Convolutional Neural Network-Based Classification of Skin Lesions Using Dermoscopic Images

Skin cancer is one of the most common and deadliest diseases globally, in which early detection may help improve patient survival rates. In this paper, an automatic skin cancer classification framework based on deep learning using dermoscopic images is presented. Various convolutional neural network (CNN) models were trained and tested on an annotated skin lesion dataset, including MobileNet, EfficientNetB0, VGG16, ConvNet, and ResNet50. Metrics of the models were calculated by using Micro-averaged metrics to assess the general effectiveness for all the classes. ResNet50 obtained the best performance against all tested models with a micro-average accuracy of 97.75%, precision of 97.79%, recall of 97.75%, and F1 score of 97.76%. Our results indicate that the model enables accurate, consistent, and balanced classification of different skin lesion categories, including actinic keratoses, basal cell carcinoma, benign Keratosis-like lesions, dermatofibroma, and melanoma. For real-world utilization, the top performer, the ResNet50 model, was implemented in a Streamlit-based web application, which is designed to automatically predict skin diseases in dermoscopic images that were uploaded. Experimental results show that deep residual learning is effective for improving the classification performance of skin lesions, and it can become an assistive decision-making tool for dermatologists in early diagnosis and clinics

A Data-Informatics-Oriented CNN-Based Intelligent Model for Handwritten Mathematical Symbol Recognition

Handwritten mathematical symbol recognition remains a challenging problem due to variations in writing styles, stroke structures, and visual similarities among symbols, which often reduce classification accuracy. This study proposes a data-informatics-oriented convolutional neural network (CNN) model for robust recognition of handwritten mathematical symbols. The research adopts a supervised experimental design using a balanced dataset consisting of six mathematical symbol classes. A systematic preprocessing pipeline including image resizing, normalization, and structured dataset partitioning is implemented to ensure data consistency and improve feature learning. The CNN model is implemented in MATLAB and optimized using stochastic gradient descent with momentum. Model performance is evaluated using confusion matrix–based metrics, including accuracy, precision, recall, and F1-score, along with computational time analysis. Experimental results demonstrate stable performance across multiple experimental runs, achieving an average accuracy of 97.08%, precision of 97.10%, recall of 97.08%, and F1-score of 97.07%. Confusion matrix analysis indicates that most handwritten symbols are correctly classified, with only minor misclassifications occurring among visually similar operators. These results confirm the effectiveness of integrating data informatics principles with CNN-based feature learning for handwritten mathematical symbol recognition. The proposed framework provides a reliable foundation for intelligent systems supporting digital education, automated assessment, and mathematical document digitization

Multi-Agent AI Systems for Autonomous and Context-Aware Data Orchestration in Hybrid Cloud Platforms

Hybrid cloud platforms face challenges in data orchestration due to dynamic resource allocation and workload changes. The framework uses multiple reinforcement learning agents equipped with context-awareness to autonomously manage data orchestration tasks. This investigation aims to develop an artificial intelligence (AI)-based data orchestration model using a Flexible Binary Spider Wasp Algorithm-enriched Double Deep Q-Network with Markov Decision Process (FBSWA-DDQN-MDP) to autonomously manage and optimize data placement, migration, and processing in hybrid cloud platforms. Data is collected from simulated hybrid cloud environments with varying workloads and resource availability. To ensure the dataset is prepared for modeling tasks, it has been preprocessed to eliminate missing values, normalize continuous features, and encode categorical variables. Principal Component Analysis (PCA) was used for feature extraction to improve computational efficiency. Using Python, simulations showed that the FBSWA-DDQN-MDP model outperformed traditional techniques with average energy consumption (AEC) (17.0 J), and average renting charge (ARC) (0.0006 cost) obtained at 1.8 Weight ω₂, normalized reward mean and standard deviation (SD) (0.97±0.01) values achieved at 1800 no of training episodes with adaptive response times under dynamic workloads. The proposed multi-agent AI system significantly improves data orchestration in hybrid cloud environments

Wavelet-Based Intelligent Framework for Network Traffic Anomaly Detection in IoT Embedded Systems

The rapid growth of the Internet of Things (IoT) and embedded systems has increased the vulnerability of network infrastructures to cyber-attacks, necessitating efficient real-time anomaly detection methods. In this study, we introduce a primary IoT network traffic dataset generated through controlled simulation of normal and malicious behaviors. Both time-domain features, including packet size, inter-arrival time, protocol type, and TCP flags, and frequency-domain features, such as spectral entropy and band energy derived via wavelet transform, were extracted to capture comprehensive traffic characteristics. These features were used to train a hybrid deep learning model, the Adaptive Differential Evolution Weighted Deep Belief Network (ADE-WDBN), which combines deep hierarchical feature learning with evolutionary weight optimization to enhance detection accuracy and computational efficiency. Experimental results demonstrate that ADE-WDBN outperforms traditional machine learning models and conventional deep learning approaches, achieving an accuracy of 98.37%, precision of 97.65%, recall of 98.02%, and F1-score of 97.83%. The low variability in performance across cross-validation folds indicates the model's robustness and generalizability. This research contributes a novel IoT traffic dataset and a cost-effective, adaptive anomaly detection framework capable of detecting subtle anomalies

Recruitment of research lecturers in Madagascar by a multicriteria group decision support system

The recruitment of permanent Lecturers for higher education is a major challenge for all universities. Given the absence of defined hiring criteria, this article will suggest some and support the recruitment committees at the Ministry of Higher Education in Madagascar. Three methods will be implemented in a decision support system. Each recruitment committee member will assess the importance of each criterion. The Rank Sum weight method is used to determine the weightings of the criteria. The Combined Compromise for Ideal Solution (CoCoFISo) method ranks the candidates. The Minimum of rank (MIRA) method will aggregate the several rankings of candidates. Three fundamental criteria were selected: experience in university teaching, time elapsed since obtaining the teacher's opinion, and number of research publications. These criteria were assessed according to their importance by six members of the recruitment committee. The weights assigned to the criteria vary from one criterion to another and from one decision-maker to another. Applying these weights to the CoCoFISo method resulted in six rankings of the candidates. The rankings of the candidates varied from one decision-maker to another, except for the candidate in first place and the candidate in last place. The MIRA method successfully merged these six rankings into a final ranking for each candidate. The Kendall rank correlation coefficient between the MIRA method and the six rankings obtained using the CoCoFISo method varies between 0.80 and 0.97, thus confirming the validity of the rankings. This new technique will solve the challenge of hiring temporary lecturers as permanent lecturers in Madagascar universities, thanks to the transparency of the process, and will assist the Ministry of Higher Education in its decision-making

Integrating Federated Transfer Learning for Secure Multi-Tenant Data Management in Decentralized Cloud Infrastructures

Ensuring secure multi-tenant data management while enabling cross-domain knowledge sharing is challenging in decentralized cloud infrastructures. Traditional centralized learning methods pose risks like data leakage and non-compliance with privacy regulations. To address these concerns, this research integrates Federated Transfer Learning (FTL) for secure and efficient multi-tenant data management. The proposed approach employs Federated Learning (FL) to enable collaborative model training while keeping raw data localized, preserving privacy. Additionally, BERT-based transfer learning improves knowledge sharing by adapting pre-trained models to tenant-specific tasks, enhancing efficiency and reducing computational overhead. A tenant-aware security mechanism dynamically assesses trust levels to ensure secure workload allocation and mitigate risks. Furthermore, a decentralized aggregation strategy enhances data privacy and prevents single-point failures, improving system robustness. The framework was evaluated using real-world datasets, assessing privacy, adaptability, communication overhead, and computational efficiency. Experimental results demonstrate that FTL-driven decentralized architectures achieve low latency (6ms), high throughput (850000 requests/sec at 10000 tenants), better utilization of resources (83%), and good security compliance (9.5/10). Consistency models also come with a 50% overhead for strong consistency, which shows the trade-offs involved in ensuring data consistency. The results confirm the proposed model as a scalable, efficient, and privacy-preserving solution for multi-tenant cloud environments

An Improved Web-Based Weather Information Retrieval Application

The Improved Web-Based Weather Information Retrieval Application was implemented to address Nigeria's challenges in weather information retrieval, including limited data collection and accessibility issues for diverse linguistic groups. The system integrates advanced forecasting models, real-time updates, caching mechanisms, and multilingual support for Hausa, Yoruba, and Igbo, ensuring inclusivity and accessibility, particularly for rural users. With a user-friendly interface, the application caters for users with varying technical expertise and supports critical sectors like agriculture and disaster management. The results of the system evaluation revealed that the existing system took a time of 1.774s at the speed of 8.719s to retrieve relevant weather information, while the proposed system took a lesser time of 0.753s at a faster speed of 3.929s to retrieve relevant weather information. This result shows significant improvements over the existing system, which lacked caching mechanisms and multilingual support, resulting in slower data retrieval and limited accessibility. It also demonstrated its effectiveness in enhancing decision-making, climate resilience, and disaster preparedness

Deep Learning based Seasonality and Trend Detection in Sales Forecasting

Sales forecasting is essential for business planning, as it aids inventory management, marketing, and decision-making.  Deep Learning combined with time-series analysis boosts prediction accuracy by capturing intricate temporal patterns.  Precise sales forecasting remains difficult because of trends, seasonality, and noise.  Previous techniques have issues with feature extraction and sequential dependencies, resulting in suboptimal efficiency.  This study aims to develop a Hybrid Deep Learning (HDL) technique that combines the benefits of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve sales prediction accuracy.  The primary emphasis is on combining feature extraction and temporal sequence learning to address the shortcomings of conventional methods.  The proposed HDL framework prepares a sales dataset for time-series evaluation using a structured workflow that includes data exploration, preprocessing, and aggregation.  To better comprehend the fundamental patterns, seasonal decomposition and autocorrelation analyses are used.  The sliding window method is used to produce sequential data, which is then split into training and testing sets.  Three predictive models—CNN, LSTM, and a hybrid CNN-LSTM—are built and trained using hyperparameter tuning.  The models are evaluated using performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).  Experimental results demonstrate that the proposed HDL surpasses CNN and LSTM with the lowest RMSE (2171.38), MAE (1219.79), and MAPE (538.18).  The HDL technique combines CNN and LSTM to enhance sales prediction accuracy by capturing patterns and seasonality for better demand prediction and business evaluation

Malicious URL detection using machine learning techniques

With numerous new websites being created every day, it's getting increasingly challenging to tell which ones are safe and which could be dangerous. These websites frequently gather sensitive user data that may be hacked in the absence of proper cybersecurity safeguards, such as the effective identification and categorization of dangerous URLs. In order to improve cybersecurity, this study attempts to create models based on machine learning algorithms for the effective detection and categorization of harmful URLs. In this regard, our proposal uses decision trees, logistic regression, support vector machines, and Naive Bayes to reliably categorize dangerous URLs. To improve classification efficiency, we have integrated hyper-parameter tuning using the Grid Search technique, optimizing model performance for more accurate and reliable results. The results demonstrate the effectiveness of Naive Bayes in achieving high accuracy (91.9%) and reliable performance in detecting malicious URLs. Implementation as a web service of the study provides evidence of the practicality and natural fit into more generalized security frameworks. Ultimately, our approach significantly enhances the detection of unsafe URLs, offering a robust solution to address the growing challenges in cybersecurity

Evaluation Metrics and Optimization Strategies for Routing Protocols in Resource-Constrained Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are indispensable for current applications, such as smart cities, industrial automation and environment monitoring. However, the performance of these networks is heavily dependent on the routing protocols used, especially given the strict limitations on energy, memory, and processing power in sensor nodes. This study presents a detailed evaluation of routing protocols designed for resource-constrained WSN nodes with a focus on energy efficiency, computation overhead, communication performance, scalability and security. A comparable study for the most popular routing protocols such as LEACH, AODV, DSDV, PEGASIS, and GPSR was performed to highlight the compatibility of such protocols with variety of WSN applications. Various optimization techniques to enhance the efficiency of the protocol are also introduced based on adaptive duty cycles, hierarchical clustering, hybrid routing paradigms, and lightweight security mechanisms. The vision and insights of this paper are to offer a good and well-organized ground for enabling and refining the selection of routing protocols for WSN implementation in the complex world of reality