Skin Disease Classification Using Multi-Model Optimization and Augmentation

Skin diseases affect millions globally, posing screening challenges due to complex lesion characteristics and limited access to medical expertise. Traditional screening methods are time consuming, often requiring extensive laboratory testing. Deep learning and machine learning techniques have gained significant traction in recent years, serving as powerful tools in tackling complex problems, particularly in areas requiring substantial prior knowledge, such as biomedicine. With the challenge of inadequate medical resources, these methods have found impactful applications in disease screening, emerging as a pivotal research focus on dermatology. This project aims to develop an automated skin disease screening system using machine learning and deep learning techniques. The system is designed to accurately identify skin diseases, enhance early detection, address existing challenges in screening and ensure accessibility and affordability for all. This provides a concise review of the classification of skin diseases, leveraging Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN) to analyze skin lesion characteristics and evaluate imaging technologies. By exploring the strengths of CNNs due to its high performance in image classification and feature extraction. KNN providing evidence by identifying similar images, making it an explainable AI model. This study presents an Evidence based screening system a virtual dermatology platform leveraging cutting-edge artificial intelligence and deep learning techniques for efficient skin disease classification. Using pre-trained models like GoogleNet, EfficientNet, ResNet, DenseNet, MobileNet and achieving a classification accuracy of 97% through EfficientNet. significantly reducing screening time and cost. The proposed system optimizes preprocessing, transfer learning, model training and cross-validation, significantly improving accuracy. The results highlight AI's potential to revolutionize dermatological screening, reducing costs and improving early detection

A new Dynamic Routing Approach for Software Defined Network

Introduces a new dynamic routing approach tailored for Software Defined Network (SDN) that takes advantages of the programmability and centralized control inherent SDN architectures. Traditional routing protocols often struggles often to adapt to dynamic network conditions, leading to suboptimal performance and resource utilization. In contrast the objective of the paper is to proposed approach uses real time network information collected by the SDN controller to dynamic adjust routing decisions and dynamic routing algorithms for software define networks in wide area network (SDN-WAN), provide a new approach; By employing a combination of machine learning algorithm and network speed back mechanism. Using the approach optimizes routing paths based on factors such as link utilization and quality of service requirements.  The shortest feasible path (SOFP) is an adaptation of the shortest feasible path algorithm that uses a statistical technique from the OpenFlow interface. The goal of the SFOP algorithm is to efficiently use SDN-WAN resources by determining the best route from source to destination.  Overall, the dynamic routing approach provides a promising solution to efficiently manage network traffic in SDN. Paving the way for more adaptative and responsive networking infrastructure

Cardiovascular Disorder Detection in Diabetes Mellitus Patients: An Integrated VGG and Bi-LSTM Model Optimized Using the ABC Algorithm

There is a major public health concern at the intersection of Diabetes Mellitus (DM) and Cardiovascular Diseases (CVDs). Patients with a diabetes diagnosis are more likely to experience a variety of cardiovascular problems. Better patient outcomes and lower healthcare costs can result from early diagnosis of these problems. This study presents a fresh computational model to tackle this problem. This research presents an integrated method that optimizes the VGG and Bidirectional Long Short Tem Memory (Bi LSTM) models together with the help of the Artificial Bee Colony (ABC) algorithm, which is based on the swarm intelligence of artificial bees. Cardiac images are processed using the VGG network, which has been shown to be highly effective in image classification, while the Bi LSTM is optimized for processing time series data from medical sensors, such as heart rates and blood sugar levels. The selected characteristics are then used in the proposed VGG 16 model before being sent to Bi-LSTM for further processing and abnormality detection. The VGG consists of 16 layers, all of which are blocks of 2D Convolution and Max Pooling layers. The ABC method was created as a result of research into intelligent behavior and is now widely used in areas such as problem solving, categorization, and optimization. The ABC algorithm is used to the unified model, which results in improved adaptability, speed of convergence, and robustness. To better forecast cardiovascular diseases, this research presents an Integrated VGG16 model with Bi-LSTM model with ABC optimization (VGG-Bi-LSTM-ABC) to predict the cardiovascular disorders. When compared to the standard model, the proposed model's ability to detect disorder is much better. Preliminary results from a carefully selected dataset of DM patients show that the integrated model outperforms state-of-the-art approaches in key measures, further demonstrating the promise of Artificial Intelligence (AI)-driven advances in medical diagnosis

Internet of Things (IoT) Adoption in Higher Education Institutions: An Empirical Study in Saudi Arabia Universities

The Internet of Things (IoT) may offer many advantages to academic institutions, but its adoption, like other technologies, may also result in unanticipated risks and the necessity of significant organizational adjustments. This study examines the adoption of IoT by Saudi public and private universities. It targets the students and teachers to measure their intentions and actual behaviors to adopt IoT in academic research.  An exhaustive literature review is necessary to create the research hypotheses and classify the anticipated benefits and risks of the Internet of Things (IoT).  For the purpose of gaining an understanding of the relationships between university and technology, the study offers a theoretical framework by developing research hypotheses. The study used a quantitative research design by administering the survey questionnaires among the students and teachers of 7 public and private universities in Saudi Arabia. The study received 338 filled responses from the survey questionnaires.  The findings showed that perceived usefulness and ease of use significantly and positively influence the intention to adopt IoT. Additionally, perceived ease of use significantly and positively influences perceived usefulness. Finally, the study found that the intention to adopt IoT significantly and positively influences actual user behavior to adopt IoT in academic research.  The study recommends that the internet of things (IoT) may then provide universities with a multitude of benefits. It is necessary to make modifications to the organization, its procedures, and its systems to cultivate capabilities and make sure that IoT is compatible with the objectives of academic institution

Retracted

Retracte

E-Commerce Dynamics: Identifying the Behavioral Triggers in Online Shopping Among College Students

This research investigates the influence of marketing stimuli, website design, and emotional factors on impulsive and intentional online shopping behaviors among college students. Key findings indicate that discounts and promotions serve as the most significant triggers for both impulse and intentional purchases, while product recommendations, although impactful, demonstrate a lesser influence. Positive emotions mediate the relationship between discounts and impulse purchases, emphasizing the critical role of emotional engagement. Website design elements, such as visual appeal and ease of navigation, show minimal direct impact on purchase behavior. A cluster analysis revealed two consumer segments: one responsive to aggressive marketing and another preferring personalized strategies. These insights underline the importance of combining promotions, personalized recommendations, and emotionally engaging marketing tactics to effectively target diverse consumer preferences and enhance sales outcomes

Maturity in IT Monitoring: Enhancing Enterprise Preparedness for Critical Incidents

In today's complex enterprise IT environments, the true measure of an organization's preparedness for critical incidents lies in the maturity of its IT monitoring capabilities. This maturity directly dictates how effectively IT teams can detect, navigate, and resolve incidents, ultimately minimizing downtime and business impact. High Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) IT problems are directly linked to significant business losses, with IT downtime costing businesses over $100,000 per hour, and high-impact outages frequently exceeding$1 million per hour, sometimes lasting for days [5, 6, 7]. This white paper delves into the dual pillars of IT monitoring maturity: proactive monitoring with actionable alerting and comprehensive visibility for deep investigation and root cause analysis. We will explore how the proliferation of alert noise can severely impede incident triage, leading to significant delays and extended MTTD. A mature monitoring practice emphasizes the generation of critical, high-fidelity alerts that truly matter. Beyond alerts, effective incident response hinges on holistic visibility across all IT layers—network, application, infrastructure, end-user, and logs—ensuring real-time data capture and historical storage for context to drastically reduce MTTR. Through a detailed use case of high CPU utilization on a server, we will illustrate the rigorous process of problem qualification and the multi-faceted investigation required to uncover root causes. This involves correlating data from diverse dependencies, from network traffic and application transactions to server health metrics and logs. The paper argues that true problem resolution aims for long-term fixes, moving beyond superficial adjustments to address underlying issues and build enduring IT resilience. Achieving IT monitoring maturity is not just about tools, but about establishing processes and data-driven insights that empower IT teams to fix problems faster and more effectively than ever before

Federated Learning a Collaborative Machine Learning Across Countries with Data Privacy

With growing importance of data in shaping policies, economic strategies, and healthcare systems, securing citizens data has become a critical issue for national governments. At the same time, the potential benefits of large-scale collaborative machine learning (ML) across countries are undeniable. Federated learning (FL) offers a unique solution to this dilemma by enabling the training of AI models across decentralized data sets without requiring data to be shared. This paper explores how different countries can use federated learning to contribute to collaborative machine learning while ensuring national data security. We examine the privacy-preserving mechanisms in FL, the technical challenges, and propose a framework for cross-country collaboration on a global scale.

Heart Disease Prediction using Integrated Technology of XGBoost, Random Forest and Multi-Layer Perceptron

Cardiovascular disease remains a leading cause of death worldwide, requiring prompt and accurate diagnosis to minimize patient mortality rates. More recent developments in artificial intelligence (AI) applications have demonstrated how to enhance prognostic performance and interpretability in clinical diagnosis. This research paper analyzes the application of machine and Deep Learning models for heart disease prediction by voting with a selection of models in order to develop a strong classifier. A weighted ensemble voting approach is employed and leverage is made from XGBoost, Random Forest, and Multi-Layer Perceptron (MLP) model strengths. Further, explainability is offered by SHapley Additive exPlanations (SHAP) to facilitate model decisions, allowing feature importance and decision-making insight. The proposed methodology is supported by established performance metrics, retaining clinical relevance. Results imply that AI-based approaches can achieve elevated predictive accuracy and interpretable diagnoses, informing the creation of automated cardiovascular risk stratification

Smart Irrigation System using Raspberry Pi

This project focuses on automated and manual irrigation in addition with plant disease detection and growth monitoring using image processing on a Raspberry Pi 3B+. By leveraging TensorFlow Lite and OpenCV, the system can analyze plant health and trigger appropriate irrigation actions. The aim is to design accurate agriculture system by reducing water wastage and improving crop monitoring