. In this study, a large dataset of lung and colon histopathology images was employed for training and the validation process. The dataset is comprised of 25000 histopathology images of lung and colon tissues equally divided into 5 classes. A pretrained neural network (AlexNet) was tuned by modifying the four of its layers before training it on the dataset. Initial classification results were promising for all classes of images except for one class with an overall accuracy of 89%. To improve the overall accuracy and keep the model computationally efficient, instead of implementing image enhancement techniques on the entire dataset, the quality of images of the underperforming class was improved by applying a contrast enhancement technique which is fairly simple and efficient. The implementation of the proposed methodology has not only improved the overall accuracy from 89% to 98.4% but has also proved computationally efficient.

Urdu language is being spoken by over 64 million people and its Roman script is very popular, especially on social networking sites. Most users prefer Roman Urdu over English grammar for communication on social networking platforms such as Facebook, Twitter, Instagram and WhatsApp. For research, Urdu is a poor resource language as there are a few research papers and projects that have been carried out for the language and vocabulary enhancement in comparison to other languages especially English. A lot of research has been made in the domain of sentiment analysis in English but only a limited work has been performed on the Roman Urdu language. Sentiment analysis is the method of understanding human emotions or points of view, expressed in a textual form about a particular thing. This article proposes a deep learning model to perform data mining on emotions and attitudes of people using Roman Urdu. The main objective of the research is to evaluate sentiment analysis on Roman Urdu corpus containing RUSA-19 using faster recurrent convolutional neural network (FRCNN), RCNN, rule-based and N-gram model. For assessment, two series of experiments were performed on each model, binary classification (positive and negative) and tertiary classification (positive, negative, and neutral). Finally, the evaluation of the faster RCNN model is analyzed and a comparative analysis is performed for the outcomes of four models. The faster RCNN model outperformed others as the model achieves an accuracy of 91.73% for binary classification and 89.94% for tertiary classification.

Abstract Global land cover (LC) changes threaten sustainability and yet we lack a comprehensive understanding of the gains and losses of LC types, including the magnitudes, locations and timings of transitions. We used a novel, fine-resolution and temporally consistent satellite-derived dataset covering the entire Earth annually from 1992 to 2018 to quantify LC changes across a range of scales. At global and continental scales, the observed trajectories of change for most LC types were fairly smooth and consistent in direction through time. We show these observed trajectories in the context of error margins produced by extrapolating previously published accuracy metrics associated with the LC dataset. For many LC classes the observed changes were found to be within the error margins. However, an important exception was the increase in urban land, which was consistently larger than the error margins, and for which the LC transition was unidirectional. An advantage of analysing the global, fine spatial resolution LC time-series dataset is the ability to identify where and when LC changes have taken place on the Earth. We present LC change maps and trajectories that identify locations with high dynamism, and which pose significant sustainability challenges. We focused on forest loss and urban growth at the national scale, identifying the top 10 countries with the largest percentages of forest loss and urban growth globally. Crucially, we found that most of these ‘worst-case’ countries have stabilized their forest losses, although urban expansion was monotonic in all cases. These findings provide crucial information to support progress towards the UN’s SDGs.

Oxidative stress is involved in the pathophysiology of multiple health complications, and it has become a major focus in targeted research fields. As known, black seeds are rich sources of bio-active compounds and widely used to promote human health due to their excellent medicinal and pharmaceutical properties. The present study investigated the antioxidant potency of various black seeds from plants and their derived mycoendophytes, and determined the total phenolic and flavonoid contents in different extracts, followed by characterization of major constituents by HPLC analysis. Finally, in silico docking determined their binding affinities to target myeloperoxidase enzymes. Ten dominant mycoendophytes were isolated from different black seed plants. Three isolates were then selected based on high antiradical potency and further identified by ITS ribosomal gene sequencing. Those isolated were Aspergillus niger TU 62, Chaetomium madrasense AUMC14830, and Rhizopus oryzae AUMC14823. Nigella sativa seeds and their corresponding endophyte A. niger had the highest content of phenolics in their n-butanol extracts (28.50 and 24.43 mg/g), flavonoids (15.02 and 11.45 mg/g), and antioxidant activities (90.48 and 81.48%), respectively, followed by Dodonaea viscosa and Portulaca oleracea along with their mycoendophytic R. oryzae and C. madrasense. Significant positive correlations were found between total phenolics, flavonoids, and the antioxidant activities of different tested extracts. The n-butanol extracts of both black seeds and their derived mycoendophytes showed reasonable IC50 values (0.81–1.44 mg/ml) compared to the control with significant correlations among their phytochemical contents. Overall, seventeen standard phenolics and flavonoids were used, and the compounds were detected in different degrees of existence and concentration in the examined extracts through HPLC analysis. Moreover, the investigation of the molecular simulation results of detected compounds against the myeloperoxidase enzyme revealed that, as a targeted antioxidant, rutin possessed a high affinity (−15.3184 kcal/mol) as an inhibitor. Taken together, the black seeds and their derived mycoendophytes are promising bio-prospects for the broad industrial sector of antioxidants with several valuable potential pharmaceutical and nutritional applications.

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions covering the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that features selection, and data sampling was necessary pre-processing steps that improve the performance of ensemble classifiers.

Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.

Increasing man-machine trust has burgeoned during the last few decades. The growing interest in trust-building has led to the study of the non-dichotomous nature of trust. Trust as social behavior is an integral part of effective team building. The major focus has been offered to study how humans build trust towards machines, whereas few attempts have been made to study the reverse. Studies have shown that trustworthiness perceptions initialize trust behavior whereas trust behavior influences subsequent trustworthiness perceptions. This paper presents the design and comparative analysis of evidential fuzzy multi-criteria decision-making (EFMCDM) based on multi-dimensional trust quantification schemes to quantify trust level with the human agent in a collaborative environment.

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence (AI) and machine learning (ML) play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate.

A large proportion of rural households, particularly in the dry land areas, representative for more than 10% of the world's land surface and up to 80% in Morocco, depend for their livelihoods on livestock. They exploit livestock's capacity to live in very harsh environments using herd-mobility at multiple scale level. Understanding the multiple contributions of livestock to the household and national economy raises complex research issues and challenges linked with the multitude of goods and services derived from livestock, their interactions with other family activities, and the local and national context. The objective of our research was therefore to analyse the diversity and assess the resilience of livelihood strategies of farming households oriented to livestock using a set of data collected in the dry land areas (oases and mountainous zones) of Morocco and discuss the livelihood outcome indicators. To achieve this, we have realized a cross-sectional analysis of livelihoods and adaptive capacity, to select a set of pertinent indicators. These indicators have been developed using an adapted version of the Rural Household Multi-Indicator Survey (RHoMIS) toolkit for pastoral and agropastoral household systems. Our results highlight the critical importance of livelihood diversification (off-farm diversification, livestock diversification, and crop diversification) in building household resilience and the livelihood outcomes. While livelihood strategies undoubtedly contribute to livelihood outcomes, there is also a critical iterative process, i.e., livelihood outcomes also influence the livelihood strategies at the farming households. The present work proposes an aggregated indicator of livelihood outcomes allowing us to capture the heterogeneity of living conditions of agropastoral systems by considering the main drivers of this system, i.e., mobility, livestock species, and physiological stage composition of the herd. This approach could constitute a valuable contribution to help fill the knowledge gaps that do not allow policy makers in developing contextualized rural development policies and instruments in these very harsh environments.

Flexible, manageable, and more efficient energy storage solutions have increased the demand for electric vehicles. A powerful battery pack would power the driving motor of electric vehicles. The battery power density, longevity, adaptable electrochemical behavior, and temperature tolerance must be understood. Battery management systems are essential in electric vehicles and renewable energy storage systems. This article addresses concerns, difficulties, and solutions related to batteries. The battery management system covers voltage and current monitoring; charge and discharge estimation, protection, and equalization; thermal management; and battery data actuation and storage. Furthermore, this study characterized the various cell balancing circuit types, their components, current and voltage stresses, control reliability, power loss, efficiency, size and cost, and their benefits and drawbacks. Secondly, we review concerns and challenges in battery management systems. Furthermore, we identify problems and obstacles that need additional attention for optimal and sustainable battery management systems for electric vehicles and renewable energy storage systems. Our last topic will be on issues for further research.

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.

The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breeding programs. Biometric identification technologies, such as thumbprint recognition, facial feature recognition, and retina pattern recognition, have been traditionally used for human identification but are now being explored for animal identification as well. Muzzle patterns, which are unique to each animal, have shown promising results as a primary biometric feature for identification in recent studies. Muzzle pattern image scanning is a widely used method in biometric identification, but there is a need to improve the efficiency of real-time image capture and identification. This study presents a novel identification approach using a state-of-the-art object detector, Yolo (v7), to automate the identification process. The proposed system consists of three stages: detection of the animal’s face and muzzle, extraction of muzzle pattern features using the SIFT algorithm and identification of the animal using the FLANN algorithm if the extracted features match those previously registered in the system. The Yolo (v7) object detector has mean average precision of 99.5% and 99.7% for face and muzzle point detection, respectively. The proposed system demonstrates the capability to accurately recognize animals using the FLANN algorithm and has the potential to be used for a range of applications, including animal security and health concerns, as well as livestock insurance. In conclusion, this study presents a promising approach for the real-time identification of livestock animals using muzzle patterns via a combination of automated detection and feature extraction algorithms.

The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry.Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.

The effects of aqueous leaves extract of three types of windbreak trees, Acacia, Casuarina, and Eucalyptus, were tested at 0, 25, 50, 75, and 100 g.L-1 for their effect on squash (Cucurbita pepo L.) seed germination and seedling growth. A group of squash seeds was sown in pots and irrigated with aqueous extract concentrations until the germination stage, while another group of seeds was maintained for three weeks under the same treatments until the seedling stage. Treating seeds with aqueous extract of Acacia resulted in a significant decrease in the percentage of germination (GP) and the germination rate (GR) of the seeds. The aqueous extract of Eucalyptus also led to a slight decrease in the GR, while no effect was observed when the aqueous extract of Casuarina was used. Treatment of seedling, on the other hand, with an aqueous extract of all windbreak types decreased all of the seedling growth parameters, fresh and dry weight (FW and DW), water content (WC), length of petioles (LP), number of buds (NB) and number of leaves (NL). The three windbreak trees induced an allelopathic effect on squash seedling growth in particular, more than the squash seed germination, but at different degrees. Acacia extract resulted in the highest effect, while Eucalyptus and Casurina resulted in intermediate and low inhibitory effects respectively. In all three windbreaks, the inhibitory effect of the extract on the seed germination and seedling growth increased as the extract concentration increased from 0 to 100 g.L-1.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses.

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

Cytoplasmic male sterility (CMS) provides an irreplaceable strategy for commercial exploitation of heterosis and producing high-yielding hybrid rice. The exogenous application of plant growth regulators could improve outcrossing rates of the CMS lines by affecting floral traits and accordingly increase hybrid rice seed production. The present study aimed at exploring the impact of growth regulators such as gibberellic acid (GA3), indole-3-acetic acid (IAA), and naphthalene acetic acid (NAA) on promoting floral traits and outcrossing rates in diverse rice CMS lines and improving hybrid rice seed production. The impact of foliar applications of growth regulators comprising GA3 at 300 g/ha or GA3 at 150 g/ha + IAA at 50 g/ha + NAA at 200 g/ha versus untreated control was investigated on floral, growth, and yield traits of five diverse CMS lines. The exogenously sprayed growth regulators, in particular, the combination of GA3, IAA, and NAA (T3) boosted all studied floral, growth, and yield traits in all tested CMS lines. Moreover, the evaluated CMS lines exhibited significant differences in all measured floral traits. L2, L3, and L1 displayed the uppermost spikelet opening angle, duration of spikelet opening, total stigma length, style length, stigma brush, and stigma width. In addition, these CMS lines exhibited the highest plant growth and yield traits, particularly under T3. Consequently, exogenous application of GA3, IAA, and NAA could be exploited to improve the floral, growth, and yield traits of promising CMS lines such as L2, L3, and L1, hence increasing outcrossing rates and hybrid rice seed production.

Younger granite bodies form two arches, the western and the eastern (WA, EA), which extend from the south northwards from the Meatique, ophiolitic group-island arc rocks, to the large older granite outcrop to the north. This paper concerns the feasibility of exploration in the El-Erediya-Ria El-Garah-El-Gidami-El-Missikat Y Gr regions. Fieldwork and remote sensing, together with geochemical, petrochemical, and mineralogical studies, are used to show the controlling factors, routes, and the origins of the deposits. Remote sensing is used to delineate the different rock units. Normal and strike–slip NW, NNE faults, veins, fractured ENE shear zones, and alteration zones of magmatic-hydrothermal fluids are discussed. Granites are considered using petrochemical diagrams as resources. These rocks are categorized as syeno- and alkali feldspar granites. Geochemical binary relationships recognized the granites are highly fractionated calc-alkaline-altered Monzo-, syeno-, and alkali feldspar granites formed in the active continental margin. The observed positive Ga vs. Cu, Zn, and Ni correlations are used for epithermal-magmatic-hydrothermal polymetallic veins and mineralized greisen zones. Negative Cu vs. Mo correlation patterns show probable Mo-porphyry deposits in the deeper zones at the contact point between porphyritic perthite and perthite granitic El-Erediya mass. The Zr/Sr between 1.65 to 2.93 plus fluorites in El-Missikat and up to 5.48 plus fluorites in El-Erediya show both U-poor at El-Missikat and U-rich deposits at El-Erediya. The recorded U, Th, Cu, and Pb vertical zoning sequence of deposition differentiates U aureole and deposit zones. The estimated lateral zoning sequences of deposition of these elements define the centers of U deposits. Pathfinders for the deposit of the examined area include the positive Fe2O3 vs. Mg O and Fe2O3 vs. Ca O correlations, and also negative Rb/Sr vs. K/Na and Rb vs. Sr ones, can be applied to future prospecting for similar U-F-Au-W-Mo deposits in the Eastern Desert of Egypt.

Powder packing in metal powders is an important aspect of additive manufacturing (otherwise known as 3-D printing), as it directly impacts the physical and mechanical properties of materials. Improving the packing density of powder directly impacts the microstructure of the finished 3D-printed part and ultimately enhances the surface finish. To obtain the most efficient packing of a given powder, different powder blends of that material must be mixed to minimize the number of voids, irrespective of the irregularities in the particle morphology and flowability, thereby increasing the density of the powder. To achieve this, a methodology for mixing powder must be developed, for each powder type, to obtain the maximum packing density. This paper presents a model that adequately predicts the volumetric fraction of the powder grades necessary for obtaining the maximum packing density for a given powder sample. The model factors in the disparity between theoretical assumptions and the experimental outcome by introducing a volume reduction factor. We outline the model development steps in this paper, testing it with a real-world powder system.

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency.