The current era of computing is witnessing a huge amount of data being generated with every passing moment. This massive data if nourished effectively can open new horizons for the computing world. The modern world is slowly but surely moving towards the automation age where every entity and object is being automated to perform desired tasks without the need of human interventions. This has made the lives of people more convenient and comfortable. Automation has taken over every single field of computing and even beyond. Smart mobility is one such example of automation wherein the users get real time information about the traffic conditions as well as alternate route suggestions in case of traffic jams. Transportation is considered as the backbone of every business. The automated intelligent transportation system (ITS) has completely transformed the way how people, goods and services are transported and is quite important for achieving sustainability. This paper provides an overview of the existing ITS system, concept of smart mobility and existing vulnerabilities in these systems. Their security concerns and scenarios are also analyzed. Furthermore, in this paper the importance and need for securing these intelligent systems is highlighted and future trends in ITS is also suggested. Although ITS and smart mobility technology are already providing convenient transportation and navigational facilities, there is still a huge scope to improve these facilities for the end users. The suggested future trends if integrated in an effective manner can provide exemplary means to provide state-of-the-art navigational facilities and smart mobility in a true sense.

Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant women using advanced machine learning techniques. The study employs a systematic methodology including data collection, preprocessing, feature selection, and model implementation. Data collection was conducted at Majidia Hospital, involving a diverse sample of 70,000 pregnant women recruited through antenatal clinics, online health platforms, community outreach programs, and telephone surveys using structured questionnaires. Participants were selected across all pregnancy trimesters to ensure a representative demographic, capturing variations in age, educational background, occupational status, and parity. A diverse set of machine learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron (MLP), were evaluated alongside ensemble methods to achieve robust and reliable predictions. The experimental results demonstrate that the Random Forest model consistently outperforms other classifiers with an accuracy of 97.82% ± 0.03%, precision of 97.82% ± 0.03%, recall of 100.00% ± 0.00%, and an F1 score of 96.81% ± 0.02%. SVM and Decision Tree classifiers also showed strong performance, with accuracy scores of 93.79% ± 0.01% and 91.82% ± 0.03%, respectively. Furthermore, ensemble methods enhanced predictive performance, highlighting their ability to balance accuracy, precision, recall, and F1 score. In regression tasks, the Random Forest Regressor achieved near-perfect predictions with a Mean Squared Error (MSE) of 4.5767 × 10⁻⁸ and an R² score of 1.000, underscoring its superior predictive capabilities. Additionally, a custom loss function integrating Cross-Entropy Loss and an F1 Score Penalty was introduced to address class imbalance and enhance model performance. The training process, conducted over 10 epochs, demonstrated consistent loss reduction, with the lowest recorded loss at epoch 8 (2.4382), reflecting effective learning and parameter tuning. This study envisions the development of an intelligent, web-based tool aimed at revolutionizing psychological health assessment and support for pregnant women. This tool will not only provide early diagnosis and intervention but also recommend personalized yoga practices and natural remedies to improve maternal mental health and overall wellbeing. These findings highlight the potential of AI-driven innovations in enhancing maternal care through holistic and accessible technological solutions.

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.

The paper comprehends an impending accost of intensifying biometric stationed authentication protocol(BSAP) bestowing meta-heuristic genetic algorithm for securing MANET. Biometric authentication using fingerprint, facial, iris scan, voice recognition etc. have gain a lot of importance in recent years to provide security in MANET. Biometrics are more advantageous and secure as compared to prevailing data security techniques like password or token mechanisms. A higher level of security is achieved in our impending approach using genetic algorithm to overcome the security and privacy concerns that exist in biometric technology. The foremost requirement of our protocol is to overcome various data attacks such as wormhole, cache poisoning, invisible node attack etc. that are confronted by MANET and make the network more secure.

This paper includes a prospective approach of developing a trust based QOS protocol (TBQP) using meta-heuristic genetic algorithm for optimizing and securing MANET. Genetic algorithm will help in maintaining Quality of Service(QOS) by selecting a fittest i.e. shortest route hence providing better performance. Intelligent optimization approaches or meta heuristic algorithms like, genetic algorithm(GA), neural networks(NN) based on artificial intelligence(AI), particle swarm optimization(PSO) technique and simulated annealing(SA) in the recent years have well consigned QOS issues. Ad-hoc networks face the primary defiance of restraining attacks against data, like, unauthorized data modification impersonation etc caused by malicious nodes in the network. This problem is tackled by trust application in our proposed approach which helps in securing ad-hoc networks.