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All Journal TechComp Innovations: Journal of Computer Science and Technology Edu Spectrum: Journal of Multidimensional Education
Md. Safaet Hossain
University of Information Technology and Sciences
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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Journal : techcomp innovations journal of computer science and technology

 1 
AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems Md. Anas Mondol; Md. Ashaf Uddaula; Md. Safaet Hossain; Mst. Ayesha Siddika
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 1 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i1.59

Abstract

This study presents an advanced AI-powered framework to detect and prevent cyberbullying across diverse social media platforms using a multiclass classification approach. Addressing the growing complexity and linguistic diversity of online abuse, the research integrates various machine learning (RF, LR, SVM) and deep learning (Bi-LSTM, BERT) models trained on a balanced dataset covering bullying categories based on religion, age, ethnicity, gender, and neutral content. Data preprocessing, tokenization, feature extraction via TF-IDF and CountVectorizer, and class balancing using SMOTE were applied to enhance model accuracy. The proposed system further supports real-time detection through social media APIs, offering dynamic monitoring and intervention capabilities. Among the tested models, Random Forest and BERT achieved the highest classification performance with 94% accuracy. Despite its robust architecture, limitations include dependence on English-language datasets, exclusion of multimodal data (e.g., memes, audio), and API restrictions that challenge scalability. Future development will focus on incorporating vision-language models and optimizing the system for real-time, multilingual, and multimodal environments. This study contributes to digital safety efforts by proposing a scalable and adaptive detection system suitable for safeguarding users from evolving forms of online harassment.
TraceRoot: A Blockchain-Based Traceability Framework for Enhancing Transparency and Trust in Rice and Essential Goods Supply Chain Md. Safaet Hossain; Mohammad Shakibul Hasan Sakib; Md. Rayhan Ahmed Shis; Sakib Ahmed; Md. Fudail
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.192

Abstract

Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation
IoT-Enabled Smart Plant Ecosystem Integrating Real-Time Monitoring, Automated Irrigation, and Intelligent Growth Optimization Frameworks Md. Safaet Hossain; Israt Jahan; Jawata Afnan; Israt Sultana Tanny; Nashid Sultana Mim; Kayes Mahmood
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v3i1.198

Abstract

Urban plant care is increasingly important for sustainable living, but many users face inconsistent watering, insufficient care knowledge, unsuitable plant selection and delayed disease recognition. This study presents Easy Grow Plants, an integrated web and Internet of Things (IoT) ecosystem that connects plant care guidance, soil-moisture monitoring, automated watering, plant recommendation, image-based plant health assistance, marketplace functions, community interaction and plant exchange. The prototype was implemented using a React frontend, Django REST backend, SQLite database and an Arduino UNO R4 WiFi smart pot with a soil moisture sensor, relay module and DC water pump. Functional, interface, API, IoT connectivity, sensor calibration, watering control and LAN deployment tests were conducted. The results show that the core modules operated together as an integrated academic prototype. The system demonstrates a practical foundation for smart urban gardening, although cloud deployment, multi-device testing and stronger AI validation remain future improvements
Co-Authors Israt Jahan Israt Sultana Tanny Jawata Afnan Kayes Mahmood Md. Anas Mondol Md. Ashaf Uddaula Md. Fudail Md. Rayhan Ahmed Shis Mohammad Shakibul Hasan Sakib Mst. Ayesha Siddika Nashid Sultana Mim Sakib Ahmed