Jawaharlal Nehru Technological University, Kakinada (JNTU Kakinada) is a public university, in Kakinada, East Godavari district, North of the Indian state of Andhra Pradesh. It is one of India's universities focusing on engineering. The university has been accredited by the National Assessment and Accreditation Council (NAAC) of University Grants Commission (UGC) with an "A" grade. JNTU Kakinada is known for its EAMCET entrance test where over 250,000 students take the test every year for around 600 seats in its undergraduate engineering courses. All the selected top candidates for engineering programs enroll at the University College of Engineering (Autonomous) Kakinada.
Android operating system has taken over the mobile ecosystem across the world and thus is a prime target of more advanced malware that uses its ability to obfuscate, dynamically load code, encrypted communications and even environment aware evasion behaviors. These properties severely circumscribe the ability of conventional signature-based detection systems, and are problematic to either all-static or all-dynamic analysis. Although machine learning-based detection methods have promise, most current solutions are black-box systems with no forensic transparency [5][6] , and are not as applicable in security operations and digital investigations. This paper presents an android malware detection system being a hybrid between the static-dynamic framework and powered by Indicators of Compromise (IoCs) and machine learning. The framework uses a constrained dual-VM setup consisting of an analysis host based on Ubuntu and an instrumented android emulator. Standard analysis tools are used to extract the static IoCs, which consist of permissions, manifest components, use of sensitive APIs, cryptographic artifacts, certificate metadata, and embedded network indicators. Controlled application execution can be used to obtain dynamic IoCs by monitoring and instrumenting runtime behaviors related to behavioural indicators like network communication, filesystem activity and sensitive API invocation. A framework IoC correlation and normalization engine is used to combine heterogeneous non-uniform indicators which are static and dynamic, into structured feature vectors. A random Forest model that has been chosen due to its strong ability to predict high-dimensional data, as well as the ability to have an interpretable component, are used to classify these vectors. In addition to binary classification, the framework produces a structured forensic intelligence report, which records the IoCs behind each detection decision, which in turn facilitates evidence-based and explainable malware detection. The suggested framework puts the emphasis on the detectability, interpretability and forensic relevance. It improves resilience to evasion by obfuscation, but does not increase decision-making transparency through the correlation of fixating and moving IoCs. The work provides an expandable and explainable android malware detection architecture that can be used in academic research, enterprise security operations, and advanced digital forensic investigation, and which may be extended in the future to the scale of validation and on-device detection.
In wireless ad hoc networks, where conventional infrastructure is lacking, effective routing algorithms, security mechanisms, and energy efficiency are critical challenges. While existing cluster-based routing protocols demonstrate efficiency, the absence of a comprehensive approach for selecting cluster heads based on multiple parameters remains a gap. Addressing this multi-objective challenge is pivotal in optimizing cluster-based routing. Ant Colony Optimization (ACO) emerges as a key algorithm for path selection, while the integration of Log Likelihood Ratio (LLR) enhances route longevity and reliability. The proposed system introduces cluster-based region formation coupled with the selection of trusted cluster heads, enriching routing paths. It prioritizes paths with heavy weights and fewer links, optimizing efficiency. Implementation of the proposed system yields a notable increase in delivery ratio to 96% marking a significant advancement in wireless ad hoc network performance.
Pneumonia is a leading cause of mortality among children, particularly in low-income regions where limited healthcare access and overlapping symptoms with other respiratory diseases make diagnosis difficult. Traditional methods are further challenged by poor image quality, a shortage of specialists, and the complexity of interpreting pediatric chest X-rays. To tackle these issues, this study introduces a Adaptive Multi-Convolutional and Cross-Attention-based ShuffleNet (AMC-CASNet) for pneumonia detection. The model employs multiple adaptive convolutional layers to capture discriminative features from chest X-rays, while a cross-attention mechanism highlights the most informative regions, thereby improving early disease recognition. To enhance its performance, the model’s parameters are optimized using the Re-derived Arbitrary Parameter Addax Optimization (RAPAO) algorithm, which reduces overfitting and improves generalization. By combining AMC-CASNet with RAPAO, the proposed system achieves robust and accurate classification performance and outperforms several existing deep learning and machine learning approaches for pediatric pneumonia diagnosis.
This work presents a new Optimal Reactive Power Dispatch (ORPD) scheme using single-objective and multi-objective optimization to achieve higher system performance under diverse operating conditions. Two different Flexible AC Transmission Systems (FACTS) devices, in particular the Thyristor-Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC), have been strategically implemented on the IEEE 30-bus and IEEE 118-bus test systems, enabling control of reactive power flows, enhancement of voltage stability, and reduction of transmission losses. The Kepler Optimization Algorithm (KOA) is chosen as the main optimization algorithm by virtue of its excellent global exploration ability, as well as strong convergence behavior for both single-objective and multi-objective formulations, as they are used for loss reduction, voltage deviation minimization, and stability margin enhancement. The performance of the proposed methods is also demonstrated with comparisons to existing metaheuristic methods like Bat Algorithm (BA), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), Tabu Search (TS), Particle Swarm Optimization (PSO), Modified Ant Lion Optimizer (MALO), and Hybridized Tabu Search-Simulated Annealing (HTSSA). Through the simulation, the KOA proves to be consistently better compared to benchmark methods for single-objective and multi-objective ORPD scenarios, leading to significant reductions in real power losses, improvement of voltage profiles, and the provision of optimal trade-off solutions. The TCSC and UPFC are also incorporated to enhance the controllability and resilience of the system. This KOA-based ORPD approach is an adequate and scalable solution for modern power systems, needing advanced reactive power handling and multi-criteria operational optimization.
Pinching-Antenna Systems (PASS) have emerged as a promising low-power alternative to traditional high-power 6G Multiple-Input Multiple-Output (MIMO) architectures by facilitating beamforming through selective antenna activation along dielectric waveguides instead of power-hungry phase shifters. Although PASS can potentially reduce the hardware power consumption of the antenna by over 95
