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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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PropFND: Propagation Based Fake News Detection

PropFND: Propagation Based Fake News Detection

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International Conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Verma, Pawan Kumar Agrawal, Prateek Department of Computer Engineering & Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9789811948305

In the recent years, social media acts as double-edged sword for the society as it is being used for exchanging real as well as fake news. Large number of researchers are involved for the detection of fake news using user credibility, content and propagation-based features. This paper proposes PropFND (Propagation based Fake News Detection) model to classify news as real or fake based on the combination of propagation pattern and user profile features. For the training of proposed model, we used combined features and applied several classifiers and finally concluded that Support Vector Machine (SVM) gives an improved result. This proposed model gives the improved accuracy of 93.81% which is higher than state-of-the-art model. After experimental analysis we noticed that real news propagates for long duration as compared to the fake news. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Detecting and Locating Image Forgeries with Deep Learning 2

Detecting and Locating Image Forgeries with Deep Learning

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Singh, Kamred Udham Rao, Ankur Kumar, Ankit Varshney, Neeraj Chundawat, Pushpendra Singh Singh, Teekam School of Computing Graphic Era Hill University Dehradun India Rajashan Udaipur India Guru Ghasidas Vishwavidyalaya Department of Information Technology Chhattisgarh Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India SS engineering College Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

It is becoming more important to have reliable methods for identifying picture forgeries, particularly passive forging techniques like copy-move and splicing, since the proliferation of digital images across a variety of platforms has raised the urgency of the situation. The purpose of this research is to explicitly target passive forgery tactics by presenting a unique strategy that makes use of deep learning approaches for the purpose of accurately identifying modified areas inside photographs. The methodology that we have suggested makes use of Mobile Net and ResNet as feature extraction networks. It does this by extracting rich representations from picture data in order to identify complicated patterns that are suggestive of possible manipulations, such as copy-move and splicing manipulations. We make use of Mask R-CNN, which is a cutting-edge instance segmentation model, in order to precisely localize and quantify forged areas. This allows us to calculate the proportion of changed material that is included inside photos. © 2024 IEEE.

关键词： Deep learning

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Melanoma Boundaries Detection Techniques using Artificial Intelligence 3

Melanoma Boundaries Detection Techniques using Artificial In...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Velmurugan, K. Jayasakthi Srinivasan, P. Gayathri, A. Yuvarani, S. Jeppiaar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha School of Engineering Department of Cse Tamil Nadu Chennai India Department of Computer Applications Tamil Nadu Perambalur India

ISBN: (纸本)9781665462167

Melanoma is the 6th most successive disease in the United States, with more than 9000 individuals kicking the bucket every year. Fast acknowledgment of melanoma expands an individual's life expectancy, but further developed analytic advancements are as yet required. Skin injury limit anomaly, which addresses the "B"includes in the "ABCD rule", is viewed as a critical clinical component for the early discovery of melanoma. What's more, blue-white line structure evacuation additionally further develops the identification proficiency. In this paper, an AI based location method for distinguishing skin injury limit inconsistencies is presented. The technique involves extricating the skin injury from the dermoscopic images, recognizing the skin sore line, estimating line inconsistency, and prepared famous directed learning models called SVM, RF, DT, and gathering move learning calculations to distinguish line anomaly naturally, bringing about a true choice on whether the skin sore boundary is customary or unpredictable. The method produces excellent results, with 92.6 percent accuracy, 91.3 percent sensitivity, 92.5 percent specificity, and 95.1 percent F-Score, respectively. © 2023 IEEE.

关键词： Support vector machines

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An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure 4

An Automated Face Mask Detection System using Deep CNN on AW...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Pandey, Ankur Chaturvedi, Ankur Gupta, Manish Mannepalli, Praveen Kumar Kumar, Santosh Chhabra, Gunjan Manipal University School of Computer Science and Engineering Jaipur India Galgotia University Greater Department of Computer Applications Noida India Gla University Department of Electronics and Communication Engineering Mathura India India Era University Department of Computer Science Lucknow India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350300093

Face detection methods have usually been used mostly for faces that aren't covered up (non-masked faces). These include nose, lips, ears, chin, and eyes. Faces must be concealed by masks in various locations, including labs, hospitals, crime scenes, and areas with high levels of pollution. The global spread of coronavirus known as COVID-19 has disastrous effects on community wellbeing. This study aims to apply a model too complicated data, namely, image and real-time video image tasks for personal face identification with and without a mask. The healthcare industry is in a hazardous state. Numerous preventative measures, including the use of masks, are being set in place to restrict the spread of this sickness;WHO (World Health organization) highly encourages the use of a mask. This extremely large number of COVID-19 samples provides valuable information regarding the pandemic's progression, as well as a possible way to encircle it to prevent future transfers. To prevent the transmission of the virus, several preventative measures have been implemented, including the use of masks. using a face mask that prevents droplet transfer discharged into the atmosphere, on either hand, may help avoid the outbreak. As a solution, the purpose of this function will be to form an FMD (Face Mask Detection) model which can be used as part of an embedded imaging system. It is expected to enhance the detection rate and achieve an accuracy of more than 99%. © 2023 IEEE.

关键词： COVID-19

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Optimizing Regulatory Compliance in Supply Chains with Blockchain and Gaussian Process Regression 2

Optimizing Regulatory Compliance in Supply Chains with Block...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Tulasiram, D. Jebaseeli, S. Kanmani Adnan, Myasar M. Madhavi, Arelli Varshney, Neeraj Nandhini, T.J. CMR College of Engineering & Technology Department of CSE India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Increased regulation of the supply chain is another important issue of concern to industries in distinct countries as they struggle to implement the regulation in a network setting. This paper aims to explore ways to improve regulatory compliance through the application of the blockchain and machine learning model, namely Gaussian process regression. Blockchain is an accurate and permanent record-keeping system that can be applied to track the flow of items and associated activities in the supply chain. Compliance can also be enforced, and through smart contracts, analyses of various parameters such as obligations can be monitored and adjusted automatically through GPR which involves the analysis of data from systems that use blockchain to model compliance risks and predict compliance issues. Thus, using the above-mentioned technologies, a company can prevent compliance failures, meet the necessary standards and set up a constant process of improvement in the supply chain area. This research specifically defines the theoretical foundations, application, and advantages of blockchain and GPR in enhancing the compliance of supply chains with the regulations in this work. In this approach, case-study and use of examples show how this approach works in handling compliance issues in several industries. © 2024 IEEE.

关键词： Smart contract

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Multidimensional binary layered model for census prediction using machine learning techniques

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Asian Journal of Civil engineering 2024年第6期25卷 4877-4891页

作者： El-Salhi, Subhieh Albqowr, Hala Mahmoud Saleh Igried, Bashar Awwad, Sari Dittakan, Kwankamon Department of Computer Information System The Hashemite University Zarqa 13133 Jordan Surveying and Geomatics Engineering Department Al-Balqa Applied University Al-Salt 19117 Jordan Department of Computer Science and Its Applications The Hashemite University Zarqa 13133 Jordan College of Computing Prince of Songkla University Phuket 83120 Thailand

The development of the resource allocation process is based on essential factors, such as population size, which can be considered one of the most critical ones. Estimating the population size for a particular region, especially in developing countries, indicates the government’s authority to invest and utilize the resources efficiently. As a result, forecasting population size is one of the most essential requirements that guide the entire development process and, as a result, affects human well-being. This paper presents a novel technique for census seven prediction of a specific region of interest in the context of satellite images. The Multidimensional Binary Layered Model (MDBL) is a representation technique founded on identifying a given region of interest in terms of the geographical nature of the local surrounding regions. The MDBL is then employed to generate an "effective" classifier using different machine learning techniques such as the Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Support Vector Machine (SVM). The Black Widow Optimization (BWO) method has been used to maximize performance and provide the best machine learning model. In order to produce the best prediction model, this optimization method ensures the models are tuned with the most appropriate hyperparameters. To this end, Al-Karak City, in Jordan, has been used as a case study to evaluate the proposed model. The evaluation of the classifiers has been reported in terms of the accuracy and the Area Under Curve (AUC) measurements. The CNN achieved the “best” overall AUC result of 88%, indicating the significant impact of using the proposed MDBL to generate an accurate, effective classifier. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Binary layered model Black widow optimization (BWO) Census prediction Classification CNN Machine learning

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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High-dimensional nonlinear variable separation solutions and novel wave excitations for the(4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation

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Communications in Theoretical Physics 2024年第11期76卷 1-11页

作者： Zu-feng Liang Xiao-yan Tang Wei Ding Department of Physics Hangzhou Normal UniversityHangzhou 310036China School of Mathematical Sciences Key Laboratory of Mathematics and Engineering Applications(Ministry of Education)&Shanghai Key Laboratory of PMMPEast China Normal UniversityShanghai 200241China School of Computer Science and Technology Shanghai Normal UniversityShanghai 200234China

Considering the importance of higher-dimensional equations that are widely applied to real nonlinear problems,many(4+1)-dimensional integrable systems have been established by uplifting the dimensions of their corresponding lower-dimensional integrable ***,an integrable(4+1)-dimensional extension of the Boiti-Leon-Manna-Pempinelli(4DBLMP)equation has been proposed,which can also be considered as an extension of the famous Korteweg-de Vries equation that is applicable in fluids,plasma physics and so *** is shown that new higher-dimensional variable separation solutions with several arbitrary lowerdimensional functions can also be obtained using the multilinear variable separation approach for the 4DBLMP *** addition,by taking advantage of the explicit expressions of the new solutions,versatile(4+1)-dimensional nonlinear wave excitations can be *** an illustration,periodic breathing lumps,multi-dromion-ring-type instantons,and hybrid waves on a doubly periodic wave background are discovered to reveal abundant nonlinear structures and dynamics in higher dimensions.

关键词： (4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation variable separation solution periodic breathing lumps multi-dromion-ring-type instanton hybrid waves on a doubly periodic wave background

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