Reliable renewable energy forecasting for climate change mitigation.

Accurate prediction of electricity generation from diverse renewable energy sources (RES) plays a pivotal role in optimizing power schedules within RES, contributing to the collective effort to combat climate change. While prior research often focused on individual energy sources in isolation, neglecting intricate interactions among multiple sources, this limitation frequently leads to inaccurate estimations of total power generation. In this study, we introduce a hybrid architecture designed to address these challenges, incorporating advanced artificial intelligence (AI) techniques. The hybrid model seamlessly integrates a gated recurrent unit (GRU) and a ResNext model, and it is tuned with the modified jaya algorithm (MJA) to capture localized correlations among different energy sources. Leveraging its nonlinear time-series properties, the model integrates meteorological conditions and specific energy source data. Additionally, principal component analysis (PCA) is employed to extract linear time-series data characteristics for each energy source. Application of the proposed AI-infused approach to a renewable energy system demonstrates its effectiveness and feasibility in the context of climate change mitigation. Results reveal the superior accuracy of the hybrid framework compared to more complex models such as decision trees and ResNet. Specifically, our proposed method achieved remarkable performance, boasting the lowest error rates with a normalized RMSE of 6.51 and a normalized MAPE of 4.34 for solar photovoltaic (PV), highlighting its exceptional precision in terms of mean absolute errors. A detailed sensitivity analysis is carried out to evaluate the influence of every element in the hybrid framework, emphasizing the importance of energy correlation patterns. Comparative assessments underscore the increased accuracy and stability of the suggested AI-infused framework when compared to other methods.

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO's efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques.

This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62 ± 0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.

Deep learning hybridization for improved malware detection in smart Internet of Things.

The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet's exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen's Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

Nature-inspired solutions for energy sustainability using novel optimization methods.

This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.

L-CPPA: Lattice-based conditional privacy-preserving authentication scheme for fog computing with 5G-enabled vehicular system.

The role that vehicular fog computing based on the Fifth Generation (5G) can play in improving traffic management and motorist safety is growing quickly. The use of wireless technology within a vehicle raises issues of confidentiality and safety. Such concerns are optimal targets for conditional privacy-preserving authentication (CPPA) methods. However, current CPPA-based systems face a challenge when subjected to attacks from quantum computers. Because of the need for security and anti-piracy features in fog computing when using a 5G-enabled vehicle system, the L-CPPA scheme is proposed in this article. Using a fog server, secret keys are generated and transmitted to each registered car via a 5G-Base Station (5G-BS) in the proposed L-CPPA system. In the proposed L-CPPA method, the trusted authority, rather than the vehicle's Onboard Unit (OBU), stores the vehicle's master secret data to each fog server. Finally, the computation cost of the suggested L-CPPA system regards message signing, single verification and batch verification is 694.161 ms, 60.118 ms, and 1348.218 ms, respectively. Meanwhile, the communication cost is 7757 bytes.

Securing Secrets in Cyber-Physical Systems: A Cutting-Edge Privacy Approach with Consortium Blockchain.

In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept (PoC) design that leverages consortium blockchain technology to address privacy challenges in cyber-physical systems (CPSs). The proposed design introduces a novel approach to safeguarding sensitive information and ensuring data integrity while maintaining a high level of trust among stakeholders. By harnessing the power of consortium blockchain, the design establishes a decentralized and tamper-resistant framework for privacy preservation. However, ensuring the security and privacy of sensitive information within CPSs poses significant challenges. This paper proposes a cutting-edge privacy approach that leverages consortium blockchain technology to secure secrets in CPSs. Consortium blockchain, with its permissioned nature, provides a trusted framework for governing the network and validating transactions. By employing consortium blockchain, secrets in CPSs can be securely stored, shared, and accessed by authorized entities only, mitigating the risks of unauthorized access and data breaches. The proposed approach offers enhanced security, privacy preservation, increased trust and accountability, as well as interoperability and scalability. This paper aims to address the limitations of traditional security mechanisms in CPSs and harness the potential of consortium blockchain to revolutionize the management of secrets, contributing to the advancement of CPS security and privacy. The effectiveness of the design is demonstrated through extensive simulations and performance evaluations. The results indicate that the proposed approach offers significant advancements in privacy protection, paving the way for secure and trustworthy cyber-physical systems in various domains.

HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications.

The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data's privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users' privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.

ECA-VFog: An efficient certificateless authentication scheme for 5G-assisted vehicular fog computing.

Fifth-generation (5G)-enabled vehicular fog computing technologies have always been at the forefront of innovation because they support smart transport like the sharing of traffic data and cooperative processing in the urban fabric. Nevertheless, the most important factors limiting progress are concerns over message protection and safety. To cope with these challenges, several scholars have proposed certificateless authentication schemes with pseudonyms and traceability. These schemes avoid complicated management of certificate and escrow of key in the public key infrastructure-based approaches in the identity-based approaches, respectively. Nevertheless, problems such as high communication costs, security holes, and computational complexity still exist. Therefore, this paper proposes an efficient certificateless authentication called the ECA-VFog scheme for fog computing with 5G-assisted vehicular systems. The proposed ECA-VFog scheme applied efficient operations based on elliptic curve cryptography that is supported by a fog server through a 5G-base station. This work conducts a safety analysis of the security designs to analysis the viability and value of the proposed ECA-VFog scheme. In the performance ovulation section, the computation costs for signing and verification process are 2.3539 ms and 1.5752 ms, respectively. While, the communication costs and energy consumption overhead of the ECA-VFog are 124 bytes and 25.610432 mJ, respectively. Moreover, comparing the ECA-VFog scheme to other existing schemes, the performance estimation reveals that it is more cost-effective with regard to computation cost, communication cost, and energy consumption.

LR-AKAP: A Lightweight and Robust Security Protocol for Smart Home Environments.

For the betterment of human life, smart Internet of Things (IoT)-based systems are needed for the new era. IoT is evolving swiftly for its applications in the smart environment, including smart airports, smart buildings, smart manufacturing, smart homes, etc. A smart home environment includes resource-constrained devices that are interlinked, monitored, controlled, and analyzed with the help of the Internet. In a distributed smart environment, devices with low and high computational power work together and require authenticity. Therefore, a computationally efficient and secure protocol is needed. The authentication protocol is employed to ensure that authorized smart devices communicate with the smart environment and are accessible by authorized personnel only. We have designed a novel, lightweight secure protocol for a smart home environment. The introduced novel protocol can withstand well-known attacks and is effective with respect to computation and communication complexities. Comparative, formal, and informal analyses were conducted to draw the comparison between the introduced protocol and previous state-of-the-art protocols.

A Web-Based Model to Predict a Neurological Disorder Using ANN.

Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person's ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person's ability to control emotions or to be social can result in demotivation which can severely affect the brain's ability to perform optimally. One of the main causes of reliance and disability among older people worldwide is dementia. Often it is misunderstood which results in people not accepting it causing a delay in treatment. In this research, the data imputation process, and an artificial neural network (ANN), will be established to predict the impact of dementia. based on the considered dataset. The scaled conjugate gradient algorithm (SCG) is employed as a training algorithm. Cross-entropy error rates are so minimal, showing an accuracy of 95%, 85.7% and 89.3% for training, validation, and test. The area under receiver operating characteristic (ROC) curve (AUC) is generated for all phases. A Web-based interface is built to get the values and make predictions.

A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem.

Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined constraints. The investigations into the performance of population-based algorithms that utilized to tackle the PAS problem considered in this paper reveal their weaknesses in obtaining quality solutions that create a space to investigate the performance of another population-based method. Thus, in this paper, an Artificial Bee Colony Algorithm (ABC) is proposed to tackle the formulation of the PAS problem under consideration. It is a class of swarm intelligence metaheuristic algorithms based on the intelligent foraging behaviour of honey bees developed to solve continuous and complex optimization problems. Due to the discretization of the PAS, the continuous nature of the ABC algorithm is changed to cope with the rugged solution space of the PAS. The initial feasible solution to the PAS problem is obtained using the room-oriented approach. Then the ABC algorithm optimizes the feasible solutions with the aid of three neighbourhood structures embedded within the employed bee and the onlooker bee operators of the algorithm. The performance of the proposed ABC algorithm based on three different parameters, the solution number (SN), limit value (LV), and the maximum cycle number (MCN) is evaluated on six standard benchmark datasets of the PAS. Two of these main parameters (i.e. SN and LV) are fine-tuned to obtain the best solutions on instances like Test-data 1 = 679.80, Test-data 2 = 1180.40, Test-data 3 = 787.40, Test-data 4 = 1198.60, Test-data 5 = 636.80, and Test-data 6 = 818.60. The best solutions obtained by the proposed method are evaluated against the results of the 19 comparative algorithms comprising five population-based methods, eleven heuristic, and hyperheuristic-based methods, and three integer programming-based methods. The proposed method shows its supremacy in the performance by achieving the best results in all the instances of the dataset when compared with five population-based methods (DFPA, HSA, MBBO-GBS, BBO-GBS, and BBO-RBS) and producing the best results in five instances when compared with eleven heuristic and hyperheuristic-based methods (LAHC, DHS-GD, HTS, DHS-SA, ADAPTIVE GD, GD, HH-GD, DHS-IO, HH-SA, HH-IE, TA) and Finally, it had a competitive performance with the other three Integer programming methods (MIP warm start, MIP-Heuristic, CG) that worked on the same formulations of the PAS. In a nutshell, the proposed ABC algorithm could be adopted as a new template algorithm for the PAS community.

An empirical study of factors influencing e-health services adoption among public in Saudi Arabia.

Until now most of the studies concerning eHealth in Saudi Arabia are about the exploration of the national benefits from eHealth and the technical and infrastructural challenges in implementing eHealth rather than finding the factors influencing the users in adopting eHealth services. Further, the eHealth adoption rate in Saudi Arabia is low despite the scope of potential growth for the eHealth market. In this study, the authors added Trust, Privacy and System Quality factors to Technology Acceptance Model by considering the research context to examine the factors influencing eHealth services adoption. The proposed model was empirically tested based on the data collected, through survey questionnaire from 314 responses in Saudi Arabia. Structural Equation Modelling was followed to analysis and validates the proposed model based on partial least squares method using SmartPLS. Based on the findings, Perceived Usefulness in addition to Privacy significantly affect eHealth adoption. Moreover, Perceived Ease of Use factor has indirect effect on people perception toward eHealth services. Furthermore, the results show that System Quality and Trust are not influencing factors. This study is important for the healthcare policymakers for getting direction for future studies to avail the maximum benefits of eHealth in Saudi Arabia.

Survival prediction among heart patients using machine learning techniques.

Cardiovascular diseases are regarded as the most common reason for worldwide deaths. As per World Health Organization, nearly 17.9 million people die of heart-related diseases each year. The high shares of cardiovascular-related diseases in total worldwide deaths motivated researchers to focus on ways to reduce the numbers. In this regard, several works focused on the development of machine learning techniques/algorithms for early detection, diagnosis, and subsequent treatment of cardiovascular-related diseases. These works focused on a variety of issues such as finding important features to effectively predict the occurrence of heart-related diseases to calculate the survival probability. This research contributes to the body of literature by selecting a standard well defined, and well-curated dataset as well as a set of standard benchmark algorithms to independently verify their performance based on a set of different performance evaluation metrics. From our experimental evaluation, it was observed that decision tree is the best performing algorithm in comparison to logistic regression, support vector machines, and artificial neural networks. Decision trees achieved 14% better accuracy than the average performance of the remaining techniques. In contrast to other studies, this research observed that artificial neural networks are not as competitive as the decision tree or support vector machine.

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion.

Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

Using algorithmic trading to analyze short term profitability of Bitcoin.

Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.