Comparative Study of Item-Based Collaborative Filtering Algorithms for Book Recommendation Systems

Authors: Shradha U. Lipane

Abstract: Book recommendation systems pivotal in enhancing user experience and engagement across digital platforms, facilitating personalized content discovery in a vast sea of information and choices. This paper presents a comprehensive study on the implementation and evaluation of item-based collaborative filtering algorithms for book recommendation systems. Two prominent algorithms, k-nearest neighbor (KNN) and singular value decomposition (SVD) are used with cosine similarity as a similarity metric. This study compares these algorithms in terms of their effectiveness in recommending books that are relevant to users. The KNN algorithm identifies the nearest neighbors of a given book based on user ratings, while SVD decomposes the user-item interaction matrix to capture latent features underlying the data. Both algorithms offer unique advantages and trade-offs, which are thoroughly analyzed in this study. The evaluation metrics include precision providing insights into the accuracy and effectiveness of the recommendation models. Additionally, the computational efficiency of each algorithm is assessed to understand its scalability in real-world applications.

Keywords: Item-based collaborative filtering algorithm, k-nearest neighbor, singular value decomposition, cosine similarity.

Enhancing Network Security: A Comprehensive Review of Deep Learning Models and Datasets for IDS

Authors: Padmapani P. Tribhuvan, Amrapali P. Tribhuvan

Abstract: Intrusion Detection Systems (IDS) are indispensable in safeguarding computer networks from increasingly diverse cyber threats. Traditional methods, while effective for known attacks, struggle with the detection of novel and sophisticated threats. Deep Learning (DL) models have emerged as promising to enhance IDS capabilities by automatically learning and extracting complex patterns from network data. This paper comprehensively reviews various DL models applied in IDS, examining their applications, datasets, strengths, challenges, and future research directions.

Keywords: Intrusion Detection System, Deep Learning, Convolutional Neural Network, Re-current Neural Network, Generative Adversarial Network, Long Short-Term Memory

Enhanced Facial Expression Recognition: A Convolutional Neural Network Approach

Authors: Sumit Sanjay Maske

Abstract:Within non-verbal communication, the identification of facial expressions stands as a significant and formidable task. The objective of a facial expression recognition system is to categorize real-time facial images into distinct emotional classes, encompassing Anger, Disgust, Fear, Happy, Sad, Surprise, and Neutral. Drawing inspiration from the advancements achieved in image recognition and classification through Convolutional Neural Networks (CNNs), this paper advocates for a CNN-based methodology to tackle the complexities of facial expression recognition. The model presented in this study harnesses various libraries, including OpenCV, Keras, and TensorFlow. Through the utilization of grayscale images from the Face Expression Recognition dataset on Kaggle, the model undergoes training with CNN architectures of varying depths. Notably, the proposed model attains an accuracy of 72.34% for familiar data and 60.54% for previously unseen data.

Keywords:Intrusion Detection System, Deep Learning, Convolutional Neural Network, Re-current Neural Network, Generative Adversarial Network, Long Short-Term Memory

Machine Learning Approaches for Fake News Detection on Social Media: A Review

Authors: Sapna I. Narwade

Abstract:Malaria continues to pose a significant global health challenge, necessitating accurate and timely diagnostic methods to enhance patient outcomes. Traditional microscopic techniques, though effective, are labor-intensive and reliant on expert interpretation. This study investigates the potential of deep learning-based approaches for automated malaria detection, evaluating the performance of MobileNetV2, NASNetMobile, Xception, and InceptionResNetV2. Comparative analysis reveals that Xception outperformed the other models, offering an optimal balance of accuracy and efficiency. This study provides a robust foundation for selecting the most suitable deep learning models for malaria diagnosis, particularly in resource-limited settings.

Keywords: Deep Learning, CNN, Malaria Detection, MobileNetV2, NASNetMobile, Xception, InceptionResNetV2, Automated Diagnosis

Deep Learning-Based Malaria Detection: A Comparative Study of CNN Architectures

Authors: Shweta Kharat

Abstract: Intrusion Detection Systems (IDS) are indispensable in safeguarding computer networks from increasingly diverse cyber threats. Traditional methods, while effective for known attacks, struggle with the detection of novel and sophisticated threats. Deep Learning (DL) models have emerged as promising to enhance IDS capabilities by automatically learning and extracting complex patterns from network data. This paper comprehensively reviews various DL models applied in IDS, examining their applications, datasets, strengths, challenges, and future research directions.

Keywords: Intrusion Detection System, Deep Learning, Convolutional Neural Network, Re-current Neural Network, Generative Adversarial Network, Long Short-Term Memory

📢 Call for Papers

We are now accepting submissions for Volume 1, Issue 2 (June 2025). Submit your manuscript today!