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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware 40

Exploring Large Language Models for Semantic Analysis and Ca...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Walton, Brandon J Khatun, Mst Eshita Ghawaly, James M Ali-Gombe, Aisha Louisiana State University Dept. of Computer Science and Engineering Baton Rouge United States

ISBN: (纸本)9798331532819

Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases' fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-Tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-Tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. © 2024 IEEE.

关键词： Android malware

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An Extensive Review of Intelligent Deep Learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging Data 3

An Extensive Review of Intelligent Deep Learning Based Anoma...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Hima Bindu, J. Uma Dev, M. Vignan's Foundation for Science Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350396157

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep Learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness. © 2024 IEEE.

关键词： Brain

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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NutriSpy: Investigate Your Food's Health Quotient 3

NutriSpy: Investigate Your Food's Health Quotient

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Thanay Kumar, Y.R. Joshi, Sudhanva S Sujan, A.T. Ujwal Karanth, K.V. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

NutriSpy is a comprehensive web and mobile application designed to enhance users' health and wellbeing through personalized food and exercise recommendations. At its core, NutriSpy integrates three powerful features: a tailored workout recommendation system, an advanced food identification tool, and a robust diet advisory service. By leveraging state-of-the-art machine learning algorithms, NutriSpy accurately identifies food items, assesses their nutritional and caloric content, and generates customized dietary guidance. The food identification system employs image recognition and natural language processing to provide precise nutritional information from photographs or descriptions of meals. This is combined with a diet advice system that considers individual health profiles, dietary restrictions, and fitness goals to offer personalized nutrition plans. Additionally, NutriSpy's workout recommendation system aligns exercise routines with users' dietary needs and fitness aspirations, creating a harmonious balance between food intake and physical activity. NutriSpy's sophisticated technology facilitates real-time adjustments and recommendations, making it a dynamic tool for promoting healthier lifestyles. By analyzing user data and integrating current health trends, NutriSpy not only aids in immediate dietary and fitness decisions but also contributes to long-term health improvements. This study explores the development and functionality of NutriSpy, detailing its components, underlying technologies, and potential impact on fostering healthier living habits. © 2024 IEEE.

关键词： Diet Recommendation system Exercise Recommendation System Food Recognition System

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Demystifying Existing Sentiment Analysis Approaches of Hindi and English Languages using Machine Learning 1

Demystifying Existing Sentiment Analysis Approaches of Hindi...

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

作者： Om, Hari Kumar Sharma, Avinash Sharda University Sharda School of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Gr Noida India

ISBN: (纸本)9798350356816

The number of operational information acquired, such as product reviews in several dialects, has expanded as the digital era has progressed. Many businesses, such as online service providers, benefit greatly from information analysis. Sentiment analysis (SA) is the technique of deciphering & categorizing the opinions/viewpoints/sentiments following the content (assessment) of user via text analysis tool (SA). Sentiment Analysis is a Natural Language Processing (NLP) program that is exercised in order to determine opinions of online consumers' comments, evaluations, thoughts, viewpoints and so on. Nowadays, social networking websites like Facebook & Twitter are recurrently utilised to submit consumer assessments regarding a variety of topics like politics, news, movies, fashion, food, etc. Viewpoints, Opinions & Reviews are important in determining the degree of gratification of consumers with a given unit. To determine the polarity, which might be neutral, negative, or positive. This study discusses a Sentiment Analysis technique on different social media platform reviews in Hindi. This paper presents a comparative study of sentiment analysis on Hindi and English texts using machine learning techniques. Datasets are accumulated of Hindi & English texts from various online sources, including social media platforms, news websites, and movie review sites. Various machine learning algorithms, including Naive Bayes, Support Vector Machines, Random Forest, and Logistic Regression, to perform sentiment analysis on the datasets. © 2024 IEEE.

关键词： Decision trees

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SafeSync: A Comprehensive Safety and Monitoring System for Two-Wheeler Users Using Machine Learning, ESP32, GPS, and GSM 6

SafeSync: A Comprehensive Safety and Monitoring System for T...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sunil, K.S. Adithyan, P. Krishnan, Anjana Hashim Sharaf, C.P. Paul, Jerin John, Salvi Hanna Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381689

Our work, 'SafeSync' proposes a complete safety and monitoring system aimed to address the considerable hazards encountered by two-wheeler users, such as accidents and rash driving scenarios. Unlike other systems, SafeSync combines powerful technology such as ESP32, GPS, and GSM with an innovative application of machine learning methods. This system specializes in detecting and alerting to accidents, tracking vehicles in real time, and identifying risky driving behaviour. SafeSync goes beyond traditional safety measures by not only identifying ongoing accidents but also forecasting potential events. This predictive feature enables the system to proactively inform users or authorities in advance, assisting in the prevention of accidents. The Flutter-built dedicated mobile app serves as a user-friendly interface, offering real-time updates on the vehicle's status and location. The program also allows users to customise monitoring settings and add parental restrictions. By actively recognizing and warning users to dangerous practises, SafeSync strives to improve rider safety and promote responsible driving behaviour. Finally, the initiative helps overall road safety by providing a solid solution for two-wheeler users to monitor and enhance their road safety. © 2024 IEEE.

关键词： Accident Alert ESP32 GPRS GPS GSM MPU6050 Rash Driving

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