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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Umer, Qasim Naveed, Zunaira Lee, Choonhwa Ali, Asif Saeed, Malik Khizar Hanyang University Department of Computer Science Seoul Korea Republic of Institute of Southern Punjab Department of Computer Science Multan Pakistan Beijing Institute of Technology School of Automation Beijing China Comsats University Islamabad Department of Computer Science Vehari Pakistan

ISBN: (纸本)9798350313277

Coronavirus is a type of virus that can cause Respiratory Disease (RD) in people. The World Health Organization (WHO) states that signs and symptoms in mild cases include dry throat, fever, nasal secretions, shortness of breath, fever, and malaise. The disease is more dangerous than viruses and can cause serious illness. Although many researchers have tried various techniques for classifying RD patients, it is essential to identify the critical features before applying machine learning methods for classification to save time and cost. To this end, this paper proposes a Convolutional Neural Network (CNN) based Identification of RD patients (CNN-IRD) caused by Coronavirus and divides them into two classes, i.e., C19+ve and C19-ve. First, we apply the binarization technique to preprocess data into useful information. Second, we identify the significant features using Linear Discriminant Analysis (LDA). Finally, we train a deep learning classifier (CNN) with two publicly available datasets. The evaluation results suggest that CNN yields other classifiers in predicting RD patients. The performance improvements of CNN-IRD in accuracy, precision, recall, and f-measure with both datasets are (7.92%, 5.35%, 16.92%, and 11.22%) and (4.04%, 9.08%, 25.18%, and 17.25%), respectively. © 2023 IEEE.

关键词： Classification Convolutional Neural Network COVID-19 Decision Tree Linear Regression Respiratory

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KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection 31

KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News De...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Kumar, Anuj Kumar, Pardeep Yadav, Abhishek Ahlawat, Satyadev Prasad, Yamuna Department of Computer Science & Engineering Indian Institute of Technology Jammu India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (纸本)9798891762121

The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

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International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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Intrusion Detection Framework in IoT Networks

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SN computer science 2023年第4期4卷 1-17页

作者： Bajpai, Soumya Sharma, Kapil Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity University Madhya Pradesh Maharajpura Dang Gwalior India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The Internet of Things (IoT) deals with the internet and physical objects such as smart home automation, industrial applications, smart cities, health and fitness, environmental monitoring, etc. The IoT network is interconnected over the internet for data analysis and sharing. It has made it possible for people and organizations to accomplish more with less, both in terms of time and resources. However, this accomplishment of the IoT has also contributed to a worrying increase in IoT network threats. Botnet invasions are likely the most worrisome of these assaults. Attackers are also becoming more inventive as time and technology develop. To recognize these assaults and detect these incursions before they cause the system to become unresponsive, it is crucial to deploy stronger and more efficient machine learning technologies. By employing supervised learning and evaluating its overall performance, this work aims to design an intrusion detection framework in IoT networks using machine learning (IDFML). The proposed IDFML will examine the various performance measures for each of the different classifiers. Results show that the proposed IDFML has a 98.68% attack detection accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Internet of Things (IoT) Intrusion detection system (IDS) IoT20IDS Machine learning (ML)

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Real Time Fruit Recognition and Calories Estimation Using CNN and Model Deployment on Amazon's AWS 3

Real Time Fruit Recognition and Calories Estimation Using CN...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Roy, Vikas Kumar Roy, Ganpati Kumar Thakur, Vasu Baliyan, Nikhil Goyal, Nupur Chauhan, Yash Ruprecht Karl University of Heidelberg Department of Data and Computer Science Germany Sharda University Department of Computer Science and Engineering Greater Noida India RIT Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India Graphic Era Deemed to Be University Department of Mathematics Dehradun India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350303360

To carry out vigorous and effective fruit recognition in farming is difficult since there are a number of variations. Lately, various deep-learning approaches presented an outstanding result in numerous visual-guided agricultural usage. These approaches need labelling on training data, which is a tedious work. This study presented a customized five layers CNN architecture and various pretrained deep learning algorithms i.e., VGG-16, DenseNet-121 and InceptionV3are used to recognise the fruit and their calories for helping farmers, patients and peoples to estimate the fruit calories for their health and in farming. The model is trained and validated on integrated datasets which is downloaded from Kaggle and IEEE dataport having 10 classes of different fruits. After extensive experiments, highest accuracy obtained was 99.60% by five layers of CNN and proposed deep learning model has been deployed on Amazon AWS EC2 instance using WinSCP, PuTTY and PuTTYgen. Flask (Python Framework), HTML, CSS and JavaScript is used for website development and connected to its parent website which help users to recognize the fruit and their calories. © 2023 IEEE.

关键词： Fruits

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Securing SCADA System from DDoS Attack 2

Securing SCADA System from DDoS Attack

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Srivastava, Animesh Saini, Parveen Kumar Tiwari, Shweta Sawan, Vikash Garg, Navin Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Chandigarh University Department of Computer Science and Engineering Gharuan Punjab Mohali India University Institute of Engineering Chandigarh University Department Computer Science & Engineering Gharuan Punjab Mohali India Gla University Uttar Pradesh Mathura India Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350387933

Emergency response systems, water treatment facilities, wastewater collection systems, Oil and gas pipelines, electrical power transmission systems, wind farms, defence networks, and large-scale communication networks are all vulnerable to the increasing prevalence of this threat. of distributed denial of service (DDoS) attacks. A dynamic proxy defence method is required because static thresholds cannot protect a critical SCADA system from DDoS attacks. This proposed way to protect these services from attacks is by masking their IP addresses hidden behind the proxies. This technique allows for the swift removal of misbehaving or attacked proxies by having the service generate hidden paths on the fly. By removing the attacker's cover, this technique creates a secure network that is only accessible to authorized users. An attacker doesn't need to know the data proxy to disrupt ongoing client communications because it is hidden. This DDOS prevention architecture is a useful addition to existing DDoS defences, and it can be effectively implemented in SCADA systems, data servers, and data centres. Even when they only occupy 10% of the network, a dynamic proxy density of 0.5% is sufficient to suppress over 93% of attackers. © 2024 IEEE.

关键词： SCADA systems

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Optical Character Recognition System of Telugu Language Characters Using Convolutional Neural Networks 7th

Optical Character Recognition System of Telugu Language Char...

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7th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

作者： Charan, K.V. Pramod, T.C. Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Karnataka Tumkur India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur India

ISBN: (纸本)9789811954818

In pattern recognition, recognition of characters plays a crucial role. Optical Character Recognition (OCR) is a challenging problem for many decades. Identifying handwritten characters is an easy task for humans, but it is hard for a computer. Exploration in character recognition is very popular for many applications such as in banks, processing of languages, library automation, and designs of multi-media. An OCR system contains an optical scanner to scan the text and advanced software for interpreting images. Recognition of Handwritten Telugu Characters is strenuous as discrete handwritten scripts may have discrete styles of writings, slants, etc. The main purpose of optical character recognition is to make editable documents or images. In this paper, we have used deep learning techniques like Convolutional Neural Networks for recognizing Telugu characters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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