J. Jabez

A Wireless Sensor Network (WSN) is an efficient system for monitoring distributed areas and controlling environments; however, such networks are susceptible to malicious node attacks that bring forth network insecurity and untrustworthy data. WSNs are vulnerable to malicious nodes and cyber attackers that can interfere with data transmission, leading to compromised decision-making systems. Traditional security techniques against WSNs lack flexibility in real-time detection and data integrity because of constrained processing resources and vulnerabilities from centralized storage. This work aims to improve detection accuracy through a multi-stage strategy, which constitutes the general objective of this research. The presented model uses WSN-DS and WSN-BFSF datasets. The data are pre-processed using Localized-Global Depth Normalization for uniformity, followed by feature selection via Boosted Tern-Cat Hunting Optimization, which combines Cat Hunting Optimization and Boosted Sooty Tern techniques to reduce dimensionality. The attack detection is performed by a Parallel Triple Graph Attention-based Convolution Network, which employs Quantum Parallel Deep Convolution and Triple Graph Attention Networks. The RMRO optimizes the model's parameters to classify more accurately, and the benign data are safely stored through the Consensus-Aided PoA Decision Blockchain Engine and InterPlanetary File System. This approach achieved 99.4% accuracy, 99.3% recall, and 99.5% F1 score on the WSN-DS dataset and 99.2% accuracy, 99.1% precision, and 99.3% F1 score on the WSN-BFSF dataset while showing robustness across different combinations of sensors. Hence, the Tri-QPdCNet offers a pioneering approach toward securing WSNs from dynamic and persistent attacks by providing an improved framework for anomaly detection using a strong, scalable architecture, augmented with blockchain technology. That leads to more robust WSN infrastructures that can be more securely and smoothly deployed in real-time critical environments.

The amalgamation of machine learning (ML) techniques and natural language processing (NLP) is leveraged to evaluate the sentiment of textual input. With the increasing popularity of e-commerce platforms like Amazon, product reviews have emerged as an essential source of information for potential purchasers, providing insights into product quality and performance from the consumers' viewpoints. This study aims to systematically organize and analyze customer opinions to effectively capture consumer sentiment based on product reviews. In this study, we propose a deep learning framework that combines a stacked 1D convolutional layer (CNN) with a Long Short-Term Memory (LSTM) network, using pre-trained Word2Vec embedding as fixed input representations. Evaluated on a large Amazon product review dataset, our model — StackedCNN-LSTM-W2V — achieves a classification accuracy of 99%, outperforming traditional CNN, LSTM, and logistic regression baselines.

The chance of developing "Alzheimer's Disease (AD)" increases every 5 years after 65 years of age, making it a particularly common form of neurodegenerative disorder among the older population. The use of "Magnetic Resonance Imaging (MRI)" to diagnose AD has grown in popularity in recent years. A further benefit of MRI is that it provides excellent contrast and exquisite structural detail. As a result, some studies have used biological markers backed by "structural MRI (sMRI)" to separate AD populations, which indicate differences in brain tissue size and degradation of the nervous system. The lack of properly segmented regions and essential features by the existing models might affect classification accuracy for AD. The categorization of AD in this study is based on sMRI. In this research, the hybrid Deep-Learning Models "SegNet and ResNet (SegResNet)" have been proposed for segmentation, feature extraction, and to classify the AD. SegNet network is used to identify and segment specific brain areas. Edges and circles are the SegNet's first levels, whereas the deeper layers acquire more nuanced and useful features. SegNet's last deconvolution layer produces a wide range of segmented images linked to the 3 categorization labels "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "AD" which the machine has earlier found out. To increase classification performance, the attributes of each segmented sMRI image serve as strong features of the labels. To enhance the feature information used for classification, a feature vector is built by combining the values of the pixel intensity of the segmented sMRI images. ResNet-101 classifiers are then used for characterizing vectors to identify the presence or absence of AD or MCI in each sMRI image. In terms of detection and classification accuracy, the proposed SegResNet Model is superior to the existing KNN, EFKNN, AANFIS, and ACS approaches

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

This work exhibits an efficient women security recognition system utilizing GPS and GSM modems. Inspite of different enormous growth in technology, science and mechanical engineering today world creates so many threats and risky spot for woman and for some kids. Women are living in the basic environment and society in which the dread of wrongdoing is dependably in high system. Today, viciousness against women unavoidably expanded. This paper targets a gadget which guarantees the assurance of women. This gadget enables women in the risky circumstances by recognizing their area, to secure and call the closest assets. Whenever you sense risk, you should simply, hang on the catch of the gadget. The system comprises of an ARDUINO NANO Controller, Bluetooth module, Taser and an Android versatile. When it is actuated, it tracks the spot of the women utilizing GPS (Global Positioning System) and sends crisis messages utilizing GSM (Global System for Mobile correspondence) through Android portable Application to SOS contacts (Predefined Numbers). In the event that the message isn't checked by the specific number referenced, the system will give the call to the predefined number. It is the convenient gadget for the lady which will probably convey and simple to utilize. This gadget is joined to Taser will create a stun which will assault the abusers. The utilization of advanced parts guarantees precision and makes it solid. The system additionally gives voice acknowledgment includes in the Android versatile Application which will help in sort of crisis circumstances when the gadget is harmed.

RFID is a technology similar to that of bar code scanning. An RFID system consists of tags, which use radio frequency signals to transmit its location information to a reader, which usually sends this information to a server that processes it according to the needs of the application. This paper presents a system that can track buses across a city by placing RFID tags in the buses and the readers in every alternative bus stop. The local server for the city recieves the location information, and alerts the forthcoming bus stops in the route of the bus, of the bus' number, route and expected time of arrival, which are then displayed at the stop. This system thus describes is a cost effective and easy to implement scheme for tracking buses in real time.