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Detection and Classification of Aggressive Comments and Hate Speech

Detection and Classification of Aggressive Comments and Hate...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Nemade, Srushti Mane, Sunil B. Nandgaonkar, Sushma Dept. of Computer Engineering and Information Technology Maharashtra Pune India

ISBN: (纸本)9798350323917

The prevalence of social media has led to an increase in aggressive online behaviour such as cyberbullying, trolling, hate speech, and flaming. The anonymity and convenience of social media platforms have allowed individuals to express their aggressive tendencies without fear of retaliation. This issue is not limited to certain sections of society and has impacted people globally. Cyberbullies now have the power to affect the lives of millions of people worldwide, which can be detrimental to the internet's importance as a communication and networking tool. Therefore, it is critical to recognize and restrict this negative behaviour. It is essential to detect and prevent cyberbullying and hate speech in Indian native languages, considering India's diverse population and multiple languages. Social media platforms have brought about significant benefits, including improved communication and networking, but the increased use of these platforms has led to an increase in harmful behaviour that can negatively impact mental health. Additionally, misinformation and propaganda can cause severe consequences for society. This paper discusses the detection and classification of aggressive comments in Hindi-English code-mixed and Hindi data from popular social media platforms such as Facebook, Twitter, and Instagram. © 2023 IEEE.

关键词： Social networking (online)

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Revolutionizing Mango Crop Health: A Deep Learning Approach for Precise Identification of Leaf Diseases 10

Revolutionizing Mango Crop Health: A Deep Learning Approach ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Navadharani, N. Mouthami, K. Devipriya, R. Anandamurugan, S. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798350384369

Plant diseases pose significant challenges to the agricultural sector, leading to decreased crop production and financial losses. Mango, being a popular fruit worldwide, is susceptible to various leaf diseases. Traditional diagnosis of these disorders requires labor-intensive and time-consuming manual picture interpretation. To address this issue, deep learning models have been utilized to enhance the precision and effectiveness of identifying leaf illness in mango crops. In this study, ResNet50, ResNext50, Convnet, and Conventv2 deep learning models were compared to find the most successful approach. The results demonstrated that the Conventv2 model achieved the highest accuracy of 98.21%. The major aim of the research work is improvising sustainable agricultural practices by leveraging advanced technology for accurate plant disease classification. The findings have implications for improving mango crop management and ensuring a more sustainable and efficient agricultural industry. © 2024 IEEE.

关键词： Fruits

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people's attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use Natural Language Processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： Natural language processing systems

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

引用

2024 International Conference on Science, technology, Engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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Comparative Analysis of Machine Learning Methods in Climate Data Extraction and Forecasting

Comparative Analysis of Machine Learning Methods in Climate ...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Khajgiwale, Parth Ajit Naveen, Atul Jethani, Nishtha Shitole, Ajitkumar International Institute of Information Technology Dept. of Computer Engineering Hinjewadi Pune India

ISBN: (纸本)9798331507541

Climate forecasting plays a critical role in understanding and mitigating the impacts of climate change. Advances in machine learning (ML) have significantly enhanced the accuracy of climate projections, particularly when dealing with large and complex datasets derived from regional climate models (RCMs) and global climate models (GCMs). This study investigates the application of various ML techniques for predicting precipitation based on temperature data extracted from climate datasets. We evaluate a range of regression models, including linear models and advanced ensemble methods such as Gradient Boosting and XGBoost, along with deep learning approaches like Convolutional Neural Networks (CNNs). Through a rigorous evaluation of these models, we focus on their performance as measured by Mean Squared Error (MSE) to identify the most effective techniques for capturing the intricate relationships within climate data. By analyzing existing studies and performance metrics, we aim to provide insights into the capabilities and limitations of these ML models in improving climate predictions. Furthermore, this research highlights the need for incorporating additional climate variables and optimizing model configurations to enhance predictive accuracy in climate forecasting. © 2025 IEEE.

关键词： Convolutional neural networks

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration 2

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Harshit Middha, Parth Thakur, Ayush Singh, Sofia Saraswat, Shipra Bhatia, Neha Amity University Dept. of Computer Science & Engineering Noida India Amity Institute of Information Technology Amity university Noida India Panipat Institute of Engineering & Technology Dept. of Information Technology Panipat India

ISBN: (纸本)9798331519056

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence. © 2024 IEEE.

关键词： Accelerated Training Hidden Weights Neural Networks Spectral Relaxation X-Iteration

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Scheduling of Flexible Manufacturing System with Penalty Cost Using Simulated Genetic Algorithm 15

Scheduling of Flexible Manufacturing System with Penalty Cos...

引用

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

作者： Singh, Vijai Singh, Varsha Inderprastha Engineering College dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India Indian Institute of Information Technology Allahabad dept. of Information Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350370249

This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling is considered to be a computationally hard problem. In this paper, various approaches are reviewed that are applied to the FMS scheduling problem. This paper also attempts to find a possible solution to the scheduling problem of FMS. To identify the order in which the various jobs would be assigned to a number of machines, this paper uses a genetic algorithm with Simulated Annealing to generate a machine sequence. In this, the developed tool requires tabular input. To obtain a schedule with minimum make span, many iterations of the genetic algorithm are applied. In this system, an optimal schedule is given for FMS by taking the makespan time and minimum penalty cost, and therefore calculating the minimum combined objective function. © 2024 IEEE.

关键词： Scheduling algorithms

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Study of the Process of Porous Materials Impregnation Using the Example of Leather Tanning 18

Study of the Process of Porous Materials Impregnation Using ...

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18th International Scientific and Technical Conference on Dynamics of Systems, Mechanisms and Machines, Dynamics 2024

作者： Damdinova, T.Ts. Radnaeva, V.D. Dept. Engineering and Computer Graphics Dept. Leather and Fur Technology. Water Resources and Commodity Research of Non-Food Goods East Siberia State University of Technology and Management Ulan-Ude Russia

ISBN: (纸本)9798331509019

The paper presents the results of the study of the process of porous materials impregnation from the point of view of percolation theory. Diffusion front, diffusion front shell, percolation cluster are determined using developed application. Processing of digital images obtained during the study of the porous object impregnation is used. This allowed obtaining the necessary quantitative characteristics of the diffusion front, percolation clusters and percolation shell. Quantitative characteristics were analyzed and kinetic curves of the impregnation process were constructed on the example of leather tanning process. As a result, the main stages of the impregnation process of the tanning composition: accumulation, percolation and distribution have been revealed. The methodology of the impregnation process study and the method of its visualization have been developed and applied to the processes of leather tanning. The universality of this development will allow applying them to the study processes in other porous materials, and in modeling and creation of new materials. © 2024 IEEE.

关键词： Leather

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An Automatic Generation of Test Cases in JUnit Testing Framework Using Genetic Algorithm 11

An Automatic Generation of Test Cases in JUnit Testing Frame...

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11th IEEE International Conference on Systems and Control, ICSC 2023

作者： Amarif, Mabroukah Alfitouri, Aisha Allag, Zaydan Sebha University of Libya Faculty of Information Technology Dept. of Information Systems Sebha Libya Sebha University of Libya Faculty of Information Technology Dept. of Computer Sciences Sebha Libya Sebha University of Libya Information Technology Faculty Dept. of Computer Sciences Sebha Libya

ISBN: (纸本)9798350304886

The testing phase is one of the most important phases of the software life cycle, which ensures the efficiency and quality of the software product. Many tests are conducted on the software, not only to detect errors, but also to provide a complete picture of the quality of the outputs provided by the program and to increase confidence in all of the functions. Due to the difficulty in estimating most of the test data and the time spent in this process, many techniques are used in the automatic generation of test cases. The genetic algorithm is one of the most important used methods in this process, and it has achieved great success in generating limited and appropriate test cases to give a complete conceptualization of the function under test. The Junit testing framework is used in automated testing processes within test-based development methodologies, but it lacks of a mechanism to specify and generate a specific test data that put the program under test in the required scenario. In order for the programmer to be able to perform the test automatically in an integrated way within JUnit testing framework, the genetic algorithm has been improved in this research and emerged into the JUnit testing framework to fully automatic testing and generate various numeric test cases at the unit-test stage. Based on testing of the proposed algorithm within the Junit testing framework, it has proven its effectiveness and facilitation of generating perfect specific numeric test cases. © 2023 IEEE.

关键词： Software testing

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