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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Das, Mridusmita Senapati, Apurbalal Central Institute of Technology Kokrajhar Department of Computer Science and Engineering Kokrajhar India

ISBN: (纸本)9798350337785

Co-reference resolution (CR) is an essential task in several Natural Language Processing (NLP) applications. It implies finding all the linguistic expressions (known as mentions) in a given text and the references of those mentioned. Consider the following text: Ishwar Chandra Bandyopadhyay was a great man in India in the nineteenth century. He received the honorable title Vidyasagar. In this text, each italics phrase is a mention and all are co-references. That means both expressions He and Vidyasagar refer to the same discourse entity Ishwar Chandra Bandyopadhyay. It is used in various NLP applications like Text Summarization, Information Retrieval (IR), Question Answering systems, Machine Translation (MT), etc. To develop a co-reference system using any algorithm must be needed the data. For a resource-scarce language like Assamese, there is no such data set or supportive tools to prepare the data. So, the data preparation is the primary task of the research in co-reference resolution. This paper attempt a semi-automated approach to develop a tagged data set for the co-reference resolution. This resource will boost the researchers in NLP research in Assamese languages. © 2023 IEEE.

关键词： Search engines

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Sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. They make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. This paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. The work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. The work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 IEEE.

关键词： Energy utilization

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HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

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VeriCombTest: Automated Test Case Generation Technique Using a Combination of Verification and Combinatorial Testing 18

VeriCombTest: Automated Test Case Generation Technique Using...

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18th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2023

作者： Godboley, Sangharatna Department of Computer Science and Engineering National Institute of Technology Telangana Warangal India

ISBN: (纸本)9789897586477

We propose VeriCombTest which is the combination of Verification and Combinatorial Testing. We experimented with 38 C-Programs from The RERS challenge repository. Verification (CBMC) produced 940 test cases and Combinatorial Testing (PICT) populated a total of 42053 test cases. The good point is that for 40 programs, PICT consumed only 2.6 Minutes to populate the test inputs, however, CBMC which is a Static Symbolic Executor consumed 546.99 Minutes to generate the test inputs. We performed mutation analysis for this work. VeriCombTest has 355 extra killed mutants as compared to the baseline. VeriCombTest is a fully automated tool. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： C (programming language)

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TCI-Net: A Deep Learning Approach for Tropical Cyclone Intensity Prediction

TCI-Net: A Deep Learning Approach for Tropical Cyclone Inten...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Gujral, Sahil Rao, Darshan Khanvilkar, Anuj Shah, Umit Maktum, Tabassum Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350300826

One of the most affecting types of weather phenomena are tropical cyclones (TCs), commonly referred to as typhoons or hurricanes. Atlantic Ocean, North Pacific Ocean, Western Pacific Ocean, South Pacific Ocean, and Indian Ocean are the five oceans where hurricanes, typhoons, and tropical cyclones can form. Many techniques have been tried in the past to monitor cyclones and assess their intensity before they form, but they have always been shown to be ineffective. It is crucial to estimate TC intensity in order to forecast and decrease human loss and suffering. Recently, data scientists in addition to meteorologists have started to show interest in the task. This claim demonstrates an innovative approach based on image processing for predicting TC intensity given a meteorological geostationary satellite imagery. The Convolutional Neural Network (CNN) architecture which has to be modified in a number of ways, will be used to create the suggested cyclone intensity estimating model. The results obtained from experiments conducted on a widely accessible benchmark dataset reveal that the proposed approach stands out as one of the most precise models for estimating TC intensity. Moreover, it exhibits remarkable stability across various scenarios. These findings underscore the substantial promise of harnessing data science in the field of meteorological research. © 2023 IEEE.

关键词： Convolution

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GWO-Based Workflow Scheduling in Cloud-Fog Environments

GWO-Based Workflow Scheduling in Cloud-Fog Environments

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2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Soni, Dinesh Kumar, Neetesh Indian Institute of Technology Department of Computer Science and Engineering Roorkee247667 India

ISBN: (纸本)9798350305111

The scientific workflows are a set of predefined tasks aiming to achieve a target goal state. Cloud computing provides an appropriate environment for orchestrating such scientific workflows. However, scheduling these workflows in a cloud environment is a big challenge due to the high latency and connectivity on low bandwidth network infrastructure. Nowadays, fog and edge computing devices offer an alternative to cloud computing. As a result, these devices can provide cloud computing capabilities for processing scientific workflows near the source. This paper explores the possibilities of scheduling workflows on both cloud and fog computing devices. In order to schedule workflows on cloud-fog integrated devices, we implemented the Grey Wolf Optimizer (GWO) based algorithm using dynamic weights for updating the grey wolf position on the FogWorkflowSim simulator. The proposed scheme avoids getting into a local optimum and improves the performance of the QoS parameters. We also compare the outcomes of different scheduling algorithms in cloud and hybrid cloud-fog architectures. The simulation results of the proposed approach using well-known scientific workflows suggest that cloud-fog integration for workflow scheduling improves processing performance while reducing energy consumption and cost. © 2023 IEEE.

关键词： Fog

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Exploring the Self-Organizing Mechanisms for Maintaining Stability in Multi-Agent Systems within Dynamic Environments 3

Exploring the Self-Organizing Mechanisms for Maintaining Sta...

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3rd International Conference on computer Science, Electronic Information engineering and Intelligent Control technology, CEI 2023

作者： Gao, Sheng Liu, Wei Mu, Yongan Wang, Kai Wuhan Institute of Technology Dcse Department of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350372052

In the absence of external interference, independent multi-agent systems evolve over time, leading to a transition from an ordered to a chaotic state within the internal dynamics of the scene. To assist in maintaining the stability of dynamic systems, this paper explores the design and composition of the scenario. A category of altruistic agents with selfless attributes is introduced into the multi-agent environment, and the decentralized algorithm I-DDPG is employed to train these altruistic agents to perform tasks with altruistic tendencies. Weakly connected self-organization is used to enable cooperation among altruistic agents in addressing internal issues. The study demonstrates a positive correlation between the overall system stability and individual information entropy, as well as the method of obtaining individual altruistic rewards. To investigate the connection between overall scene stability and self-organization among internal individuals, a traffic scenario is constructed with both human-driven and autonomous vehicles. Autonomous vehicles (AVs) represent a type of altruistic agent, and experiments are conducted to explore the relationship between scene stability and internal self-organization among individuals. The study concludes that using I-DDPG improves the learning efficiency of agents compared to DDPG and demonstrates a higher safety level in stress tests. Post-training, agents utilizing weakly connected self-organization in scenarios involving merging and exiting obtain higher altruistic rewards. This approach aids human-driven vehicles in achieving their intended goals, contributing to an overall improvement in the stability of dynamic systems. © 2023 IEEE.

关键词： Multi agent systems

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An Expert System for Analyzing the Behavior of Student's in the Higher Education 10

An Expert System for Analyzing the Behavior of Student's in ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bansal, Pravesh Kumar Ahmed, Mushtaq Malviya National Institute of Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9789380544472

The institution and the students can respond immediately to improve performance by implementing an interim mechanism to analyze and anticipate student performance early. Growing demand in the education industry has opened the way for numerous research projects that strongly emphasize students' academic achievement and behavior analysis. Machine learning methods are used in data mining for education to extract functional patterns and insights from educational datasets. Research supporting assessments of students' mindfulness in classrooms has drawn far less empirical attention. The Student Mindfulness Survey, a new 50-item, multidimensional, self-report measure of students' mindfulness strongly tied to theory and specifically aimed at college settings, is presented in this study as a case for its use. After being piloted with a target sample of 208 students, the Data underwent several structural and concurrent validity analyses. The outcomes demonstrated how data recipients reacted regarding acceptance, approach, and perseverance. The importance of future research and its value is highlighted in my study. This study used educational data mining to examine how contemporary pupils learn. Our case study gathered student information about the above attributes from a higher education institution. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data mining

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computer Vision Based Car Overtaking Advisory

Computer Vision Based Car Overtaking Advisory

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Samarpana U, Delphine Deb, Neha Muppalaneni, Naresh Babu Gupta, Puspayon Das National Institute of Technology Silchar Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798350310061

A vehicle overtake advisory system is a technology that instructs drivers on how to pass another vehicle safely while driving. Overtaking involves inherent risks, as it calls for the driver to move into the oncoming traffic lane and possibly into the path of other vehicles. Poor road conditions, driver error, and limited visibility can also contribute to an increased risk of accidents when overtaking. In these circumstances, overtaking advisory systems may be quite helpful. These systems provide drivers with real-time information about the current state of the road, the position and speed of other vehicles, and any obstacles or hazards in the surrounding environment, thereby increasing road safety and lowering the risk of accidents. This study proposes a method to generate a car overtake advisory based on computer vision. Cameras are mounted in front of vehicles which capture video streams in a variety of illumination and weather conditions. The frames extracted from the video feed are then used for speed estimation of the vehicles and an overtake advisory is generated. The frames extracted from the video feed are then used to estimate the speed of the vehicles. Based on the speeds and positions of the vehicles, an overtaking advisory is generated. © 2023 IEEE.

关键词： computer vision

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Federated Learning for Wearable Sensor-Based Human Activity Recognition 7th

Federated Learning for Wearable Sensor-Based Human Activity...

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7th International Conference on Intelligent Technologies, ICIT 2022

作者： Sachin, D.N. Annappa, B. Ambesenge, Sateesh Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9789819919116

Computing devices that can identify various human behaviors or motions may be used to aid individuals in multiple contexts, including sports, healthcare, and interactions between humans and robots. Data readily accessible for this purpose may be collected from smartphones and wearable devices used daily. Therefore, efforts are made to classify real-time activity data effectively utilizing various machine learning models. However, current methods for human activity recognition do not sufficiently consider user data privacy. To mitigate privacy issues, federated learning can be employed to build generic activity classification model by aggregating a locally trained model at a user-edge device. This paper adopted a deep-learning neural network model called the transformer for motion signal time-series analysis. It uses the attention mechanism to provide context for each point in the time series. It also compares federated learning’s performance to centralized learning. Experimental results show that federated learning outperformed centralized training without com- promising user data privacy, with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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