Recent Advances in Computer Science and Communications - Online First

Introduction

One fundamental characteristic of Cognitive Radio Networks (CRNs) is their dynamic operating environment, where network conditions, such as the activities of Primary Users (PUs), undergo continuous changes over time. While Secondary Users (SUs) are engaged in communication, if a PU reappears on an SU's channel, the SU is required to vacate the channel and switch to another available channel. Thus, finding a stable route that minimizes frequent channel switches is a challenging task in CRNs.

Method

Existing solutions to reduce PU interference often overlook the energy consumption of nodes when forming clusters, focusing solely on the minimum number of common channels in a cluster. Consequently, these schemes suffer from frequent channel switches due to PU appearances. The proposed Cognitive Radio Network Routing (CRNR) approach aims to minimize frequent channel switches by employing a Reinforcement Learning (RL) technique called Q-Learning to select stable routes with channels exhibiting higher OFF-state probabilities.

Result

This strategy ensures that selected routes avoid rerouting by prioritizing channels with higher off-state probabilities. Experimental studies demonstrate that the CRNR approach enhances network throughput and reduces interference when compared with existing techniques. CRNR introduces a novel application of AI, use of Q-Learning, a reinforcement learning technique in wireless networks.

Conclusion

This bridges the gap between machine learning and network design, showcasing how intelligent algorithms can optimize communication decisions in real-time, which could inspire further exploration of AI-driven techniques in network management and beyond.

Introduction

Cardio Vascular Disease (CVD), a primary cause of death worldwide, includes a variety of heart-related disorders like heart failure, arrhythmias, and coronary artery disease (CAD), where plaque buildup narrows the heart muscle's blood vessels and causes angina or heart attacks. Genetics, congenital anomalies, bad diet, lack of exercise, smoking, and chronic diseases including hypertension and diabetes can cause cardiac disease.

Method

The symptoms can range from chest pain and shortness of breath to exhaustion and palpitations and diagnosis usually involves a medical history, physical examination, and electrocardiograms (ECGs), and stress testing. Lifestyle adjustments, medicines, angioplasty, and bypass grafts or heart transplants are possible treatments. Preventive measures include healthy living, risk factor management, and frequent checkups, which are few measures, whereas advanced algorithms can analyze massive volumes of ECG and MRI data to find patterns and anomalies that humans may overlook.

Results

The deep learning models increase arrhythmia, coronary artery disease, and heart failure diagnosis accuracy and speed. They enable predictive analytics, early intervention, and personalized treatment programs, increasing cardiac care results. The proposed DNN model consists of a 3-layer architecture having input, hidden, and output layers. In the hidden layer, 2 layers, namely, the dense layer and batch normalization layer are added to enhance its accuracy.

Conclusion

Three different optimizers namely Adam, AdaGrad, and AdaDelta are tested on 50 epochs and 32 batch sizes for predicting cardiovascular disease. Adam optimizer has the highest accuracy of 85% using the proposed deep neural network.

Background

The high-voltage cable is a critical component in power transmission systems, making regular inspections essential for the timely detection of potential hazards, schedule maintenance, and avoiding safety accidents.

Objective

This paper aims to use deep learning algorithms to improve the precision and timeliness of cable fault detection, thereby ensuring safe and secure power system operation.

Methods

Automatic cable fault detection based on YOLOv8s was conducted in the study in order to assist the power sector in automatically detecting cable faults.

Results

PConv and BiFPN networks were added to the backbone network to improve the feature fusion performance of the model. To enhance the model's identification capabilities, the WIoU loss function was modified.

Conclusion

The proposed method allows for the rapid detection of cable faults by analyzing three common fault types: “thunderbolt,” “wear,” and “break.” By deploying this approach on edge computing devices mounted on UAVs, automatic inspection of power faults can be effectively achieved.

Background

Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal.

Objectives

Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage.

Method

This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data pre-processing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience.

Result

The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning.

Conclusion

By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.

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