A Hybrid Deep Learning Framework for Accurate Polycystic Ovary Syndrome Detection Using Ultrasound Imaging

Polycystic Ovarian Syndrome (PCOS) is a hormone-related health condition in women, commonly classified as an endocrine disorder. It is most prevalent during the childbearing years, typically between the ages of 15 and 44. PCOS leads to hormonal imbalances that cause irregular menstrual cycles, hair loss, and other symptoms, and it is associated with long-term health risks such as heart disease and diabetes. Recent advances in deep learning have shown promising results in accurately recognizing and differentiating ovarian cysts from other ovarian tumours. This study proposes a novel technique for PCOS symptom detection by analysing ovarian images through feature extraction, classification, and metaheuristic-based optimization. Ovarian images are first pre-processed for noise removal and smoothing, followed by feature extraction and classification using a Convolutional Wavelet Attention Neural Network with a Naïve Bayes Fuzzy Autoencoder (CWANN–NBFA). Optimization is then performed using the Metaheuristic Multilevel Hawks Algae Optimization (MMHAO) algorithm. Experimental evaluations were conducted on multiple ovarian image datasets. The proposed technique achieved an accuracy of over 98% across the PCOSUSG, KFHU, and MMOTU datasets, demonstrating its robustness and effectiveness in addressing the challenges of PCOS detection

Mitigating Wormhole Attacks’ Risks within Wearable Body Network

In this research, we sought to develop a trust and secure routing protocol based on the Ad-hoc On-Demand Distance Vector (AODV) routing to combat wormhole attacks in Wearable Body Networks (WBNs), which integrates a routing strategy that leverages the path-checking method to detect and isolate paths affected by wormhole attacks effectively, it employs a routing technique that prioritizes nodes with the most heightened remaining energy during data transmission, along with a mixed cryptographic algorithm that combines the modified One Pad Time with the modified Affine ciphers to ensure safe transmission against malicious biosensor threats. Experimental findings indicate that our proposed protocol transcends the classic AODV routing protocol across all evaluation parameters, including packet delivery ratio, throughput, and energy consumption. Its primary advantage lies in considering multiple factors, like detecting unauthorized biomedical biosensors, efficient energy utilization in the network, and secure data transmission—differentiating it from other safe routing protocols. Moreover, the mixed encryption algorithm enhances efficacy and bolsters sensitive data security compared to classic cipher methods like the One Pad Time and Affine ciphers

Supporting Communication for Deaf People with Sign Language Recognition Using Deep Learning Approach

Sign language recognition (SLR) plays a crucial role in improving communication for deaf individuals. This paper investigates the recognition of sign language through deep learning models based on action features using Skeleton data from the Argentinian Sign Language (LSA64) dataset. The models explored include Multi-layer Perceptron (MLP) Neural Network, and Long Short-Term Memory (LSTM). The MLP Neural Network, utilizing multiple layers of perceptrons, reached an accuracy of 96.10%. The LSTM model, excelling in processing sequential data, attained the highest accuracy at 98.60%. These results demonstrate the effectiveness of deep learning models in sign language recognition, with LSTM showing the most promise due to its ability to effectively capture temporal dynamics. Consequently, this study opens up prospects for applying sign language recognition technology in practice, contributing to enhancing the quality of life for deaf individuals

CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification

In modern network environments, securing systems from newly emerging attacks is essential, and a constructive approach is the use of an IDS (Intrusion Detection System). When faced with attacks that are not in the list of predefined patterns, traditional IDS methods such as signature-based detection or standalone machine learning models may not function properly to detect such attacks because they are not adaptable and not designed to deal with this type of attack. The current IDS systems that employ deep-learning architectures have enhanced detection capabilities; however, most prior art systems are limited by partial feature learning, which only learns features of either spatial or temporal traffic structures. Meanwhile, the lack of contextaware mechanisms, such as attention, limits their ability to attend more to the most informative network components, leading to suboptimal detection performance and generalization. To counter this issue, in this work, we introduce CyberShieldDL, which is the first deep learning-based IDS framework with a novel hybrid architecture: IntruNet-Hybrid, combining Convolutional Neural Networks (CNN) for spatial pattern extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential feature extraction, and an attention mechanism to learn the salient features for intrusion detection dynamically. To create the framework, an optimized preprocessing and feature selection pipeline is presented to effectively and costeffectively prepare the model input. Extensive experiments on the CICIDS2017 dataset demonstrate that CyberShieldDL consistently outperforms the state-of-the-art, achieving an overall accuracy of 98.35% and high precision, recall, and F1-score in various attack scenarios. Cross-dataset validations on NSL-KDD and UNSW-NB15 also verify the system's generalization. The design provides a scalable and flexible solution for realworld network security, offering the flexibility and adaptability necessary to enhance classification accuracy and robustness against evolving attack patterns. Its modular construction enables us to extend it for real-time deployment and future adversarial robustness easily

Enhancing Intrusion Detection Systems in Cloud Computing Environments: A Hybrid Machine Learning Approach

Intrusion Detection Systems (IDS) are essential for maintaining the security of cloud computing environments, which are increasingly targeted by sophisticated cyber-attacks. This paper presents a novel hybrid approach for intrusion detection in cloud environments, combining Random Forest for feature selection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Transformer networks for contextual learning. Evaluated on CICIDS2017 and CSE-CIC-IDS2018 datasets, the proposed approach achieved weighted F1-scores of 97% and 99% respectively, significantly outperforming baseline models. The hybrid model improved accuracy from 95.1% to 98.0% and F1-score from 94.2% to 97.0% compared to LSTM-only approaches. While excelling at detecting common attack patterns such as Distributed Denial of Services (DDoS), challenges remain in identifying rare threats including SQL Injection. This research contributes to cloud security advancement by demonstrating the effectiveness of hybrid machine learning architectures in addressing the unique challenges of intrusion detection in distributed cloud infrastructures

Trainer Kit for Aroma Classification Using Artificial Intelligence

This research focused on the development and evaluation of machine learning algorithms for aroma classification using sensor data, implemented within the e-Trainose system. Various algorithms, including Neural Network, Support Vector Machines, and Random Forest, were tested to determine their effectiveness in distinguishing between different aroma samples, namely alcohol, coffee, and tea. The study utilized an array of metal oxide semiconductor sensors to capture the volatile organic compounds associated with each aroma. The features tested included sensor responses such as resistance changes and Gaussian smoothing of sensor data. Among the algorithms tested, Neural Network demonstrated the highest accuracy (98.89%), precision (99.10%), recall (99.10%), and F1 score (99.10%), making it the most reliable model for this task. These results highlight the potential of using machine learning with e-Trainose for real-time aroma detection and classification. The research paves the way for future advancements in the field, including the development of hybrid models and further optimization of sensor-based classification systems

Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms

Cyber threats continue to grow in this era since the bad actors are attempting to exploit individuals, organisations, and systems. The latest development in artificial intelligence has unleashed strong agents at the fingertips of humanity. As open as it is, it has made more room for possible bad actors. Systems that can successfully counter these threat actors need to be created to rescue humanity. In this research work, RNN and Random Forest classifiers' hybridised models are combined for the development of a Network Intrusion Detection System (NIDS) based on the benchmark dataset (CICIDS 2017) The requirement for an efficient and accurate method to detect network intrusions, both known and zero-day anomalies, is the primary problem considered. This research aims to enhance the accuracy and reliability of intrusion detection systems through a hybrid modelling approach. For evaluating the performance of the proposed model, various measures like accuracy, precision, recall, F1 measure, true positive rate, and true negative rate were employed. The hybrid model showed very good results with testing accuracy of 96.08%, precision of 96.0%, and recall of 96.0%, along with an F1 measure of 96.0%. The result of the experiment indicates that the model is effective and, when implemented, can detect and classify cyberattacks in modern environments

Leveraging Gradient based Optimization based Unequal Clustering Algorithm for Hotspot Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs) serve as the basic unit of the Internet of Things (IoT). Because of their low prices and potential use, in recent years, wireless sensor networks (WSNs) have garnered attention for various uses. Then sensor nodes (SN) can prepared with limited battery is critical energy utilization be monitored closely. Hence, reducing the node energy utilization is obviously vital to extending the network lifespan. Clustering is an effectual manner for diminishing energy utilization in WSNs. In a multi-hop clustered network condition, every SN transfers data to its individual cluster head (CH), and the CH gathers the information from its member nodes and relays it to base station (BS) using other CHs. Conversely, the “hotspot” issue is inclined to take place in clustered WSNs while CHs near the BS are heavier intercluster forwarding tasks. In this article, we leverage Gradient based Optimization based Unequal Clustering Algorithm for Hotspot Problem (GBOUCA-HP) technique in the WSN. The GBOUCA-HP technique is applied to get rid of the unequal clustering process in the WSN using metaheuristic algorithms. The GBOUCA-HP technique focuses on the optimization of energy usage, resolving hot spots, and extending the network lifespan. In the GBOUCA-HP technique, the GBO algorithm is based on two concepts such as diversification and intensification search and the gradient‐based Newton’s phenomena. Moreover, the GBOUCA-HP technique adaptive selects the CHs with varying cluster sizes for diverse energy levels and computation abilities of the nodes. The widespread set of simulations and evaluations shows the effective performance of the GBOUCA-HP technique. The GBOUCA-HP technique is found to be a significant approach to tackling the hotspot issue in the WSN with the intention of decreasing energy consumption optimization and enhancing network efficiency

Fractional Order Sliding Mode Control to Mitigate Power Quality Issues using Dynamic Voltage Restorer in Distribution Network

Power quality (PQ) issues lead industrial customers to suffer significant financial losses. These PQ issues are garnering more attention from electricity suppliers and consumers in the modern day. This study addresses prevalent PQ issues, namely voltage sag and swell, stemming from a decrease in RMS voltage within electrical networks, particularly impacting sensitive loads. The solution proposed involves employing a series connected custom power device (CPD) named as dynamic voltage restorer (DVR) with an integrated DC battery for energy storage, to consistently maintain the requisite voltage magnitude. To effectively combat voltage sag and swell, the study introduces a novel control strategy known as fractional order sliding mode control (FOSMC). Noteworthy features of the FOSMC methodology include its capacity to autonomously and dynamically address sag and swell issues. The Simscape toolbox of MATLAB®/Simulink® is used to perform simulations to showcase the efficacy of the FOSMC technique. The results demonstrate that this strategy ensures total harmonic distortion remains below 5% and achieves sag/swell mitigation in less than 2 milliseconds, aligning with SEMI-F-47 and IEEE voltage standard 1159-2019. In summary, the study introduces and validates a robust control strategy implemented in a DVR system to autonomously alleviate voltage sag and swell issues, with simulation results supporting its effectiveness in upholding PQ standards. The FOSMC scheme with DVR is also compared with FOSMC scheme with DSTATCOM as well as with super twisting sliding mode control (STSMC) algorithm and classical sliding mode controller (SMC) to show the effectiveness of the proposed scheme. The FOSMC technique with DVR is more effective in restoring voltage sag/swell and PQ issues

Enhancing GRU-Based DRL with Delta-LiDAR for Robust UAV Navigation in Partially Observable Dynamic Environments

Partial observability and sensor limitations are challenging for the navigation of autonomous Unmanned Aerial Vehicles (UAVs). Deep Reinforcement Learning (DRL) algorithms have emerged as potential tools in advancing this field. However, their effectiveness degrades in challenging environments, particularly in the presence of dynamic obstacles. Recent research trends emphasize the need for new DRL variants that guarantee robustness, real-time adaptability, and improved generalization under uncertainty. This paper proposes a lightweight DRL architecture that combines Proximal Policy Optimization (PPO) with a Gated Recurrent Unit (GRU), extended with a temporal LiDAR differencing feature called Delta-LiDAR. The difference between consecutive LiDAR scans is computed to provide the velocity and directional cues without the computational burden of Long Short-Term Memory (LSTM) networks. We evaluate three models, PPO-LSTM, PPO-GRU, and Delta-LiDAR augmented PPO-GRU in a 3D simulated UAV navigation environment characterized by noise, clutter, and dynamic obstacles. We considered several metrics, including success rate, collision frequency, trajectory smoothness, and computational efficiency, to determine the effectiveness of each architecture. The experimental results demonstrate that Delta-LiDAR improves GRU-based temporal reasoning. The deployment complexity is reduced compared with the LSTM-based architecture, which makes it ideal for real-time UAV operation in partially observable environments