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Unravelling the Complex Networks of Social Physics:Exploring Human Behavior,Big Data,and Distributed Systems

Journal of Social Computing 2024年第2期5卷 165-179页

作者： Subrata Paul Dhritiman Mukherjee Anirban Mitra Anuradha Mitra Pushan Kumar Dutta the Department of Computer Science and Engineering Brainware UniversityKolkata 700125India. the Department of Computer Science and Engineering Amity University KolkataKolkata 700135India the Department of Management University of LiverpoolLiverpoolL697ZXUK the Department of Electronics and Communication Engineering Amity University KolkataKolkata 700135India

Networks form an integral part of our daily lives, comprising structures known as nodes connected via ties. These networks exhibit a complex interplay of random and structured features. We encounter networks regularly in our daily activities, whether it is logging into websites, commuting by train, going to work, or attending school. Networks are prevalent in various academic disciplines and practical applications, spanning biology, physics, engineering, social science, and numerous other fields. The early 21st century has witnessed a rapid expansion of techniques facilitating human interaction, significantly impacting behavior and social bonding in both urban and rural settings. The availability and accessibility of extensive population datasets have spurred ongoing research into understanding and shaping human inclinations and sociological phenomena in unconventional ways. This review delves into the nuanced aspects of social physics, exploring the rationale behind choosing this research topic, various conceptual frameworks, categorizations, and distinctions between machine learning and deep learning. The article briefly outlines the advantages of leveraging big data in social physics, highlighting its transformative potential. Lastly, the authors provide insights into several applications of social physics in subsequent sections, offering a glimpse into its real-world implications.

关键词： networks social physics machine learning deep learning social science

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Instant Sign Language to Audio and Text Translation 15

Instant Sign Language to Audio and Text Translation

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tirpude, Shubhangi Sheikh, Mohammad Saify Shrigiriwar, Himanshu Bedi, Soham Mhatre, Rugved Department of Computer Science Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798331300579

In a world where gestures speak volumes and silence carries meaning, we introduce a groundbreaking innovation: a blend of technology and compassion that translates sign language into written text, and then to audio in real-time. Welcome to a realm where hands shape words, and inclusivity finds its voice. This report sheds light on the journey of creating a transformative solution, set to bridge communication gaps and champion accessibility for everyone. Our system is skilled at recognizing 26 letters, operating instantly to capture and interpret hand gestures accurately. By using advanced machine learning and computer vision, it identifies characters and forms words on-the-spot, allowing for complete sentences to be constructed effortlessly. This report not only explains the technical details of our solution but also highlights its profound impact on society. By breaking communication barriers for those with hearing impairments, our system promotes inclusivity, ensures equal access to information, and empowers users to participate fully in various social and professional contexts. The development of this real-time sign to-text conversion system represents a significant step towards creating an inclusive digital space. Its smart design, smooth operation, and societal benefits make it a symbol of progress, ready to redefine communication norms and enhance accessibility for all. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Low Birth Weight Prediction Using Machine Learning 5th

Low Birth Weight Prediction Using Machine Learning

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Harika, G. Venkata Lakshmi, T. Santhosh Prudhvi Raj, P. Prasan, U.D. Jayanthi Rao, M. Department of Computer Science & Engineering Aditya Institute of Technology and Management Tekkali532201 India

ISBN: (纸本)9789819767311

In recent decades, machine learning has its increased problem solving methodologies and applications in various fields of business, marketing, education and medical diagnostics. Among all the ML techniques, some have been employed in the fields of medical sciences to predict various health conditions which require complete analysis of the patient’s health by considering some parameters which impact the health condition of an individual. Low birth weight of infants has been a constant recurring problem which has its visible impacts only after the birth of the baby. It acts as an indicator of sickness in newborn baby’s weight. This can be avoided using suitable ML techniques which predict required values from the information passed to the trained model. The prediction algorithm is implemented using random forest regression algorithms. We are aiming to develop a website which considers the health indicators as inputs from the user and provides them with estimated value by the trained ML model. The measures of metrics used here are the number of gestational days, age of mother, height, weight, plurality, and smoking status of the mother. All of these parameters can be used to estimate the accurate value of the weight of the baby within the web application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CSS HTML LBW prediction Machine learning Python

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Dimensionality Reduction via Graph-based Feature Selection 8

Dimensionality Reduction via Graph-based Feature Selection

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Bojnal, Hrishik Sai Singh, Parnika Anitha, J. Rv Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Given the rapid influx of data, organizing and managing its features poses a significant challenge. Many features are deemed redundant, impeding efficient storage and training of models. We introduce a method to address this challenge by defining an algorithm that successfully achieves dimensionality reduction. By establishing a threshold, we identify pairs of features with significant linear correlations which are modelled as vertices and edges on a graph. Using graph theory principles, we can pinpoint non-essential features. By strategically removing these points, our method proceeds to streamline data management processes and enhances computational efficiency while outperforming Principal Component Analysis (PCA). © 2024 IEEE.

关键词： Dimensionality reduction

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Machine Learning Algorithms Applied with Questionnaire Dataset to Investigate Anxiety and Depression 6th

Machine Learning Algorithms Applied with Questionnaire Datas...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Verma, Richa Kumar, Gaurav Yadav, Akanksha Department of Computer Science and Engineering Rameshwaram Institute of Technology and Management Lucknow India

ISBN: (纸本)9789819778614

It is discovered that machine learning approaches and artificial intelligence are extensively examined in a variety of research articles when it comes to the identification of psychologically linked emotional activities. Studies employing datasets like face pictures, personal videos, audio, questionnaire-response, etc., demonstrated the usefulness of artificial intelligence and machine learning methods for the identification of human emotions like anger, happiness, sadness, etc. Human emotions are the result of psychological processes that may be impacted by external everyday routines. The questionnaire-based dataset used in the proposed study shows how anxiety and depression symptoms are configured. The DASS-21 questionnaire dataset includes a common set of questions on everyday activities. The experiment collects participants’ answers to the questions and then uses machine learning techniques to analyze each response. Textual data is used to compile responses from people. The features from the gathered responses are extracted using machine learning techniques. The model uses textual preprocessing units for the textual response dataset in order to more accurately categorize anxiety and depressive symptoms. The written responses have been translated into relevant portions that provide features for this module. The DASS-21 questionnaire-based dataset is subjected to the application of five distinct machine learning techniques in the current method. The model can examine characteristics against large datasets of complex text. The model does feature extraction and classification using methods including SVM, decision trees, random forests, and Naive Bayes. The model effectively compares the use of several algorithms for categorizing anxiety and depressive symptoms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees K-nearest neighbors Naïve Bayes Questionnaire dataset Random forest Support vector machines

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Comparitive study of LSTM and Ensemble models for Hindi text sentiment analysis 15

Comparitive study of LSTM and Ensemble models for Hindi text...

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

作者： Hanuman, Vishal Noel, Kevin Vangur, Shravani Shankar, R. Department of Computer Science and Engineering BMS Institue of Technology & Management Bengaluru India

ISBN: (纸本)9798350370249

Sentiment analysis, a common idea in big data analysis and natural language processing, tries to discern the emotion underlying the text and thoughts of the people. In the Indian Subcontinent, major communication is conducted in a number of languages other than English. These languages are frequently used to communicate opinions, and when such opinions are translated into English, their depth and significance are lost. The suggested study offers a method for analyzing the numerous sentiments found in literature written in different languages in their original tongues. The Hindi language is chosen to assess the functionality and performance of the model under consideration for the sake of simplicity in the demonstration. There are two different models that have been considered to understand the performance of the same. One is the LSTM model and the other was developed by implementing a stacking classifier. The stacking classifier combined different properties of various models into the base classifiers and fine-tuned them which aided in achieving a better accuracy than the previously existing models. The LSTM model showcases an accuracy of 89% whereas the stacking classifier showcases an accuracy of 96%. This can be useful in different applications including movie reviews, restaurant reviews, product reviews and so on. ©2024 IEEE.

关键词： Big data

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Enhanced Textual Image Classification with Ensemble Learning of MobileNetV2 and ResNet50

Enhanced Textual Image Classification with Ensemble Learning...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Tripathy, Sasmita Pattanaik, Satya Ranjan Rabindranath, Mohanty Hitesh Dash, Ratnakar Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India Gandhi Institute for Technology Department of Computer Science and Engineering Bhubaneswar India National Institute of Technology Rourkela Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350369175

Textual image classification is crucial in various applications, such as document digitization and automatic language identification. Although ensemble learning has been increasingly utilized to improve the accuracy of deep learning models, the optimal approaches for this task are still under-explored. This research evaluates the effectiveness of ensemble learning techniques for textual image classification, focusing on combining Convolutional Neural Networks (CNNs) like MobileNetV2 and ResNet50. The dataset used in this study includes images in multiple languages, adding complexity to the classification task. By leveraging ensemble learning, we enhanced the performance of these models, achieving an accuracy of 77%, surpassing the individual CNN models. These findings suggest that ensemble learning can significantly boost the effectiveness of deep learning models in textual image classification tasks. © 2024 IEEE.

关键词： Contrastive Learning

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A Multi-Faceted Approach for Underwater Sonar Rock vs Mine Classification Using Machine Techniques 1

A Multi-Faceted Approach for Underwater Sonar Rock vs Mine C...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Sahoo, Sasmita Mohanta, Raj Kumar Pani, Prangya Paramita Mohapatra, Sunil Kumar Chakravarty, Sujata Centurion University of Technology and Management Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

A significant challenge of underwater object classification distinguishing sonar rocks from buried mines is crucial in maritime and naval security, as the enemy country has planned explosive mines in the ocean. Furthermore, there is a possibility of encountering rocks. This research is motivated by the urgent need for reliable and efficient underwater detection methods, which have a variety of applications in environmental studies and maritime security. Many classification methods, from traditional algorithms to modern deep learning models, have been used to identify the unique patterns in sonar data. This study enhances the accuracy of the existing system capable of classifying between mines and rocks beneath the submarine to protect human resources and marine life. Our comprehensive approach integrates data preprocessing, feature extraction, and several machine learning classification techniques such as KNN, NB, SVM, Decision Tree, Random Forest, and neural network by utilizing the Sonar Mines vs. Rocks data to classify the object. The outcome of this research shows that a high accuracy of 98% was achieved in the LDA grid approach followed by 94 % in SVM (rbf), and 88-91% in neural network and boosting techniques. This research work also provides a smart web interface allowing users to input sonar data and receive real-time classification results. © 2024 IEEE.

关键词： Sonar

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Implementation on the Price Prediction and Analysis of Chicken Breast with Deep Learning Techniques 14th

Implementation on the Price Prediction and Analysis of C...

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14th International Conference on Frontier Computing, FC 2024

作者： Liu, Chao-Hung Chan, Yu-Wei Lin, Po-Yen Chang, Chih-Hung Yang, Chao-Tung Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering Providence University Taichung Taiwan Department of Computer Science and Communication Engineering Providence University Taichung Taiwan Department of Computer Science Tunghai University Taichung City Taiwan

ISBN: (纸本)9789819623945

The Taiwanese livestock industry grapples with escalating costs and challenges such as labor shortages, environmental changes, pollution, and disease threats. Trade liberalization and cheaper imports further highlight the need for industry modernization. In slaughter and food processing, predicting prices requires careful consideration of operational costs and pricing strategies. To analyze historical chicken meat prices, we compared ARIMA and LSTM models, common in time series forecasting. Additionally, we introduced STL-LSTM, combining STL and LSTM. These aimed for accurate real-time price predictions, with MSE used to measure accuracy. The study found that the LSTM model exhibited high predictive accuracy with the available data. Across various prediction horizons (1, 3, and 6 steps), the root mean squared error (RMSE) values were 3.09, 3.27, and 3.34, respectively, and the mean absolute percentage error (MAPE) values were 2.29, 2.95, and 2.92, respectively. These results surpassed those of the STL-LSTM and ARIMA models significantly. The LSTM model demonstrated capability in anticipating and signaling abnormal price fluctuations, offering valuable insights for pricing decisions within the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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