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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Sasidharan, Anjali K. Rahulnath, R. Department of Computer Science and Application Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain's rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain. © 2023 IEEE.

关键词： Clustering methods Graph reasoning Knowledge graph creation Legal documents Machine learning Natural language processing Relation extraction

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AI-Powered Job Market Analysis: Transforming Workforce Trends and Employment Predictions 5

AI-Powered Job Market Analysis: Transforming Workforce Trend...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Purohit, Sanjana Shuja, Mirza Walia, Sahil Kumar, Mukesh Bajaj, Karan Bhardwaj, Vivek Jecrc University School of Computer Applications Rajasthan Jaipur India Lovely Professional University School of Computer Application Punjab Phagwara India Snowflake Inc. AtlantaGA United States Chandigarh Group of Colleges Chandigarh School of Business Department of Computer Application Punjab Mohali140307 India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350376685

This analysis delves into the "AI-Powered Job Market Insights"dataset, offering a comprehensive exploration of how artificial intelligence (AI) and automation are influencing the modern job market. The dataset provides a snapshot of job listings across various industries, highlighting factors such as AI adoption levels, automation risk, and salary ranges. By examining these elements, we aim to uncover key trends and insights into how AI is reshaping employment opportunities and job market dynamics. The primary objective of this analysis is to identify and understand key patterns related to salaries, required skills, remote work, and job market trends influenced by AI. By employing various analytical techniques, we seek to reveal how AI adoption impacts compensation, skill requirements, and job growth, as well as how it correlates with the likelihood of remote work. © 2024 IEEE.

关键词： Job analysis

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Facial Expression Recognition (FER) System Using Deep Learning 1

Facial Expression Recognition (FER) System Using Deep Learni...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Yan, Ooi Juin Ashraf, Humaira Ihsan, Uswa Jhanjhi, Nz Ray, Sayan Kumar Kavita Taylor's University Masters of Applied Computing Selangor Malaysia Taylor's University School of Computer Science SCS Malaysia International Islamic University Department of Computer Science Islamabad Pakistan Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Sentiment analysis of both text and images have been widely implemented machine learning application. Facial expression recognition (FER) is a widely known application of sentiment analysis of image data. This application has already been implemented in various industry, but challenges such as the complexities of facial features and different image angle still remain. This project aims to develop a FER system capable of classifying human expressions through facial images using deep learning techniques. A CNN-based architecture was proposed and training using the FER2013 dataset, which is a commonly used facial expression dataset. Overall, the trained model achieved an accuracy of 63.97% through the dataset. The trained CNN model was then employed to develop a web-based prototype of a FER system through Python Flask. © 2024 IEEE.

关键词： Sentiment analysis

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An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

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computer Modeling in engineering & sciences 2023年第8期136卷 1643-1659页

作者： Siyu Ren Shi Qiu Keyang Cheng The Open University of Chengdu Major in Computer ApplicationChengdu610000China Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences.Xi’an710119China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China

Star sensors are an important means of autonomous navigation and access to space information for *** have been widely deployed in the aerospace *** satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor *** sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single *** research on the data obtained by the star sensor has very important research and application ***,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data ***,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient ***,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy *** a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the *** show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.

关键词： Star-sensing sensor cloud fuzzy set edge computing mapping

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Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnosis 4

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pattnayak, Parthasarathi Mishra, Debaswapna Patnaik, Sanghamitra Mohanty, Arpeeta School of Computer Applications KIIT Deemed to Be University Bhubaneswar India OUAT Department of Computer Science and Application Bhubaneswar India School of Law KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. Three datasets - MIMIC-III, MIMIC-IV, and NHANES - were used. The equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. The model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person. © 2024 IEEE.

关键词： Adversarial machine learning

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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A Survey of Current Progress in Depression Detection Using Deep Learning and Machine Learning

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Biomedical Materials and Devices 2025年 1-25页

作者： Kumari, Meena Singh, Gurpreet Pande, Sagar Dhanraj Computer Application Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Department of Computer Science and Engineering Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune 412106 India

Generally, depression is the most prevalent mental disorder, which has affected hundreds of people around the world and causes a high risk of committing suicide. Automatic diagnosis of depression detection plays a vital role in examining people’s mental illness through various Artificial Intelligence (AI) techniques and has a high probability of reducing patient’s burden by early treatments. In this research, the crucial area of depression detection is examined, with an emphasis on the crucial function of hybridized algorithms. More studies have been developed worldwide to provide a review of depression detection techniques. Still, obtaining significant information about the input data and the different mechanisms utilized in each research for depression detection is challenging. Consequently, this review presents a systematic review of various Deep Learning (DL) and Machine Learning (MI) techniques based on the input data utilized in the models for detecting depression, heralding a seismic shift in our understanding of depression and the diagnostic and support systems connected to it. More specifically, this review examines the literature from 50 recent research articles based on depression detection topics incorporated with its significant morphology that helps findings in the creation of novel models in the future. Further, this review discusses the research gaps, challenges, dataset collection, interdisciplinary cooperation, other ideal strategies, and future scope for progressing and catalyzing the research in this domain. In addition, this review presents an overview to identify the objectives and restrictions of different literature works presented in the domain of depression detection. Moreover, the developed review assists in advancing the knowledge about the technologies adopted for depression detection and serves as a suitable reference for upcoming researchers working in this domain. © The Author(s), under exclusive licence to Springer science+Business M

关键词： Deep learning Depression detection Machine learning Multimodal data Natural language processing

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： engineering education

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A Deep Learning Framework for Classifying Autism Spectrum Disorder from fMRI Images

A Deep Learning Framework for Classifying Autism Spectrum Di...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Varshney, Rajat Kishor Katiyar, Alok Johri, Prashant School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (纸本)9798331542344

The objective of this work is to propose a new machine learning based approach for ASD classification from fMRI data employing deep learning architecture and novel preprocessing method. Main datasets included in ABIDE I, ABIDE II, Human Connectome Project (HCP), and Social Functions and Autism Research Interface (SFARI). Many supplements include graph neural networks for brain segmentation, 3D-BRA for accurate mapping of the brain than just using conventional registration technique, EN-CC for feature selection. The approach incorporated graph neural network for segmentation of the brain, 3DBRA mapping that translates and rotates the brain precisely unlike the general technique of registration, EN-CC for selecting the features. The deep learning models under consideration were ResNet-50, VGG-16, Inception-v3, and Efficient Net which were improved through ABSC and MSTC. Classification accuracy of the proposed models was higher than 95% on all the datasets with Efficient Net scoring 99% on the SFARI dataset. This means that only the Stratified Group Cross-Validation (SGCV) was employed to make sure the results would be more reliable. The results present a considerable improvement in both accuracy and the interpretability of the derived connectivity profiles of the ASD-related brain. © 2024 IEEE.

关键词： Graph neural networks

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