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AI and Machine Learning Applications in Hepatitis B Diagnosis: An Exploration of Techniques and Outcomes

AI and Machine Learning Applications in Hepatitis B Diagnosi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Sandeep Kumar Mohit Arora Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hepatitis B is one of the major global health challenges, affecting millions worldwide and frequently leading to serious liver conditions like hepatocellular carcinoma and cirrhosis. Early and accurate diagnosis is very important for effective management and treatment; however, most of the traditional diagnostic approaches, including serological tests, liver function tests, liver biopsy, etc., are very often affected by limitations in resource availability and diagnostic accuracy. This review paper highlights the current development in various research works on the application of artificial intelligence (AI) and machine learning (ML) in the diagnosis of Hepatitis B. This paper reviews all the major ML computational techniques used in Hepatitis B diagnosis, underlining the use of various computational techniques: Generalized regression neural networks (GRNN), multilayer Mamdani fuzzy inference system (ML-MFIS), hybrid models combining clustering with ensemble learning, ANFIS, deep neural network (DNN), and support vector machine (SVM). These methodologies have been reported to show substantial improvement in the performance of diagnosis, where the maximum accuracy rate is up to 99.35%. This paper has also listed the datasets used for the studies and compared the performance metrics of these studies in terms of precision, accuracy, and F-score. Despite the progress, several issues persist, such as small dataset sizes, data imbalance, and the need for thorough validation across diverse clinical settings. Several research questions are also outlined to showcase the current ML diagnosis methods and their performances along with their limitations to provide the future research direction. It also covers the adoption of explainable AI (XAI) as of one the potential future aspect.

关键词： Support vector machines Fuzzy logic Accuracy Liver diseases Explainable AI Reviews Artificial neural networks Nonhomogeneous media Ensemble learning Informatics

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Hybrid RNN-CNN model for predicting stock market trends

Hybrid RNN-CNN model for predicting stock market trends

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Bhanujyothi H C I Jeena Jacob Dept. of Computer Science and Engineering GITAM Deemed to be University Bengaluru India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Stock market prediction has become a challenging task in today’s world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model.

关键词： Accuracy Computational modeling Time series analysis Predictive models Market research data models Nonlinear dynamical systems Convolutional neural networks Stock markets Long short term memory

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Rishabh Mahanta Hemangee K. Kapoor Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (数字)9798331539672

ISBN: (纸本)9798331539689

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure.

关键词： Performance evaluation Costs Sensitivity analysis Nonvolatile memory Very large scale integration Batteries Maintenance Proposals Energy harvesting Energy management

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Voice Mate: Voice Controlled Chessboard for Visually Impaired

Voice Mate: Voice Controlled Chessboard for Visually Impaire...

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

作者： Anishamol Abraham Agnel Mammen Ajay Soyi Ben Jhonson Joel Biju Dept. of Computer Science Amal Jyothi College of Engineering Kottayam India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Traditional chess can affect people with disabilities who have physical difficulty controlling the pieces, although the concept and gameplay are closely related. This paper explores the concept of voice-controlled chessboards to address this gap and encourage participation in chess. This paper presents a method for creating voice-controlled chessboards. This method introduces the structure of the circuit board, focusing on communication skills. It explains how to use stepper motors to move the chess according to commands. The paper recognises the importance of careful observation and control to ensure correct pronunciation and good chess performance. This paper discusses possible implications and areas that require further consideration. These areas include ensuring speech accuracy, maintaining reliable status monitoring of the card, and ensuring motor control. Solving these issues through development and testing is crucial to the success of voice-controlled chessboards. The paper concludes by highlighting the importance of user testing with target users (people with disabilities) to improve usability. This project has the potential to not only make chess more accessible but also stimulate the development of new technologies for a variety of recreational games.

关键词： Accuracy Speech recognition Games People with disabilities Motors Reliability Sustainable development

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Automated Explanation Quality Assessment based on Similarity Calculations of Representative Explanations of Different Qualities

Automated Explanation Quality Assessment based on Similarity...

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Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Chih-Yueh Chou Yu-Ting Chou Yu-Cheng Huang Dept. of Computer Science & Engineering Yuan Ze University Taoyuan Taiwan

Peer instruction is instructional in guiding students to learn by answering questions, and explaining and discussing their answers with peers. Researchers recommended asking students to write down their answers and explanations before discussion to prevent social loafing. In addition, text-based explanations can be recorded and analyzed. The quality of students’ explanations varies, ranging from superficial and low-quality to detailed and in-dept. high-quality explanations. In tradition, the qualities of students’ explanations were assessed by experts. Recently, machine learning classification models have been developed and applied to classify texts. However, the level of explanations of questions are question-dependent. Thus, each question needs its classification model. Therefore, a feature transformation was applied in this study so that the explanations of different questions could be combined and applied to train the same classification model. An automated explanation quality assessment mechanism was developed based on the similarity of representative explanations of different qualities. Students’ text-based explanations were collected and assessed by experts into four levels, ranging from 0 (worst) to 3 (best). The four-level classifications were merged into binary classifications of low (0 and 1) and high (2 and 3). Different classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT) were applied to train models and evaluate the accuracy of the models. In addition, three ensemble learning algorithms, including voting, stacking, and boosting, were applied to combine models chosen from SVM, NB, KNN, LR, and RF. The results showed that RF and RF+KNN+NB with stacking model showed the best accuracy (75.3%) among all four-level classification models whereas RF with boosting model showed the best accuracy (9

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Sentiment Analysis of COVID-19 Tweets 1

Sentiment Analysis of COVID-19 Tweets

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1st International Conference on Informatics, ICI 2022

作者： Gupta, Isha Chatterjee, Indranath Gupta, Neha Mriirs Faculty of Computer Applications Faridabad India Tomsyong University Dept. of Computer Science Engineering Busan Korea Republic of

ISBN: (纸本)9781665437219

Sentiment analysis is the field of computer science that studies people's behaviour or attitude towards objects, events, organizations, and products. Organizations make use of this to improve their products. This paper aims to study people's behaviour towards COVID 19. We have taken data from Twitter to analyse the sentiments. The results show that people don't have positive sentiments towards this coronavirus. © 2022 IEEE.

关键词： COVID-19

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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Grapevine Leaf Disease Classification Using Deep Convolutional Neural Networks

Grapevine Leaf Disease Classification Using Deep Convolution...

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Big data Analytics and Practices (IBDAP), International Conference on

作者： Md Al-Imran Sajid Faysal Fahim Sanjida Simla Fatin Hasnat Shakib Tokiuddin Ahmed Sarwar Jahan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

The existence of numerous diseases that afflict the fragile and lush leaves of grapevines, bringing with them their destructive impact, presents a looming and formidable danger to the meticulous and intricate art of cultivating grapes. This peril not only puts at risk the bountiful harvest but also endangers the unmatched brilliance and refinement of the resulting crop. In the present study, we employ cutting-edge Deep Convolutional Neural Networks (DCNNs) to present a fresh and innovative approach to identifying disorders that affect grapevine leaves. Our methodology involves the finetuning of pre-existing DCNN models, utilizing a tailor-made dataset composed of a wide array of images showcasing grapevine leaves affected by various diseases. This approach facilitates precise classification and reliable generalization. The far-reaching consequences of this investigation extend far beyond the mere advancement of grapevine disease detection. It lays the groundwork for a comprehensive system that might dramatically revolutionize the method by which we monitor illnesses in vineyards. This has the opportunity to not only transform the world of viticulture but also open the door to more environmentally friendly approaches that will eventually lead us to a more affluent and ecologically conscious future.

关键词： Plant diseases Image recognition Biological system modeling Pipelines Neural networks Transforms Convolutional neural networks

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ConfigMaster: An Interactive Solution for System Management

ConfigMaster: An Interactive Solution for System Management

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Ananya Ganapathi Parikshith Sivakumar Divya K V Aditya Elango Hardik Gupta Dept. of Computer Science Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The “ConfigMaster: An interactive solution for system management” is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. “ConfigMaster” aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, “ConfigMaster” enables users to efficiently perform system configurations along with promoting ease of use.

关键词： Wireless communication Productivity Knowledge engineering Software packages Operating systems Memory management User experience Hardware Synchronization Information technology

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Zero Area Overhead Based Scan-Chain Watermarking

Zero Area Overhead Based Scan-Chain Watermarking

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Ashwin CS Suchismita Roy Dept. of Computer Science & Engineering National Institute of Technology Durgapur India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Preventing the illegal use of Intellectual Property (IP) cores remains a crucial challenge for the semiconductor industry. Hardware watermarking has emerged as a practical and widely adopted solution to address this issue by verifying the authorship of the design, thereby detecting IP piracy. For a watermarking scheme to be robust, it must ensure easy detectability and be resilient against various attacks. In this paper, we propose a novel scan chain-based watermarking technique to protect hardware intellectual property (IP) against unauthorised usage and counterfeiting. The proposed method leverages the inherent properties of scan chains used in design-for-testability (DFT) to embed a unique watermark within the hardware design. By utilising genetic algorithms for optimal scan chain reordering and watermark embedding, our approach ensures minimal impact on the hardware's performance and testability. Experimental results demonstrate the robustness and effectiveness of the proposed watermarking scheme in various scenarios.

关键词： Electronics industry Watermarking Intellectual property Probability Hardware Robustness Protection Counterfeiting Intelligent systems Genetic algorithms

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