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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Panthagani, Vijaya Babu Duggempudi, Vijaya Deepika Reddy Kommera, Naga Lakshmi Vakkalagadda, Nikhita Vignan's Foundation for Science Technology and Research Dept. Computer Science and Engineering A.P Guntur India

ISBN: (纸本)9798350395235

In the modern world, a video transcript summarizer offers a wide range of applications. It draws attention to the crucial points in the video. YouTube videos are watched in large quantities by people for a variety of reasons, including enjoyment, education, obtaining crucial information, and pursuing personal hobbies. Finding a video with any meaningful information about a subject is incredibly tough because most videos are just buffers for unimportant information. The main goal in the majority of situations is to get useful information from the video. In order to generalize YouTube video transcripts for abstractive text summary without sacrificing the essential details and substance, this research has developed a video summarization system based on Natural Language Processing and Machine Learning. The goal of this project is to make the writing for the videos shorter. © 2024 IEEE.

关键词： Machine learning

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Innovative Solution for Enhancing Mechanical Water Meters: A Smart Water Meter Add-On

Innovative Solution for Enhancing Mechanical Water Meters: A...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Chakir, Youness Aaroud, Abdessadek Chouaib Doukkali University Lab. Elites Faculty of Science Dept. of Computer Science EI Jadida Morocco

ISBN: (纸本)9798350365306

The effective management of clean water is crucial in smart city infrastructure. In Morocco, traditional mechanical water meters are widely used, but they lack advanced data collection capabilities. This paper explores the potential of integrating Internet of Things (IoT) technology into a smart water meter add-on or extension, designed to enhance the capabilities of existing mechanical water meters. We review existing solutions and identify key components for a novel IoT-integrated smart water meter add-on, including infrared sensors, dynamic mechanical design, ultra-low power microcontrollers, and LoRaWAN modems. Our goal is to design a cost-effective and efficient solution for monitoring water consumption and detecting leaks in the Moroccan context. This paper lays the foundation for the development of a new IoT-integrated smart water meter add-on, which will be presented in future work. © 2024 IEEE.

关键词： Machine design

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Development of a Multimodal Machine Learning Model for Seizure Detection Using Wearable Devices 1

Development of a Multimodal Machine Learning Model for Seizu...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Rathore, Saurabh Pratap Magotra, Sumedha Sruthy, K.V. Sharma, Sonu Kumar Kaur, Gaganpreet Singh, Sailendra ICAPSR Dept. of Management New Delhi India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of Computer Science & Engineering Coimbatore India Mangalayatan University Dept. of Comp. Eng & Applications Aligarh India Lovely Professional University Punjab India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

This research explores the transformative potential of wearable devices designed to automatically detect & predict epileptic seizures, offering continuous monitoring & early detection capabilities. The study focuses on developing a machine learning model tailored for seizure detection in such devices, leveraging multimodal sensors such as Electrodermal Activity (EDA) & Accelerometer (ACC). By measuring skin resistance and identifying irregular heartbeats, which are signs of upcoming seizures, these sensors allow for precise seizure detection. The study intends to demonstrate the effectiveness of this machine learning model in wearable technology with the goal of improving patient outcomes and seizure management. © 2024 IEEE.

关键词： Accelerometers

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

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2024 IEEE International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： data integrity

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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BERT Goes to SQL School: Improving Automatic Grading of SQL Statements

BERT Goes to SQL School: Improving Automatic Grading of SQL ...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Rivas, Pablo Schwartz, Donald R. Quevedo, Ernesto School of Eng. & Computer Science Baylor University Dept. of Computer Science United States School of Computer Science & Math. Marist College Dept. of Computing Technology United States

ISBN: (纸本)9798350327595

Automated grading of SQL queries is a challenging task due to the complexity of the language and the variety of acceptable solutions for a given problem. In this paper, we propose a novel approach that leverages deep learning with a BERT model to understand the syntax and semantics of SQL statements. By training BERT on a dataset of SQL queries and their corresponding grades, we create a model that can automatically grade new questions accurately. Our experiments demonstrate that the proposed methodology achieves high accuracy and consistency in grading SQL queries, outperforming existing state-of-the-art models. Furthermore, we provide an analysis of the model's explainability, revealing a new capability that can be extremely beneficial for understanding the decision-making process. Overall, our work demonstrates the potential of deep learning with BERT for improving the efficiency and accuracy of SQL query grading. © 2023 IEEE.

关键词： bert deep learning machine learning natural language processing sql

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Storage Assignment Using Nested Metropolis Sampling and Approximations of Order Batching Travel Costs

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SN computer science 2024年第5期5卷 477页

作者： Oxenstierna, Johan Malec, Jacek Krueger, Volker Dept. of Computer Science Lund University Lund Sweden Kairos Logic AB Lund Sweden

The Storage Location Assignment Problem (SLAP) is of central importance in warehouse operations. An important research challenge lies in generalizing the SLAP such that it is not tied to certain order-picking methodologies, constraints, or warehouse layouts. We propose the OBP-based SLAP, where the quality of a location assignment is obtained by optimizing an Order Batching Problem (OBP). For the optimization of the OBP-based SLAP, we propose a nested Metropolis algorithm. The algorithm includes an OBP-optimizer to obtain the cost of an assignment, as well as a filter which approximates OBP costs using a model based on the Quadratic Assignment Problem (QAP). In experiments, we tune two key parameters in the QAP model, and test whether its predictive quality warrants its use within the SLAP optimizer. Results show that the QAP model’s per-sample accuracy is only marginally better than a random baseline, but that it delivers predictions much faster than the OBP optimizer, implying that it can be used as an effective filter. We then run the SLAP optimizer with and without using the QAP model on industrial data. We observe a cost improvement of around 23% over 1 h with the QAP model, and 17% without it. We share results for public instances on the TSPLIB format. © The Author(s) 2024.

关键词： Metropolis algorithm Order batching problem Quadratic assignment problem Storage location assignment problem Warehousing

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of data/data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Enhancing Emotional Support Chatbots Using Deep Learning & NLP: A Study on Personalization and Effectiveness 1

Enhancing Emotional Support Chatbots Using Deep Learning & N...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Renuka Parihar, Sanjay Khalid, Md. Saifullah Sharma, Umesh Chandra Mary, J. Josphin Chandigarh University Dept. of Computer Science Chandigarh India Madhav University Dept. of Humanities & S.Science Pindwara India Usha Martin University Dept. of Bus. Management & Commerce Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H.E.R Dept. of Computer Science Chennai India

ISBN: (纸本)9798350356816

This research focuses on utilizing deep learning and natural language processing (NLP) to design an AI-powered chatbot for emotional support. The primary objective is to establish a supportive platform for individuals dealing with emotional challenges. By leveraging NLP, the chatbot will identify and interpret users' emotional states, generating tailored and empathetic responses. Deep learning techniques will enable the system to learn from past interactions, adapting its responses to meet the unique needs of each user. The platform aims to deliver personalized, scalable support while incorporating features like sharing uplifting content, such as cute cat images. This study emphasizes the potential of AI-driven solutions in enhancing mental well-being through empathetic and meaningful interactions. © 2024 IEEE.

关键词： Contrastive Learning

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