http://103.99.128.19:8080/xmlui/handle/123456789/34 All Digital Collections of CSE Sun, 13 Sep 2026 19:03:16 GMT 2026-09-13T19:03:16Z http://103.99.128.19:8080/xmlui/handle/123456789/587 AVaTER: A Multimodal Approach of Recognizing Emotion using Cross-modal Attention Technique Das, Avishek; ID:, 21MCSE001P Multimodal emotion classification involves the analysis and identification of human emo tions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accu racy and robustness of emotion recognition systems. Audio data, for example, captures vocal tone and pitch, which are crucial for detecting emotions like anger or joy, while video data provides visual cues such as facial expressions and body language. Text data, often extracted from spoken words or written content, adds context and semantic depth to the emotion analysis. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we introduced a novel multimodal Bangla dataset named MAViT-Bangla (Multimodal Audio Video Text Bangla dataset), which consists of 1002 samples incorporating audio, video, and text modalities. This dataset includes emotional categories such as anger, fear, joy, and sadness, providing a rich resource for emotion recognition studies in the Bangla language. Each sample in MAViT-Bangla was meticulously annotated to ensure high-quality labels, making it a valuable asset for researchers working in this domain. Moreover, we developed a framework for emotion recognition that utilizes a cross-modal attention mechanism among unimodal features. This mechanism facilitates the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The proposed approach demonstrated its effectiveness by achieving an F1 score of 0.64, showcasing significant improvement over unimodal methods. This indicates that integrating multiple modalities through a cross-modal attention mechanism can substantially enhance the performance of emotion recognition systems, especially in the context of the Bangla language, where resources have historically been limited. The MAViT-Bangla dataset and our framework thus represent significant advancements in the field of multimodal emotion classification. A Master of Science (M.Sc) Thesis in Computer Science & Engineering (CSE) Department at Chittagong University of Engineering and Technology (CUET). Mon, 02 Jun 2025 00:00:00 GMT http://103.99.128.19:8080/xmlui/handle/123456789/587 2025-06-02T00:00:00Z http://103.99.128.19:8080/xmlui/handle/123456789/572 PDF Malware Detection Using Ensemble Learning Hossain, G.M. Sakhawat; ID:, 19MCSE005P Since Portable Document Format (PDF) is one of the most common file types, scammers put damaging code into people’s PDF files to get into their computers. Standard solutions and methods for identifying PDF malware are often insufficient to stop it completely. This is because PDF malware is very flexible and does not rely on a single set of traits. This work mainly aims to identify PDF malware quickly and effectively so that the problems can be addressed. To achieve the objective, initially various benchmark datasets were combined to generate an extensive dataset consisting of 15958 PDF samples, which considers the many types of behaviours exhibited by the samples, including non-malevolent, fraudulent, and deceptive behaviours. Three widely recognized PDF analysis tools (PDF-PARSER, PDFINFO, and PDFiD ) were employed to extract notable attributes from the PDF samples in the recently compiled merged dataset. Additionally, several types of derivations of traits were developed that have been shown through experimentation to help diagnose PDF malware. A technique was devised for constructing a highly effective and comprehensible set of characteristics by conducting a thorough empirical analysis of the retrieved and deduced features. Various standard machine learning models were examined which showed that the Random Forest classifier when using the final feature set, achieved an increase in accuracy of approximately 2%. In addition, the model’s explainability was showcased through the creation of a decision tree that produces rules that can be easily understood by humans. Finally, a comparative analysis was conducted with prior research that helped to highlight several significant findings. A Master of Engineering (M.Engg) Thesis in Computer Science and Engineering(CSE) Department at Chittagong University of Engineering and Technology (CUET). Wed, 25 Sep 2024 00:00:00 GMT http://103.99.128.19:8080/xmlui/handle/123456789/572 2024-09-25T00:00:00Z http://103.99.128.19:8080/xmlui/handle/123456789/555 Hybrid Caching with a Dynamic Forwarding Strategy in ICN Dey, Ashim In this era of huge data traffic, the information-centric network (ICN) is a switch ing strategy from conventional host-centric communication to a new data-centric one. In-network caching an integral part of ICN can reduce the shortcomings of today’s location-based internet paradigm. As ICN has limited caching ca pability, the decision of optimal routing and content placement are the major challenges. In this study, we suggest a hybrid caching with a dynamic forward ing (HCDF) strategy which takes advantage of the off-path hash routing scheme and on-path caching scheme. To facilitate both caching schemes, a fraction of the caching space is allotted in each cache node for local caching. At the user-end segment, an estimation-based content placement (ECP) policy is designed using content popularity state and cache node position. This policy helps to cache pop ular content near the consumer’s location using the shared cache space. At the server-end segment, the HCDF scheme dynamically selects the most appropriate routing strategy among three strategies (symmetric, asymmetric, and multicast) based on hop count and path stretch. To demonstrate the effectiveness of HCDF, extensive simulation on real-world internet topologies has been carried out. From our investigation, it is observed that the suggested approach exhibits an impres sive performance in terms of cache hit, latency, and average link load compared to other standard hash routing and well-known on-path caching schemes.ICN; Hash routing; Hybrid caching; Content placement; Caching strategy A Master of Science (M.Sc) Thesis in Computer Science and Engineering (CSE) Department at Chittagong University of Engineering and Technology (CUET). Sun, 09 Feb 2025 00:00:00 GMT http://103.99.128.19:8080/xmlui/handle/123456789/555 2025-02-09T00:00:00Z http://103.99.128.19:8080/xmlui/handle/123456789/550 Developing an Automated Framework for Detecting Check-worthiness of Bangla Claims Rahman, Md. Rashadur The evolution of technology is swiftly reshaping media trends, particularly with the mainstream media’s migration to online platforms. The rapid proliferation of misinformation through digital platforms and social media has become a critical challenge in today’s information-rich society. Despite the crucial role of fact checking in combating misinformation, traditional manual approaches are time intensive and insufficient to address the sheer volume of content. Automated claim detection systems serve as a critical first step in fact-checking by identify ing potentially falsifiable claims within the text, enabling quicker response times and reducing the workload for human fact-checkers. While research on claim de tection has been conducted for resource-rich languages like English, this domain remains underexplored for resource-constrained languages such as Bangla. In this study, we introduce the first-ever multiclass check-worthy claim detection dataset for Bangla named CheckBanC, comprising 10,023 sentences annotated into three categories. To address the unique challenges of Bangla claim detection, we pro pose a novel hybrid feature fusion model that combines contextual embeddings from DistilBERT with statistical bigram features. This approach captures both local syntactic patterns and global semantic relationships, significantly enhanc ing the model’s ability to identify check-worthy claims. Experimental results demonstrate the superiority of our method, achieving a new state-of-the-art per formance on the CheckBanC dataset. It outperforms various machine learning, deep learning, and transformer-based baselines, achieving a weighted F1-score of 0.84, surpassing other existing state-of-the-art approaches in claim detection. Our findings establish a strong foundation for future research in automated claim detection for Bangla, facilitating fact-checking efforts and curbing misinformation in resource-constrained languages. A Master of Science (M.Sc) Thesis in Computer Science and Engineering Department at Chittagong University of Engineering and Technology (CUET). Wed, 05 Feb 2025 00:00:00 GMT http://103.99.128.19:8080/xmlui/handle/123456789/550 2025-02-05T00:00:00Z