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Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

International Journal of Information technology (Singapore) 2023年第1期15卷 87-100页

作者： Uthirapathy, Samson Ebenezar Sandanam, Domnic Department of Computer Applications National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India Department of Computing Science and Engineering Vignan’s Foundation for Science Technology & Research Vadlamudi Andra Pradesh Guntur 522213 India

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer applications and Management.

关键词： Cuckoo search Forest fire algorithm Fuzzy C-means clustering Information diffusion Opinion analysis Social network

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in Information technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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Research on Error Handling Techniques for Speech Interaction 3

Research on Error Handling Techniques for Speech Interaction

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3rd IEEE International Conference on Computer Systems, ICCS 2023

作者： Ma, Rui Zou, Xuechao Tao, Pin Tu, Yixiang Qinghai University Department of Computer Technology and Applications Xining China Ministry of Education Key Laboratory of Pervasive Computing Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350313666

With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech interaction systems when faced with recognition or comprehension challenges. A case study focusing on the common task of 'electronic spreadsheet editing' is conducted, wherein an experimental environment is developed by combining an open-source neural network code repository with a third-party speech recognition interface. Drawing from historical experience, a 'triplet' paradigm is constructed to parse user command texts, and an improved approach for intelligent selection of error recovery strategies is proposed. This approach automatically determines the appropriate strategy for different speech interaction errors based on user logs. Experiments on 20 users demonstrate that our proposed intelligent error handling techniques for speech interaction, exhibit performance similar to empirically designed systems in practical tasks. Moreover, they significantly enhance user satisfaction and task completion efficiency when utilizing the system. © 2023 IEEE.

关键词： Speech recognition

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Comparative Analysis of Blowfish and Twofish Cryptographic Protocol 5

Comparative Analysis of Blowfish and Twofish Cryptographic P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rawat, Bhupender Singh Singh, Anushree Sharma, Hemendra Shankar Saxena, Megha Verma, Pooja College of Smart Computing COER University Roorkee India Invertis University Department of Computer Applications Bareilly India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun248007 India

ISBN: (纸本)9798331530013

In today's interconnected world showcasing technical advancement, the exchange of information is conducted through the network as a communication medium at the click of a button. Still, the problem is that the medium we use as communication channel for transferring personal or confidential data is secure and original. So, the confidentiality and integrity of the data transferred over the network are ensured by Cryptography. This paper is conducted to assess the strengths and weakness of Blowfish and Twofish algorithm in different scenarios. The main objective is to provide decision-making as well as comprehensive insight into the application and suitability of both algorithms. The finding of this research is to understand the dependency of security on Cryptography, facilitating informed choice in cryptographic protocol design and deployment. © 2024 IEEE.

关键词： Data transfer

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Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

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Computer Systems Science & Engineering 2023年第1期44卷 859-872页

作者： Fawaz Alassery Ahmed Alzahrani Asif Irshad Khan Kanika Sharma Masood Ahmad Raees Ahmad Khan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Applications Mangalmay Institute of Engineering&TechnologyGreater Noida201310Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

In recent years,it has been observed that the disclosure of information increases the risk of *** restricting the accessibility of information,providing security is ***,there is a demand for time tofill the gap between security and accessibility of *** fact,security tools should be usable for improving the security as well as the accessibility of *** security and accessibility are not directly influenced,some of their factors are indirectly influenced by each *** play an important role in bridging the gap between security and *** this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and *** significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle *** calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare *** study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

关键词： Information security big data big data security life cycle fuzzy AHP

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Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 136-146页

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Mughaid, Ala Taamneh, Salah Wedyan, Fadi I. Obiedat, Mamoon Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University RomeovilleIL United States

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences. © 2023 by the authors of this article. Published under CC-BY.

关键词： Internet of things

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A Study on Short-Term Nowcasting Precipitation Forecasting Methods Based on Radar Echo Maps 8

A Study on Short-Term Nowcasting Precipitation Forecasting M...

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8th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Liu, Jie Wang, Xingchi Zhang, Guojing Qinghai University Department of Computer Technology and Applications Xining China Qinghai University Qinghai Provincial Laboratory for Intelligent Computing and Application Xining China

ISBN: (纸本)9798331517915

Short-To-medium-term precipitation nowcasting aims to predict changes in mesoscale and microscale weather systems, particularly severe convective systems, within 0 to 2 hours. Compared to large-scale weather systems, mesoscale and microscale systems are characterized by their abruptness, complex mechanisms, and short life cycles, making short-term precipitation forecasting a highly challenging field. This study focuses on the Yinchuan region as a case study, processing radar echo data through dimensionality reduction, noise reduction, frame interpolation, and data augmentation to construct datasets suitable for training and testing. The IAM4VPx5 model is employed for forecasting and is compared against traditional spatiotemporal prediction models, including SimVP, ConvLSTM, and ConvGRU. Experimental results demonstrate that the IAM4VPx5 model outperforms SimVP, ConvLSTM, and ConvGRU across multiple metrics, such as Critical Success Index (CSI), Heidke Skill Score (HSS), and Probability of Detection (POD). Additionally, IAM4VPx5 exhibits superior performance in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Overall, the IAM4VPx5 model demonstrates exceptional performance compared to SimVP, ConvLSTM, and ConvGRU in various aspects. These results validate the effectiveness and advantages of the IAM4VPx5 model in processing radar echo data, offering an efficient solution for weather radar forecasting. © 2025 IEEE.

关键词： Data Augmentation IAM4VPx5 Model Precipitation Forecasting Radar Echo Spatiotemporal Prediction

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