The increasing incidence of malware presents significant obstacles to cybersecurity, necessitating sophisticated and effective techniques for detection and categorization. This paper presents a novel method that improves the precision and efficacy of malware classification by utilizing multi-processing and Bag-of-Words (BoW) vectorization. The suggested technique utilizes feature vectors to depict malware samples, leveraging the parallel processing capabilities of contemporary computer systems to expedite classification operations. This solution centers on a bespoke HexVectorizer that transforms hexadecimal strings obtained from malware binaries into feature vectors. The approach employs a balanced subset of the Microsoft Malware Classification dataset, meticulously preprocessed for dependable evaluation to assure thorough analysis. Python's multiprocessing module is utilized to meet the computing requirements of extensive datasets, facilitating the parallelization of vectorization processes and markedly enhancing processing performance. The classification system is centered on XGBoost's XGBClassifier, recognized for its superior performance and accuracy in addressing malware detection and classification issues. The experimental findings validate the efficacy of the suggested technique in real-time malware detection, confirming its relevance to diverse cybersecurity contexts. This paper offers an exhaustive elucidation of the implementation procedure, accompanied by thorough performance assessments. The results highlight the method's promise as a scalable and efficient approach for tackling the increasing issues of malware classification in cybersecurity. This research greatly advances the creation of resilient and efficient malware detection systems by integrating sophisticated vectorization techniques with cutting-edge machine learning algorithms, therefore fulfilling essential requirements in a dynamic threat environment.

Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.

Mobile Ad Hoc Networks (MANETs) are wireless networks formed by several nodes communicating on a peer-to-peer basis without being connected to any fixed infrastructure. These nodes could be laptop computers, personal digital assistants, mobile phones or sensors dispersed in an area to measure certain data and send the information to a larger node. Where a source node and a destination node are not within direct range, they communicate through multi-hop routing, i.e. nodes in between them relay messages between source and destination. The routing protocol plays a key role in finding and maintaining the route in MANETs. Routing protocol can be a uni-path and multi-path. A multipath routing protocol is designed to increase the reliability in MANET. This research work focuses on Quality of Service (QoS) based evolution of multipath routing protocol. For this purpose, diverse type of simulation scenarios is designed to find the impact of mobility, increasing the number of nodes and pause time in MANETs. The results revealed that multipath routing protocol has comparatively less delay (percentage decrease of 81.52%). While unipath routing protocols have less packet drop ratio (percentage decrease of 47.78%) and routing overhead (percentage decrease of 99.30%).

Underwater network size estimation is inefficient by applying conventional protocol based techniques used for terrestrial networks due to non-negligible capture effect, long propagation delay, high absorption and dispersion of the medium. For this reason, a statistical signal processing approach based on cross-correlation has been proposed in our previous works, which is equally applicable to any environment networks. Initially, this estimation approach was formulated without considering multipath propagation effects. But, one of the common difficulties of underwater or terrestrial wireless communication is multipath propagation. Multipath spread is more severe in underwater acoustic channel (UAC) than terrestrial radio channel. This paper aims to address the multipath propagation issue. To mitigate the effects of multipath propagation, a robust estimation approach using corss-correlation of Gaussian signals received at two sensors has been investigated in this paper.

A mobile ad hoc network is a collection of wireless mobile nodes that communicate with one another without any fixed networking infrastructure. Since the nodes in this network are mobile, the energy management and energy conservation become very critical in mobile ad hoc network. The nodes in this network have limited battery power and limited computational power with a small amount of memory. Such nodes must conserve energy during routing to prolong their usefulness and increase network lifetime. This research paper proposes a scheme that takes into consideration the energy awareness during route selection. This scheme observes power status of each and every node in the topology and further ensures the fast selection of routes with minimal efforts and faster recovery. Along with this scheme also introduce the concept of wirelessly recharge the nodes of the networks. The scheme is incorporated with the AODV protocol and introduces new protocol EEAODV the performance has been studied through simulation over NS-2.34.

The performance of an ad hoc routing protocol is highly dependent on the mobility model used in the ad hoc networks. Most of previous work used only Random Waypoint mobility model, which is unrealistic in many situations. The mobile nodes are often power constrained so that energy conservation is also an important issue on evaluating protocols performance. This issue can be sort out in this paper with the help of two concepts: energy conservation based on different mobility models and concept of wirelessly recharges the networks node. Most of them focused on metrics such as packet delivery ratio, delay, and route optimality. In this paper, three on demand routing protocols AODV, DSR and TORA would be evaluate at power management metrics. These protocols are simulated and compared under three different mobility models: Random Waypoint, RPGM, and Manhattan Grid and various scenarios.