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All Journal Kwaghe International Journal of Sciences and Technology Kwaghe International Journal of Engineering and Information Technology
Hambali Moshood Abiola
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Survey of Finger Knuckle Print Recognition and Authentication Umar Abdullahi; Hambali Moshood Abiola
Kwaghe International Journal of Sciences and Technology Vol 1 No 1 (2024): Kwaghe International Journal of Sciences and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijst.v1i1.3611

Abstract

Background: Finger knuckle (FK) has gained significant attention as a biometric characteristic in recent years. Its unique features, such as visible lines, wrinkles, and ridges on the external surface of finger knuckles, make it an economically viable option for human identification. FK serves as the foundation for many biometric systems. Aim: This report presents a comprehensive analysis of relevant FK research. The typical FK identification system consists of four steps: image acquisition, image preprocessing, feature extraction, and matching. Various methods have been employed at each stage in this research. Result: The paper highlight state-of-art methods utilized for the recognition of FK.
Tiktok Through AI Eyes: A Deep Learning Approach to Sentiment Analysis Hambali Moshood Abiola; Ayo Iyanuoluwa; Akinyemi Adesina A.; Adamu Muhammed Gadafi; Ashraf Ishaq
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i2.5485

Abstract

Background: The rapid growth of social media has transformed communication, with TikTok standing out among younger users for its short-form videos. Understanding user sentiment on these platforms is key to analyzing public opinion, trends, and engagement. Aim: This study explores sentiment analysis of TikTok user reviews using deep learning approaches, specifically Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) and Deep Belief Networks (DBN). With over 144,000 reviews collected from Google Play and Apple App stores, the dataset was preprocessed using techniques such as lemmatization, tokenization, and GloVe word embeddings. The reviews were then classified into positive and negative sentiments. Both models were trained and evaluated based on metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Result: Experimental results revealed that the RNN-LSTM model outperformed the DBN, achieving an accuracy of 81.99% and an AUC of 0.8874, compared to DBN's 78.53% accuracy and 0.8577 AUC. The findings demonstrate the effectiveness of deep learning—particularly LSTM—in capturing sentiment from noisy, user-generated content on platforms like TikTok. This work contributes to the growing field of AI-driven sentiment analysis and provides a foundation for future improvements through hybrid or multimodal approaches.
Co-Authors Adamu Muhammed Gadafi Akinyemi Adesina A. Ashraf Ishaq Ayo Iyanuoluwa Umar Abdullahi