In today’s world, the safety of children is of utmost importance due to numerous compelling factors, for example, accidents and injuries. In this fast-paced lifestyle of the new age parents, they might not always be able to accompany their children everywhere therefore the need for the tracking of the child including safety considerations and also the parent’s desire to stay connected with their child and in the absence of the parents the guardian of the child can look up for the safety of the child. This paper presents an implementation of the smart bag for toddlers which is built using blockchain technology and language solidity that ensures the tracking facility of the child. blockchain is the decentralized ledger technology that provides transparency and security between the networks. Therefore we have created a digital contract known as the smart contract on blockchain technology named a smart bag for toddlers. A smart contract is a digital agreement that is signed and stored on the blockchain and executes automatically when its terms and conditions are met.

Unmanned Aerial Vehicles (UAV) are highly versatile systems with applications expanding in various fields, for instance, Surveillance, Reconnaissance, Disaster Response, Agriculture, and many more. Although UAVs have many important advantages over conventional manned aircraft, such as cheaper operating costs and a lower risk for human pilots, their vulnerability to Radio Frequency (RF)-jamming poses a substantial risk to navigation and communication systems by disrupting signals. UAVs depend on remote control and autonomous operations. These attacks are concerning in many application areas. Communication and navigation play crucial roles in these autonomous systems. This paper aims to explore different deep-learning algorithms for the detection of RF-jamming attacks in UAVs. A comparison analysis is conducted to evaluate five distinct architectures using conventional evaluation metrics criteria comprising accuracy, precision, recall, confusion matrices, and the area under the ROC curve. These comparative analyses helped in selecting an accurate architecture for the definition, RNN architecture gave an impressive accuracy of 93% and demonstrated a superior performance than other architectures. This paper aims to strengthen RF-jamming detection systems in UAVs to enhance their safety and operational reliability in several scenarios.

The quick progress of technology has greatly influenced multiple industries, with Artificial Intelligence (AI) and Robotic Process Automation (RPA) leading the way in this transformation. When combined, these technologies can improve business procedures, increase productivity, and lower operational expenses in various sectors. The primary goal of this task is to examine how AI technologies can be incorporated into current RPA frameworks to promote the development of intelligent automation systems. These improved systems are created to not just complete specific tasks but also to autonomously make decisions and flexibly optimize operations. This research aims to link AI theoretical models with their practical uses in RPA, highlighting recent advancements and predicting future trends in AI-driven RPA solutions.

In dynamic defense operations, satellite imagery has a major role in offering crucial insights into geographical landscapes and possible dangers. Satellite imagery provides an aerial view of the battlefield environment, which helps strategically improve decision-making. The proposed framework makes use of deep learning techniques to segment satellite images into particular regions. The image is segmented into different classes, such as land, buildings, roads, vegetation, and water. The combination of the segmentation model with a comprehensive situational awareness framework allows military personnel in analyzing terrain, identification of infrastructure, and detection of anomalies. This helps improve operational effectiveness and rapid response in dynamic battlefield environments.

Software Defined Networks (SDN) utilize the network devices as virtual devices to analyze the traffic and transmit the relevant packets. It is quite beneficial for the internet of Things (IoT) where the huge number of devices share the packets which cannot be handled by the conventional networks. SDN is also essential to manage congestion scenarios during massive communication especially in video streaming. The main problem arises when the residual queues on the network path are left underutilized whereas the bandwidth is reduced as per the minimum bandwidth link between any two nodes in the path. It occurs based on the feedback based congestion avoidance mechanism. This work presents a proposed mechanism to utilize the residual queue in the communication path to enhance the minimum bandwidth. It maintains the average queue length to analyze the percentage of queue utilized and then decide about the packet dropping probability. In proposed scheme, the congestion can be managed earlier whereas the base scheme applies a constraint of double the size of previous threshold which may result in identifying the congestion at a later stage where the retreat is hardly possible. According to the findings in results after simulations, the proposed IAGRED dominates the counterparts in terms of queue delay, queue length, link utilization and throughput.

Directing research on Alzheimer’s disease toward only early prediction and accuracy cannot be considered a feasible approach toward tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence, and advancing toward the human-computer interface (HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using SHAPley additive explanation, local interpretable model-agnostic explanations, and DL algorithms. The use of DL algorithms—logistic regression (80.87%), support vector machine (85.8%), k -nearest neighbor (87.24%), multilayer perceptron (91.94%), and decision tree (100%)—and explainability can help in exploring untapped avenues for research in medical sciences that can mold the future of HCI models. The presented model’s results show improved prediction accuracy by incorporating a user-friendly computer interface into decision-making, implying a high significance level in the context of biomedical and clinical research.

Abstract The heart of the application's secure operation is its software code. If the code contains flaws, the entire program might be hacked. The issue with software vulnerabilities is that they reveal coding flaws that hackers could exploit. The prevention of cybersecurity issues begins with the program code itself. When writing software code, a software developer must consider expressing the application's architecture and design requirements, keeping the code streamlined and efficient, and ensuring the code is safe. Secure code helps save the system from various cyber‐attacks by eliminating the weaknesses that many hacks rely on. To assist the software organization in Secure Software Coding (SSC), this article proposes a readiness model for SSC, namely SSCRM. The proposed model has five levels; SSC challenges and best practices (BP) are mapped at each level. The proposed model will help the organizations better understand SSC challenges and BPs and provide a roadmap for developing secure software code. The proposed model was evaluated using three case studies. The findings demonstrate that the proposed approach helps determine an organization's SSC level.

Internet of Things (IoT) comprises of a huge number of connected devices that can communicate within the same network and across the networks. IoT-enabled wireless sensor networks (WSNs) are getting growing interest due to its wide applicability in healthcare, patient monitoring, transportation, and surveillance. The main issue is that the network is mostly deployed in hostile environments where an attacker may physically destroy the CHs or other technical fault may occur. It isolates the cluster and causes loss of sensitive data from that region. This article presents a critical data reclamation (CDR) protocol that provides secure data transmission for isolated clusters. We present the data transfer and data aggregation algorithms for sensing nodes and data receiving and extraction at CH and sink. We performed extensive simulations using NS-2.35. The results prove the dominance of CDR in contrast to counterparts in terms of communication cost, energy consumption, and resilience.

A growing amount of research conducted in digital, cooperative with advances in Artificial Intelligence, Computer Vision including Machine learning, has managed to the advance of progressive techniques that aim to detect and process affective information contained in multi-modal evidences. This research intends to bring together for theoreticians and practitioners from academic fields, professionals and industries and extends to be visualizing cries such epidemic, votes, social Phenomena in spherical representation interactive model working in the broad range of topics relevant to multi - modal data processing and forensics tools developing. Furthermore, progress has been made in this research besides that in this research conducted progression of mapping claims in present epoch necessitate the capacities of virtual guide of any understandable Geo-Visualization of spatial features that talented to convert the quantities of spatial pattern into cartography. The enlargement of a novel approaches fit for visualization of spatial pattern constituencies Starting exclusive Input Set of object O, set associated with feature F for regenerating Output the set C , interested region I special target C Even so, as indicated by the construction of the prototype as listed earlier in this thread, does it have the incentive for improvements: Representation could be used by Google Earth can Using Project enhancement representation whereby provides a 3D or 4D interaction with life measures with a view to cartography. In addition, the initiative suggests that a tool not accessible for disseminating information to the public can be addressed by the use of online mapping, which fuses with trends visualization for political circles and electors. But as mentioned above the framework is developed and it's also possible in the current example, for improvements: The project's representation 3D or 4D interacting Earth can use measures of life Earth From the map viewpoint. That's what that says. That means that. Which just means. Developers have concerns that. So it. Designers concern about that. This study supports the new, multi - demission and deployed countries in conjunction with another data is processed. Comprehensive, well-interpreted source data for the Data like Malaysia Jabatan Pendaftaran (JPN).

Internet of Things (IoT) is a network of “things,” connected via Internet, to collect and exchange data. These “things” can be sensors, actuators, smartphones, wearables, computers, or any object that is interconnected to provide specific services. Similarly, wireless sensor network (WSN), as a part of IoT, forwards the gathered data after sensing any event. The scalability and heterogeneity of IoT offer limited protection and is prone to diverse attacks, including WSN-inherited attacks. Moreover, IPv6 routing protocol for low power and lossy networks (RPL), a de facto routing protocol for IoT networks, also suffers from certain vulnerabilities based on its features and functionalities. Researchers have proposed various mitigation mechanisms for secure networks and routing in IoT. Recently, trust-based approaches have gained tremendous interest from the research community to embed security in IoT networks and routing protocols. In the existing literature, several trust models have been introduced according to the security needs of the IoT system, such as SecTrust, DCTM-IoT, CTRUST, etc. In this research, security issues and requirements of IoT networks and RPL routing protocol are studied with respect to various attacks, such as Blackhole, Spoofing, Rank, etc. Additionally, various mitigation methods and significance of trust models in IoT for secure routing are analyzed. Further, trust metrics in IoT environments, including the open issues and research challenges, as well as the implication of trust as a security paradigm in IoT networks and routing protocols are discussed.

With the proliferation of information and communication technology in every walks of the society, including healthcare services, digitization, and increased sophistication have been gaining pace, digital healthcare alternatives such as electronic healthcare record (EHR) have gained prominence with increased patients' data volume. However, traditional EHR-based systems are plagued by data loss risks, security and immutability consensus over health records, gapped communication among constituted hospitals, and inefficient clinical data retrieval systems, among others. Blockchain has been developed as a decentralized technology that holds the promise to address the aforesaid facilities in EHR-based systems. This article presents a patient-centric design of a decentralized healthcare management system with blockchain-based EHR using javascript-based smart contracts. A working prototype based on hyperledger fabric and composer technology has also been implemented which guarantees the security of the proposed model. Experiments with the hyperledger caliper benchmarking tool provide performance such as latency, throughput, resource utilization, and so on under varied scenarios and control parameters. The results affirm the efficacy of the proposed approach.