Mobile Ad hoc NETworks (MANET), unlike typical wireless networks, may be used spontaneously without the need for centralized management or network environment. Mobile nodes act as mediators to help multi-hop communications in such networks, and most instances, they are responsible for all connectivity tasks. MANET is a challenging endeavor because these systems can be attacked, which can harm the network. As a result, security concerns become a primary factor for these types of networks. This article aims to present an efficient two-factor smart card-based passcode authentication technique for securing legitimate users on an unprotected network. This scheme enables the password resetting feature. A secured mechanism for sharing keys is offered by using the hash function. We present a new two-factor mutual authentication technique based on an entirely new mechanism called the virtual smart card. Compared to authentication, the proposed method has fewer computation processes but is more time efficient since it is based on a hash function. Additionally, this approach is resistant to most attacker behaviors, such as Mutual authentication, Gateway node bypassing attacks, DoS attacks, replay attacks, Man in the middle attacks, and stolen smart device attacks. Experimental results validate the efficiency of this scheme, and its security is also analyzed.

The digital transformation of the healthcare sector has revolutionized operational efficiency and patient care, yet concurrently exposed healthcare organizations to unprecedented cybersecurity risks, jeopardizing patient confidentiality and organizational integrity. This study undertakes a comprehensive investigation into contemporary cybersecurity strategies and emerging trends within the healthcare industry. Through a meticulous examination of published literature from reputable databases, including PubMed/MEDLINE, CINAHL, and Web of Science, critical patterns and vulnerabilities are discerned, underlining the escalating frequency and severity of cyber threats such as ransomware and phishing attacks. Emphasizing the pivotal role of organizational cyber resilience governance and policies, the study identifies a notable gap in standardized cybersecurity risk assessment methodologies, signaling the urgent need for innovative approaches. In response to identified challenges, the research proposes the development of novel methodologies to fortify cybersecurity defenses and protect patient data. Leveraging cutting-edge technologies such as blockchain and artificial intelligence, the study advocates for proactive measures to mitigate emerging threats and ensure data security and patient privacy in healthcare environments. Moreover, the integration of end-to-end security measures and the adoption of DevOps methodologies are highlighted as promising avenues for enhancing cybersecurity resilience. Results from a systematic literature review underscore the imperative for ongoing research and collaboration to address cybersecurity challenges in healthcare effectively. By offering insights into key cybersecurity features, technologies, and responsibilities within the healthcare sector, this study aims to inform stakeholders and policymakers, facilitating the implementation of robust cybersecurity measures. Furthermore, the study presents key findings regarding the current state of cybersecurity in healthcare, including challenges faced and potential solutions identified through the research process. Ultimately, through concerted efforts and the utilization of innovative strategies, healthcare organizations can navigate the evolving cybersecurity landscape, safeguarding patient information and upholding the integrity of healthcare systems.

This paper investigates the application of EfficientNetV2, an advanced variant of EfficientNet, in diabetic retinopathy (DR) detection, a critical area in medical image analysis. Despite the extensive use of deep learning models in this domain, EfficientNetV2’s potential remains largely unexplored. The study conducts comprehensive experiments, comparing EfficientNetV2 with established models like AlexNet, GoogleNet, and various ResNet architectures. A dataset of 3662 images was used to train the models. Results indicate that EfficientNetV2 achieves competitive performance, particularly excelling in sensitivity, a crucial metric in medical image classification. With a high area under the curve (AUC) value of 98.16%, EfficientNetV2 demonstrates robust discriminatory ability. These findings underscore its potential as an effective tool for DR diagnosis, suggesting broader applicability in medical image analysis. Moreover, EfficientNetV2 contains more layers than AlexNet, GoogleNet, and ResNet architecture, which makes EfficientNetV2 the superior deep learning model for DR detection. Future research could focus on optimizing the model for specific clinical contexts and validating its real-world effectiveness through large-scale clinical trials.

Polycystic Kidney disease (PKD) is often caused due to inherited condition and it forms many cysts around the kidney, and it is damaged when it grow. Accurate segmentation of PKD is very crucial for a persistent MRI diagnostics. Because many people have no symptoms, they can lead to complications until the surgery is done to remove the cyst. Methods: For accurate detection PKD, the heap of MRI images have been considered, In this work, A novel method includes feature based Fuzzy C means (FFCM) with whale optimization algorithm (WOA) for accurate segmentation of kidney cyst. WOA is used to optimally attach the cluster centroids of FCM. In the conventional methods like mountain models and fuzzy C-shells models are used to identify the regions of interest (ROI). Result: The outcomes of FFCM and WOA based process are compared with the results from existing methods using IB-FCM and Fuzzy K-means and FCM model. Conclusion: However, an exact boundary of the region is obtained and  computed an experimental dispersal of the image by Feature extraction based Fuzzy C-Means Clustering segmentation. A detection process is based on the FFCM and WOA segmentation is accomplished to discriminate the normal cyst and the kidney disease. The experimental evaluation is accomplished through the use of Ischemic kidney Disease (IKD) database.

Forecasting electrical energy consumption is becoming increasingly important for a country's citizens as it addresses rising energy demand and energy waste issues. A useful electrical energy consumption prediction scheme could help users estimate their monthly electricity bills and the use of new electrical appliances in their homes. Traditional energy consumption prediction methods are time-consuming and necessitate expert assistance to analyze and calculate energy use over time. The limitations of the existing works are that the existing literature does not accurately predict monthly energy consumption and costs using machine learning. They concentrated on electrical energy consumption over a short period of time in a single building, using seasonal data rather than automating the system for repeated use. To address these issues, this paper proposes machine learning-based automation systems that predict monthly energy consumption, estimate costs, and identify relevant features using data from electrical home appliances in Bangladesh. Several regression models, including Random Forest, Decision Tree, XGBoost, Boosting, and LightGBM Regressor, are tested to find the best prediction model. We have performed dataset collection, dataset cleaning, feature extraction, scaling, normalization, hyper parameter tuning, training, testing, and model selection activities. The simulation results clearly indicated that the Random Forest regressor model performed better than the other models, with higher R squared values and lower error values. The comparison results revealed that the proposed random forest regression model outperforms previous works by at least 4% in accuracy and 7% in mean absolute error. The proposed mobile application helps users make informed decisions by calculating energy consumption for new home appliances, making recommendations, delivering updated news from the power board, and providing required guidelines. The mobile application feature evaluation results revealed that our proposed application received an excellent rating from more than 70% of customers.

The most popular way for people to share information is through social media. Several studies have been conducted using ML approaches like LSTM, SVM, BERT, GA, hybrid LSTM-SVM and Multi-View Attention Networks to recognize bogus news MVAN. Most traditional systems identify false news or true news exclusively, but discovering kind of false information and prioritizing false information is more difficult, and traditional algorithms offer poor textual classification accuracy. As a result, this study focuses on predicting COVID-19-related false information on Twitter along with prioritizing types of false information. The proposed lightweight recommendation-system consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase is performed to remove the unwanted data. After preprocessing, the BERT model is used to convert the word into binary vectors. Then these binary features are taken as the input of the classification phase. In this classification phase, a 4CL time distributed layer is introduced for effective feature selection to remove the detection burdens, and the Bi-GRU model is used in the classification phase. Proposed-method is implemented in Mat lab software and is carried out several performance-metrics, and there are three different datasets used for validating its performance. Proposed model's total accuracy is 97%, specificity is 98%, precision is 95%, and the error value is 0.02, demonstrating its effectiveness over current methods. The proposed social media research system can accurately predict false information, and recognized news may be offered to the user such that they can learn the truth about news on social media.

In recent Artificial Intelligence developments, large datasets as knowledge are a prime requirement for analysis and prediction. To manage the knowledge of the network, the Data Center Network (DCN) has been considered a global data storage facility on edge servers and cloud servers. In recent research trends, knowledge-defined networking (KDN) architecture is considered, where the management plane works as the knowledge plane. The major network management task in the DCN is to control traffic congestion. To improve network management, i.e., optimized resource management, enhanced Quality of Service (QoS), we propose a path prediction technique by combining the convolution layer with the RNN deep learning model, i.e., Convolution-Long short-term memory network as Convolution-LSTM and the bi-directional long short-term memory (BiLSTM) network as Convolution-BiLSTM. The experimental results demonstrate that, in terms of many metrics, i.e., network latency, packet loss ratio, network throughput, and overhead, our proposed methodologies perform better than the existing works, i.e., OSPF, FlowDCN, modified discrete PSO, ANN, CNN, and LSTM-based routing approaches. The proposed approach improves the network throughput by approximately 30% and 12% as compared to existing CNN and LSTM-based routing approaches, respectively.

This study analyzes wastewater treatment processes at a mining company in the Almaty region, Kazakhstan. Four treatment schemes were developed and assessed, with a focus on optimizing efficiency. The discharged water quality from different technological lines was evaluated using integral functions for a quantitative comparison of each scheme's performance. Additionally, an expert system was developed to validate the results and support future research in wastewater treatment.

This research focuses on lifestyle and work-life balance, examining how different dynamics can influence holistic wellbeing, which focuses on the current literature to develop a framework that extends beyond standard well-being concepts. The work aims to enrich the discourse on holistic well-being by synthesizing existing knowledge and exploring new knowledge, providing valuable views for individuals and age groups involved in professionals striving to develop a more balanced and meaningful life. The study utilizes AI-driven methods and machine learning algorithms to analyse work-life indices to understand patterns and correlations that contribute or hinder work-life harmony. The findings highlight the importance of lifestyle choices, social connections, and personal fulfilment in achieving holistic wellness. The research provides evidence-based insights and practical recommendations to develop a healthy lifestyle and work-life balance. The study also examines the ethical implications of AI and highlights the need for a comprehensive approach to work-life balance. The study utilizes supervised learning algorithms and a comparative analysis of the accuracy scores of various algorithms, revealing significant differences in classification and more accuracy for the work-life balance score. The study aims to uncover insights beyond the typical contrasts and illuminate the interrelationship between numerous factors affecting an individual's well-being.

The technology-based lifestyle has led to a rise in people suffering from obesity, which in turn has led to the emergence of many chronic diseases such as elevated blood sugar and blood pressure, this give researchers a good reasons to develop Internet of Things networks, as the entry of technical innovations has led to Artificial intelligence in the medical field has revolutionized the provision of medical services and facilitated the lives of patients, from monitoring blood sugar levels to using remote surgery techniques, as it has saved a lot of effort and money for both the doctor and the patient at the same time, but these advantages open a wide scope for many problems as well. This survey studied the medical Internet of Things network in terms of presenting the definition, structure, types of devices used, their applications, and some of the communication protocols used in it. The attacks that the medical Internet of Things network may be exposed to were also classified based on the concerns they cause, and the solutions proposed by the researchers were presented. On the other hand, the previous works of the researchers were classified according to the types of devices used, communication protocols, and network security. In each of the mentioned parts, what the researchers have done and their contributions in this field were discussed, analyzed, and a review of the proposed future works in the used literature was presented.