International Journal of Computer Engineering and Data Science (IJCEDS)

Evaluating and Optimizing CNN–Transformer Architectures for Musculoskeletal Disease Classification

Moulay Youssef Ichahane — 2025-10-06

This study examines the impact of dataset dimensionality on deep learning performance in musculoskeletal disease detection, focusing on osteoporosis and rheumatoid arthritis. Using over 200,000 annotated X-ray, DXA, and MRI images, the performance of Vision Transformer (ViT), ConvNeXt, and Swin Transformer models was systematically evaluated in terms of scalability, robustness, and multi-modal integration. Results demonstrate that increasing dataset scale significantly enhances model generalization, with Swin Transformer achieving the best performance (AUC = 0.94, p < 0.001). These findings underscore the critical role of self-attention mechanisms and model scaling strategies in medical image classification, providing new benchmarks for dataset requirements and guiding the development of more reliable AI-driven diagnostic systems. Furthermore, the study emphasizes the necessity of large, diverse datasets to mitigate overfitting and improve real-world applicability. It also highlights the potential of hybrid architectures for integrating multi-source medical data. Overall, this research contributes to advancing explainable and scalable AI solutions for musculoskeletal imaging in clinical practice.

Recyclitix: Waste Classification with CNN - Mobile Application

Hammam Elkentaoui — 2025-09-30

Recylitix is an innovative mobile application that improves waste sorting efficiency through AI-powered image classification and contextual guidance. Leveraging computer vision and deep learning models using TensorFlow Lite, Recyclitix enables users to accurately identify waste types and receive localized recycling recommendations. This intelligent sorting mechanism reduces classification errors, optimizing recycling processes and minimizing the environmental impact of poorly sorted waste. The platform is built on a modern architecture that integrates a Spring Boot backend with a native Android application. Communication between components is facilitated by Retrofit for efficient API interaction. By combining robust machine learning with a user-centric mobile interface, Recylitix bridges the gap between sustainable practices and everyday behavior. It enables individuals, municipalities and waste management players to adopt smarter, more responsible recycling habits.

AI-VoiceTherapy: An Automated Platform for Voice Rehabilitation Using Artificial Intelligence

Nisrine Lachguer — 2025-09-30

AI-VoiceTherapy is a mobile platform that leverages artificial intelligence to democratize access to speech therapy. The system uses OpenAI's Whisper model to automatically detect and analyze speech disorders from voice recordings, including stuttering, dysphasia, dysarthria, and apraxia. Based on this analysis, the platform generates personalized therapy exercises tailored to the specific disorder and its severity. The three-tier architecture comprises an Android mobile application, a Spring Boot REST API, and a MySQL database. Key functionalities include automated speech analysis, personalized therapy generation, comprehensive progress tracking, and professional integration with speech-language pathologists. This innovation addresses geographical, economic, and resource barriers to traditional speech therapy, offering an accessible and scalable solution for millions affected by speech disorders worldwide.

A GIS-Based Geoportal for Land Management and Societal Acceptability in Mining: Case Study of MANAGEM Group, Morocco

Maroua Chattat — 2025-09-30

The complexity of land management coupled with societal considerations in Morocco's mining sector grows over time. Challenges such as land security, traceability of mining footprints, and social acceptability in sensitive territories necessitate integrated digital solutions. To allow societal considerations to be included in the land management for mining sites, this paper develops a bimodal solution. First, a web geoportal based on Geographic Information System (GIS) named MineMaps for centralised land data visualisation and management was developed. Then, a dynamic digital tool for structured societal actions’ planning aligned with Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) standards was elaborated. The case study is the Managem Group, a key player in the mining industry in Morocco. The geoportal enables interactive visualisation of mining sites and digitises land transactions, while the societal actions tool organises actions, stakeholder data, and impact indicators. Results demonstrate enhanced land traceability, reduced legal risks, and improved community engagement, fostering inclusive and sustainable territorial governance.

Mooditor: An AI-Powered Mobile Assistant for Real-Time, Emotion-Aware Mental-Health Support

Laila HAMZA — 2025-06-30

Mooditor is a pioneering mobile and web-based application designed to enhance mental health monitoring through artificial intelligence. By integrating real-time emotion detection via facial expression analysis and a Rasa-powered chatbot for therapeutic interactions, Mooditor provides a multi-modal approach to mental well-being. The system leverages computer vision and natural language processing (NLP) to assess psychological states, offering continuous monitoring and personalized support. Comprehensive tools, including mood tracking, statistical analysis, and conversation history, enable users and healthcare professionals to track emotional trends effectively. Our evaluation demonstrates exceptional performance, with the emotion detection model achieving a macro average precision, recall, and F1-score of 0.9998 across 953 instances. Mooditor’s modular architecture supports future enhancements, such as advanced emotion detection algorithms and integration with professional mental health services. This work addresses critical challenges in mental health accessibility and early intervention, contributing to the advancement of digital mental health care.

Keratoconus Classification Using Multimodal Imaging Strategy

Mustapha AATILA — 2025-06-30

Data fusion improves the accuracy and robustness of diagnostic models by combining different types of information. This study presents a multimodal framework for keratoconus classification. It uses numeric and textual features from Pentacam reports, extracted with OCR. These are combined with corneal topographic images processed by a dual-branch deep neural network. The method was tested on 2,924 labeled Pentacam scans. Of these, 1,900 were used for training and 1,024 for testing. Scans were labeled as normal, suspicious, or keratoconus. Results show that combining image and text features improves classification. Deep learning accuracy rose from 96.78% to 98.34%. SVM improved from 93.35% to 95.60%. LDA increased from 92.85% to 94.80%, and KNN from 90.50% to 93.94%. These gains, up to 1.56% for deep learning and 3.44% for KNN, show the value of multimodal data for more accurate keratoconus diagnosis.

FitnityAI: Personalized Fitness Goal Tracking Assistant with AI

Soukaina DADI — 2025-06-30

With growing interest in health and wellness, there is a rising demand for intelligent tools that support personalized fitness routines. That’s what inspired FitnityAI, a fresh approach to mobile fitness tracking that uses generative AI to deliver customized guidance tailored to each user’s needs and progress. The application integrates key technologies, including the Gemini AI engine, a robust Spring Boot backend, and a user-friendly Android interface, to build a dynamic and adaptive fitness experience. Its standout feature is an AI-powered conversational assistant capable of interpreting user goals, activity patterns, and preferences to provide actionable, real-time fitness recommendations. FitnityAI was developed to support users in building sustainable fitness habits and to raise the bar for how mobile apps can use large language models to transform personal health management.

CityEcoScout: A Platform for Exploring Sustainable Locations Worldwide

Bader Eddine BENHIRT — 2025-05-06

As people everywhere are trying to be more environmentally conscious, there is an increasing public demand for practical tools that help make sustainable decisions in cities and towns. That's what inspired CityEcoScout a new take on mobile platforms, using AI to inform users about environmentally focused spots in local areas and further afield. The application integrates several technologies, including Google Maps, Street View, and Places APIs, with the revolutionary Gemini AI engine to create something incredibly useful. The killer feature of this app is designed to provide easy-to-digest sustainability information and allows virtual tours of green destinations before arrival. CityEcoScout was developed to assist locals and travelers with making environmental choices, setting a new standard for how location services can enhance sustainability in daily lives.

Fashion Recommendation Systems: From Single Items to Complete Outfits

Ilham KACHBAL — 2025-04-27

Fashion recommendation systems have evolved beyond traditional recommender systems to address the unique challenges of fashion retail and e-commerce. This paper presents a comprehensive categorization of these fashion recommendation systems, grouping them into four fundamental approaches: personalization-based, compatibility-based, context-based, and special applications. We examine how personalization-based approaches leverage user preferences, while compatibility-based methods address fashion coordination through visual and semantic matching. The paper also explores the progression from single-item recommendations to complete outfit generation, alongside the integration of contextual factors like climate and occasions. Additionally, special applications such as body-shape awareness and sustainable fashion demonstrate the expanding scope of the field. Through this categorization, the paper provides a structured framework for understanding current approaches and identifying promising directions for future research, offering valuable insights for both researchers and practitioners in fashion recommendation systems.

A Multimodal Approach to Breast-Lesion Classification Using Ultrasound and Patient Metadata

Amina ABOULMIRA — 2025-04-24

The diagnosis and prognosis of breast cancer have been greatly improved by incorporating machine learning methods, especially through medical imaging analysis as well as clinical information. In this study, the potential of deep learning models for breast lesion prognosis was explored by integrating imaging features with clinical data to enhance predictive accuracy. Clinical data were analyzed using multilayer perceptron (MLP) classifiers, XGBoost, and Random Forest, while several convolutional neural network (CNN) architectures, such as ResNet optimized with Adam, DenseNet with stochastic gradient descent (SGD), and EfficientNet with RMSprop, were evaluated. The integration of imaging-based features with clinical data was found to significantly improve model performance, enabling more accurate risk stratification and the development of individualized treatment strategies. The highest validation accuracy and area under the curve (AUC) were achieved by the most effective models, highlighting the advantages of a multimodal approach. Although the study was conducted on a relatively small dataset and faced challenges such as missing data, the results suggest that these methods hold considerable promise for implementation in clinical practice.

Healthcare Professional's Lifelong Learning Automation by Adapting Pedagogical Currents and Bloom's Taxonomy to Artificial Intelligence

Nadia HACHOUMI — 2025-02-18

Background: This study focuses on the integration of artificial intelligence (AI), pedagogical techniques and Bloom's taxonomy in health sciences education. AI plays a key role in this field, changing educational paradigms through personalized learning experiences. Methodology: The study examines how AI enables personalized educational progression based on individual needs, promoting continuous lifelong learning. It examines the potential of AI to provide rapid feedback on tasks and assessments, improving conceptual understanding. In addition, AI helps trainers discover learning trends through data analysis, and creates dynamic learning environments. Results: Research shows that AI-based education systems boost students' grasp of complicated subjects, problem-solving ability, and writing capabilities. Furthermore, AI's flexible capabilities enhance educational inclusion by tailoring learning approaches to various individual problems. Discussion: The findings highlight AI's transformative impact on health sciences education, stressing the transition from traditional models to adaptive, learner-centered approaches. AI's ability to accommodate different learning styles and facilitate continual skill development demonstrates its promise to transform professional education in the health sciences. Conclusion: Integrating AI into health sciences education not only improves learning outcomes but also fosters a culture of lifelong learning among students and practitioners. As AI advances, its integration with pedagogical frameworks such as Bloom's taxonomy opens up new possibilities for improving educational procedures and preparing future healthcare practitioners for dynamic professional challenges.

Comparative Study of Technical Provisioning Methods in Health Insurance: An Analytical Approach Using the Chain Ladder Method, the Mack Method and Generalized Linear Models

Ayyoub SAOUDI — 2025-02-18

This paper presents an in-depth analysis of technical reserving in health insurance, using three different methods: the Chain Ladder method (deterministic), the Mack stochastic method (stochastic) and Generalized Linear Models (GLM). The aim of this study is to compare these methods in terms of forecast accuracy, robustness and ability to consider the uncertainty inherent in health insurance claims. The scientific contribution of this research lies in the proposal of methods for improving technical reserving, thereby improving the financial management of health insurance companies.

Security Considerations in the Internet of Medical Things: COVID-19 IoMT Gadgets

Charaf Eddine AIT ZAOUIAT — 2023-10-19

The COVID-19 pandemic has underscored the significance of the Internet of Medical Things (IoMT) in healthcare. During this crisis, IoMT devices played a crucial role in remotely monitoring patients, tracking virus transmission, and supporting healthcare professionals. However, the increased adoption of IoMT devices has also raised concerns regarding security and privacy vulnerabilities. This paper presents a comprehensive analysis of the vulnerabilities found in IoMT devices designed and rapidly deployed during the pandemic, such as remote patient monitoring devices and telemedicine platforms. The study identifies common hardware, software, and communication vulnerabilities that pose potential threats to patient safety and the integrity and confidentiality of healthcare data. Furthermore, the paper examines proposed security mechanisms, including blockchain-based frameworks and lightweight authentication schemes, all of which aim to address these challenges. By raising awareness of these vulnerabilities and risks, this research aims to encourage the development of more secure and resilient IoMT devices in the future.

Artificial Neural Network model for Call Options Pricing Using Market Data

Georgios Rigopoulos — 2024-11-18

Accurate option pricing is of key importance for markets and traders. This work explores the feasibility of using artificial neural network model in call option pricing, using the traditional Black-Scholes model as a benchmark. We used a multilayer perceptron model trained to learn Black-Scholes function and tested in real option data from thirty-five S&P100 stocks. In our approach testing data is not oriented from the same distribution as training and this is a unique contribution to existing research. Findings demonstrate that artificial neural networks performs well in actual market data. Although further exploration and experimentation is required to reach required robustness and become less ad hoc and data sensitive, it is a promising approach and can play a substantial role in option pricing,.

Improving sustainable management through an AI-based strategic transition: A policy that promotes firms to use AI advancements in the Industry 4.0 era

Hanane ALLIOUI — 2024-11-18

Due to the severe effects of the Fourth Industrial Revolution and environmental disasters or subsequent pandemics, the digital infrastructures of businesses need drastic and constant changes. As a result, many companies are actively implementing innovative digital strategies to accelerate digital adjustments throughout the scale of their organizational structures. Since artificial intelligence (AI) has demonstrated its success in a wide range of fields, including both business processes and daily activities, it has gained extreme interest in corporate ecosystems. Business management techniques could be transformed by the integration of AI for more productivity, cost-effectiveness, and overall efficiency. AI is strategically incorporated into businesses to help them engage with their target consumers more effectively, giving them an edge over their digital competitors. Additionally, AI has the potential to revolutionize corporate operations, enabling the creation of novel ideas, the completion of complex tasks, and the acceleration of significant economic growth. To achieve thorough optimization, it is necessary to carefully adjust AI integration tactics to the unique requirements at each stage of development. In this study, we present a strong strategy to hasten the conception, alignment, and prioritizing of developing activities, supporting a smooth transition to more effective and sustainable management methods.

Measuring the readiness of Ecommerce Small and Medium Enterprises (SMEs) to use Artificial Intelligence: Example of Moroccan SMEs

Hanane ALLIOUI — 2024-03-15

success due to its increasing significance in facilitating large-scale data processing, producing insights, and automating repetitive processes. The implementation of AI technology has the potential to boost organizational effectiveness, save costs, and improve consumer experiences, resulting in increasing growth and competition. The adoption of AI in Small and Medium Enterprises (SMEs), especially in developing nations like Morocco, is still at a low level. This paper uses an expanded innovation-business ecosystem framework and human inventiveness to analyze the elements that influence AI adoption in Moroccan e-commerce SMEs. The study highlighted manager/ownership culture, monitoring environment, qualified advantages, maximal management support, and AI expertise as key predictors of AI adoption among Moroccan e-commerce SMEs. Although the study's sample size is small, additional research is required to investigate other issues that could hinder Moroccan e-commerce SMEs' adoption of AI and financial growth. The study's conclusions offer insightful information that might support e-commerce SMEs and the Moroccan government in spreading awareness of AI, a critical step towards technological advancement in a historically conservative corporate environment.

Advances in temperature control for microelectronic measurements

Malaoui Malaoui — 2023-10-18

This paper introduces an electronic temperature control system with the objective of delivering a functional prototype that is both cost-effective and capable of rapid and accurate performance. Intended for utilization in research laboratories for precise temperature control during electrical measurements, this device employs a 10-bit embedded system to implement the PID algorithm. The electronic schematics and results of this controller are discussed and compared to those of an earlier 8-bit controller and a standard laboratory regulator. Initial assessments focus on regulating the temperature of a photovoltaic junction to ascertain its intrinsic electrical characteristics. The results obtained vividly illustrate the extent and magnitude of the benefits conferred by this innovative device.

The governance of health information systems : a tool for improving the quality of care

fatima ezzahra salamate — 2024-02-03

Computerization of the care process is essential. It facilitates the traceability of care activity, which is the founding regulatory element of care safety, and the operational element of continuous quality improvement initiatives. This is why the implementation of traceability in the field now requires the development of responsive information systems based on information technology governance, and specifically the ITIL best practice framework. The deployment of ITIL will enable care processes to be mastered in real time, eliminating malfunctions and improving SIS performance.

Unleashing the Potential of AI: Investigating Cutting-Edge Technologies That Are Transforming Businesses

Hanane ALLIOUI — 2023-08-16

The integration of AI has ushered in a new era of enhanced reliability in digital offerings, optimization of supply chain processes, and real-time access to invaluable data and analytics. Companies stand to benefit significantly as they employ AI to reduce lead times, uncover fresh customer insights, revolutionize customer service standards, and deliver unparalleled personalized experiences. This paper strives for excellence in its quest to bridge the knowledge gap and facilitate the successful assimilation of AI into business planning. By conducting a rigorous literature analysis and synthesizing contemporary methodologies and frameworks, it brings to the fore the potential advantages, challenges, and untapped possibilities. Moreover, this study delves into future research prospects, empowering businesses with the requisite knowledge and strategies to harness the full potential of AI and achieve unparalleled success in the dynamic and competitive world of business.

Does gender affect the identification of road crash occurrences? An Overview and a comparative study

Soukaina EL FEROUALI — 2023-06-28

Around the world, traffic accidents are regarded as a large and significant cause of injury and death. Nearly 3,700 people are killed and over 1.3 million individuals lose their lives in collisions involving trucks, vehicles, buses, motorcycles, or pedestrians. This article tries to identify the important causes of traffic accidents involving both men and women as well as the methods that have been suggested and put into practice based on the literature study. In order to determine how gender impacts the frequency of traffic accidents, a survey and a comparative study were conducted in this work. According to the findings, the factors that have been studied for accident causes in urban areas include speed, age, and gender. On rural roads, speed has been recognized as the primary cause of collisions, particularly among men, while age and lack of experience have been noted as influencing factors in women's traffic incidents. Because machine learning models are effective at predicting crashes, they have been utilized in the majority of research