This study presents a deep learning solution for detecting road anomalies via a hybrid architecture consisting of a Masked Autoencoder (MAE) and a Vision Transformer (ViT) model. It presented a framework for dual road classification, namely an intact road (good) and defected road (bad) where defected roads are characterized by anomalies such as potholes or cracks. The target road anomaly classification model was trained and tested using publicly available datasets of road condition images. The model demonstrated good feature extraction as well as good generalization with a training accuracy of 99.79% and a test accuracy of 98.29%. Furthermore, we integrated the road anomaly detection model into a web-application providing real-time road anomaly detection, exemplifying the possible benefits of applying computer vision and machine learning algorithms to improve road maintenance in Nigeria.

In the rapidly evolving digital landscape, cyberattacks are becoming increasingly sophisticated, posing significant threats to organizations and individuals worldwide. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, offering advanced capabilities to detect, prevent, and mitigate these attacks. This article explores the integration of AI into cybersecurity frameworks, emphasizing its role in identifying anomalies, predicting potential threats, and automating response mechanisms. By leveraging machine learning algorithms and data-driven insights, AI enhances the speed and accuracy of threat detection, effectively combating challenges posed by modern cyberattacks. However, the adoption of AI also introduces new complexities, such as adversarial AI and ethical considerations. This paper delves into the dual nature of AI in cybersecurity, providing a comprehensive overview of its benefits, challenges, and future potential in safeguarding digital ecosystems against ever-evolving cyber threats.

In gene expression analysis, selecting informative genes is essential for uncovering biological mechanisms and identifying potential biomarkers. However, conventional gene selection methods often struggle with scalability and parameter tuning, limiting their effectiveness in large-scale datasets and algorithmic optimization. To overcome these challenges, we propose Autoencoder-based Adaptive Multi-Objective Particle Swarm Optimization for Gene Selection (AAMOPSO). Our approach incorporates an autoencoder-based preprocessing step to enhance scalability by learning a compressed representation of gene expression data, reducing dimensionality while retaining critical features. Additionally, we introduce an Adaptive Parameter Tuning mechanism within the Multi-Objective Particle Swarm Optimization (MOPSO) framework, dynamically adjusting algorithm parameters based on real-time performance metrics. Extensive experiments on four benchmark microarray datasets demonstrate that AAMOPSO consistently outperforms existing state-of-the-art methods in classification accuracy and the compactness of selected gene subsets.

The increasing reliance on interconnected systems has elevated the importance of robust cybersecurity and efficient data networking. As digital transformation accelerates, emerging cyber threats exploit vulnerabilities in critical infrastructure, emphasizing the need for innovative solutions. This paper investigates the application of machine learning in enhancing cybersecurity and data networking through predictive models. By analyzing empirical data from major network providers, cybersecurity firms, and detailed case studies, this research demonstrates the effectiveness of machine learning in improving threat detection, optimizing network performance, and mitigating risks. Findings reveal that machine learning-driven prediction models enhance security measures by 85%, optimize network efficiency by 30%, and significantly reduce financial losses stemming from cyberattacks. These predictive systems provide early warnings and automate responses, enabling organizations to transition from reactive to proactive security strategies. Furthermore, machine learning algorithms dynamically allocate network resources, reducing latency and increasing bandwidth utilization. The results showcase the transformative potential of machine learning in safeguarding digital ecosystems against evolving threats. As industries become increasingly reliant on data networking, the adoption of machine learning not only fortifies cybersecurity frameworks but also streamlines operational efficiency. Addressing challenges such as integration with legacy systems, high implementation costs, and the need for skilled personnel will be critical to unlocking the full potential of this technology. This research underscores the indispensable role of machine learning in shaping a secure and resilient digital future.

Medical report generation demands accurate abnormality detection and precise description generation from CT images. While large language models have shown promising results in natural language processing tasks, their application in medical imaging analysis faces challenges due to the complexity of fine-grained feature detection and the requirement for domain-specific knowledge. This paper presents a novel framework integrating large language models with specialized medical image processing techniques for fine-grained abnormality detection and natural language description generation. Our approach incorporates a multi-modal knowledge enhancement module and a hierarchical attention mechanism to bridge the gap between visual understanding and textual description. The framework employs an adapter-based architecture for efficient domain adaptation and introduces a medical knowledge-enhanced loss function to improve description accuracy. Experimental results on three public datasets demonstrate the effectiveness of our approach, achieving 94.6% detection accuracy and a BLEU-4 score of 0.421 for description generation, surpassing current state-of-the-art methods. The system shows particular strength in handling subtle abnormalities, with a 91.2% average precision in fine-grained detection tasks. Comprehensive ablation studies validate the contribution of each component, while qualitative analysis demonstrates the clinical relevance of generated descriptions. The proposed framework represents a significant advancement in automated medical image analysis, offering potential benefits for clinical workflow optimization and diagnostic support.

The main goal of CT-based triage is to shorten the time it takes to reach the expert opinion for patients in emergency situations, especially in cases of cranial fractures and intracranial hemorrhage. Increasing the performance in this regard may be possible with the use of artificial intelligence-supported software that may pre-scan the images and put them in order of urgency before the human triage officer is able to evaluate them. The project involves the development software that quickly classifies cases into one of two groups, urgent or non-urgent, by analysing the computerized tomography (CT) image of the brain taken without the administration of intravenous contrast material. In this way, more effective triage is aimed. The software developed for this purpose was observed to have high performances in two separate machine learning models. Additionally, a visual interface that allows viewing DICOM files was developed within the scope of the project.

This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments.

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

A 30X20X1.6mm3 Novel Rhombus Shaped ring type multiband Antenna was proposed in this paper. The Antenna has 25x15mm2 partial ground inserted below the FR4 Epoxy Substrate to improve performance of Antenna. In addition, we added two rectangle shaped patch structures of dimensions 14X5.5mm2 on both sides of Feed structure to improve S11.The proposed design operates at three bands covering C, X, and Ku band regions. Here we got better S11 at 12.1GHZ which is useful for Radar Applications.

In recent times, there has been a swift advancement in the creation of smart cities. A smart city is a metropolitan area that makes use of cutting-edge technology for effective resource management and monitoring. The goal is to maximize energy and cost savings while simultaneously enhancing the quality of life for its citizens. With an emphasis on energy-related monitoring, a smart energy meter is the fundamental element that forms the basis for all data gathering, analysis, and automation. This paper describes how to turn a regular energy meter into a smart energy meter by adding an Arduino and a GSM module. With the help of the Internet of Things, this smart energy meter is further improved using IoT. An embedded controller (Arduino) and a GSM modem will be integrated by the smart energy meter system to transfer data over a GSM mobile network, such as generated invoices, spent energy in kWh, etc. IoT can be used to enable energy providers and power companies to monitor and control their customers' services without the need for human labour by providing them with the user -generated data. By connecting the smart meter to the Internet of Things, the user can access the data using a password and ID that has been issued from any location in the globe. Major energy companies will benefit from our project's user-friendliness, reduced labour costs, and decreased error and loss rates.