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Enhanced Alzheimer's Disease Classification: A Stacked Model Fusion with Brain MRI Imaging

Enhanced Alzheimer's Disease Classification: A Stacked Model...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Tekumudi Vivek Sai Surya Chaitanya R Annamalai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Alzheimer's disease (AD) is still a major global health issue that requires sophisticated staging and classification techniques. This paper introduces a novel approach to AD categorization, employing a stacked network architecture and feature extractions from InceptionV3, EfficientNetB0, and MobileNet models. The primary objective is to address the challenge of imbalanced datasets while minimizing the risk of overfitting. The proposed method utilizes brain MRI scans, extracting features and incorporating a Global Average Pooling (GAP) layer to improve the identification of critical features that correspond to AD phases. Combining the retrieved features results in a single set that helps with stage discrimination, followed by finishing via a SoftMax classifier. Model evaluation and validation are essential steps in determining the ideal model to minimize the risk of overfitting. A comprehensive evaluation is ensured by cross-validation (CV) using a 3-fold technique, which shows that the suggested Stacked Model has a better accuracy of 94.58%, closely followed by EfficientNetB0 at 90.70%. Both InceptionV3 and MobileNet exhibit competitive results, with accuracy rates of 89.46% and 87.60%, respectively. By presenting a strong approach to AD classification, this study advances the field of early detection and staging of AD by demonstrating the effectiveness of the Stacked network in identifying complex features that are indicative of different stages of AD progression within brain MRI datasets.

关键词： Deep learning Accuracy Magnetic resonance imaging Network architecture Feature extraction Brain modeling Reliability

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Leveraging Deep Learning for Detecting Toxicity in Online Comments

Leveraging Deep Learning for Detecting Toxicity in Online Co...

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International Conference on computing and Networking Technology (ICCNT)

作者： Reddy Kowshik Rayani Samhitha Tekula Subhash Kovid Vattigunta Naveen Kumar Kovi Kalakunnath Namitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Toxic comments are pervasive on online platforms, which makes it difficult to maintain positive dialogue and create a secure online space. People’s emotions and overall state of mental health start to seriously decline as a result of those toxic remarks. In this study, we examine how optimizers affect the training of three well-known RNN architectures for the toxic comment categorization task: LSTM, Bi-LSTM, and GRU. According to our research, the model’s performance is greatly influenced by the optimizer used; the Bi-LSTM model, which was trained using the ADAM optimizer, showed the greatest test accuracy of $\mathbf{9 5. 3 3 \%}$. Our results highlight the crucial role optimization techniques play in improving moderation systems’ effectiveness and advancing the creation of more potent mitigation strategies for online toxicity.

关键词： Training Deep learning Toxicology Accuracy Prevention and mitigation Mental health computer architecture Optimization Long short term memory

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Comparative Analysis of Machine Learning Models for Water Quality Prediction

Comparative Analysis of Machine Learning Models for Water Qu...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Bode Vamsi Krushna Sasikala D Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Water Quality plays an essential role in assessing environmental health, which has profound implications for both ecosystems and human well-being. In this work, various machine learning models including Logistic Regression, Decision Trees, Gaussian Naive Bayes, Random Forest, Support Vector Machine (SVM), and XGBoost were employed to analyze and predict water quality. The dataset used for the analysis encompasses diverse water quality parameters such as hardness, pH levels, solids, chloramines, turbidity, sulfate among others. Class imbalances in the dataset is addressed with Synthetic Minority Over-sampling Technique(SMOTE). The research findings highlight the significant improvement in classification accuracy of underrepresented data points due to SMOTE, with SVM and XGBoost achieving a training accuracy of around 78% and 75%, respectively. The study used K-fold cross-validation techniques and strategies to handle convergence warnings, enhancing the credibility and adaptability of the predictions. This study not only contributes to the growing body of knowledge in water quality analysis but also provides practical insights for researchers and practitioners seeking to employ machine learning approaches in environmental monitoring, water contamination. These findings highlight the critical role of machine learning in facilitating informed decision-making for long-term water management, ensuring the health and longevity of communities and ecosystems.

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Visio-Voice Transforming Images into Sound for the Visually Impaired

Visio-Voice Transforming Images into Sound for the Visually ...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Gogineni Ashrith Sai Kondareddy Balaji Reddy Kothuru Gurunadh Thanakanti Ganesh Madhav S. Santhanalakshmi Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The integration of deep learning and computer vision has led to groundbreaking innovations, notably in Image-to-Audio technologies. This transformative paradigm aims to provide comprehensible audio descriptions for visual content, offering increased independence and inclusivity for individuals with visual impairments. Rooted in intricate computational methodologies, this approach seamlessly translates visual data into auditory representations. The paper explores the technical aspects of Image-to-Audio conversion, emphasizing its potential applications in digital media, education, and everyday environments. Technology is seen as a societal catalyst, empowering individuals with visual impairments and fostering newfound self-sufficiency and autonomy.

关键词： Deep learning Training Technological innovation Visualization computer vision Communication systems Space technology

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Secure Authentication Scheme for Internet of Battlefield Things

Secure Authentication Scheme for Internet of Battlefield Thi...

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International Conference on computing and Networking Technology (ICCNT)

作者： Devu Pawan Ashutosh Shoaib Akhtar G Yeshwanth Reddy Hardik K Singh K Nimmy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Future military operations will increasingly depend on interconnected technology, using advanced embedded systems and AI to improve defense capabilities and technology. The Internet of Battlefield Things (IoBT) will link soldiers with smart battlefield equipment such as armor, radios, and weapons, reducing decision cycle times, improving tactical analytics, and delivering precise AI support where needed. Authentication plays an important role in IoBT, as confidential information generated from battlefield devices needs to be secured from enemies. We propose a novel authentication approach designed to address the particular constraints of the IoBT, utilizing fuzzy extractor and geometric secret sharing techniques. This approach overcomes the weaknesses caused by dynamic and hostile combat situations, in which standard authentication methods may fail. The proposed scheme is based on fingerprint biometrics and fuzzy extractors, enabling reliable authentication even when dealing with noisy biometric data. Moreover, geometric secret key sharing improves security by providing secure key establishment. Also, the suggested methodology is ideal for resource-sparse Internet of Things (IoT) devices which are commonly found in military situations. The effectiveness and robustness of the proposed authentication design are shown by exhaustive analysis employing Scyther tool and simulations, illustrating its ability to resist numerous security attacks prevalent in IoBT environments.

关键词： Military communication Protocols Weapons Authentication Fingerprint recognition Vectors Cryptography Internet of Things Artificial intelligence Standards

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Data Insights as Data: Quick Overview and Exploration of Automated Data Insights

Data Insights as Data: Quick Overview and Exploration of Aut...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Wu, Shangxuan Luan, Wendi Wang, Yong Zeng, Dan Shen, Qiaomu Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore Singapore School of Artificial Intelligence Sun Yat-Sen University Guangdong Zhuhai China

ISBN: (纸本)9798400713958

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews. © 2025 Copyright held by the owner/author(s).

关键词： Data Insights Glyph Multidimensional Data Visual Analytics

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Cancer Classification using Gene Expression Dataset to Detect Cancer

Cancer Classification using Gene Expression Dataset to Detec...

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International Conference on computing and Networking Technology (ICCNT)

作者： Y Nishanth G K Meghraj Akshat Kuttan Apurvanand Sahay Dept of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cancer classification plays a very important role in personalized medicine and targeted therapy. To classify the kinds of cancer based on the gene expression data, three different kinds of algorithms are applied in this paper: the greedy method, the Recursion Tree Method, and Hashing. The selection of these algorithms forms a large combination to address the complexity of the gene expression datasets in an attempt to increase the accuracy of cancer classification and minimize the computational time. The study evaluates the performance of each of the algorithms and also show how well they do in cancer classification through detailed experimentation and review. The Greedy Algorithm and Feature Hashing enable accuracy of 88% and 85%, respectively. Feature Hashing reduces the computational complexity significantly. Such findings bring out the trade-offs between accuracy and computational efficiency, giving insights that might be of importance for cancer detection and treatment methods.

关键词： Greedy algorithms Accuracy Reviews Scalability Precision medicine Robustness Classification algorithms Computational efficiency Gene expression Cancer

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An Integrated Mobile Applications for Enhancing Women’s Safety – A Comprehensive Approach

An Integrated Mobile Applications for Enhancing Women’s Saf...

引用

Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Ashwini K Sree Neha Perla Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Women's safety is a critical concern in today's society, necessitating innovative solutions that leverage technological advancements. This Proposed paper proposes two different applications, namely Womenify for the user especially women and Wopo for police which is combined with computer-aided dispatch (CAD) software to enhance women’s safety and improve police response time. This research paper lays the groundwork for a comprehensive technical solution and presents valuable insights for creating safer communities for women. The proposed Womenify application will enable users to scan their surroundings using GPS tracking, access crime data in required areas to know whether the place is safe or not, and can also trigger emergency alerts. On the police side, the Wopo application provides patrolling schedules and real-time notifications. Further, the developed app can also be integrated with CAD software, using real-time data, ensuring easy accessibility for illiterate or unemployed women, expanding features, incorporating advanced technologies like predictive policing, and collaborating with law enforcement agencies. The effectiveness and functionality of the proposed application are evaluated through a feedback-based evaluation process which yielded positive results, indicating their effectiveness in enhancing women's safety.

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BTVSL: A Novel Sentence-Level Annotated Dataset for Bangla Sign Language Translation

BTVSL: A Novel Sentence-Level Annotated Dataset for Bangla S...

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International Conference on Automatic Face and Gesture Recognition

作者： Iftekhar E Mahbub Zeeon Mir Mahathir Mohammad Muhammad Abdullah Adnan Department of Computer Science Bangladesh University of Engineering and Technology Kahlert School of Computing University of Utah

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Sign language, a vital communication tool for individuals with hearing impairments worldwide, is prominently utilized within the BangIa-speaking community. BangIa is recognized as the seventh most spoken language globally. However, research in BangIa Sign Language Recognition (SLR) - the process of translating symbols or words from images and videos - has been predominantly confined to controlled environments with limited samples and rudimentary symbol annotations, impeding its application in real-world scenarios. In contrast to previous studies, our research concentrates on SLR. It delves into the relatively unexplored territories of BangIa Sign Language Translation (SLT) and Sign Language Production (SLP), areas that have been largely overlooked due to dataset constraints. We introduce BTVSL, a comprehensive BangIa Sign Language dataset derived from the YouTube series ‘BTV Desh o Jonopoder Khobor'. This dataset, featuring 60 hours of news content in sign language delivered by professionals, represents the largest sentence-level dataset available for BangIa SLT, encompassing a broad spectrum of expressions. Leveraging BTVSL, we evaluated four distinct SLT models, achieving an average BLEU score of 20.42. This result underscores the potential of BTVSL in enhancing the accuracy of sign language translation.

关键词： Sign language Video on demand Image recognition Face recognition Symbols Process control Production

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Comparative Analysis of Deep Learning Models for Currency Recognition and Value Detection

Comparative Analysis of Deep Learning Models for Currency Re...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tata Kaushik Aasritha Sri Vani N Vithyatharshana Meena Belwal Sangita Khare Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Accurate currency recognition and conversion are essential for smooth cross-border transactions in today’s globalized economy. Yet, the wide variety of cash denominations and variances, combined with the complexities of deep learning and image processing, make this work extremely difficult. A comparative comparison of deep learning models is helpful in addressing these issues by identifying the best method for value identification and currency recognition. The objective of this research is to examine how well ResNet50V2, VGG16, and MobileNet perform in terms of precisely identifying and valuing currencies. Utilizing a sample of Thai and Indian banknotes from IEEE data port that include notes with differences in orientation (rotated at 180-degree angles), lighting conditions, and other environmental factors., the study assesses how well each model handles the complexity of money data. The purpose of this study is to give practitioners in the banking and finance industries useful insights.

关键词： Deep learning Industries Biological system modeling Computational modeling Image processing Lighting Environmental factors Complexity theory Object recognition Currencies

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