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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak Sanskaar Srivastava School of Aerospace Mechanical and Mechatronic Engineering University of Sydney Sydney New South Wales Australia Department of Engineering Sciences ABV-Indian Institute of Information Technology and Management Gwalior India Student in Computer Science ABV-Indian Institute of Information Technology and Management Gwalior India Student in Chemical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

In the field of plant-based disease diagnosis, convolutional neural networks (CNNs) have become an essential tool due to their exceptional ability to recognize sick foliage. With an emphasis on bell pepper plants specifically, this study provides a thorough examination of modern plant disease detection technologies. Several modeling techniques were investigated, with a particular emphasis on neurons and layers, by utilizing CNNs that were trained on a carefully selected dataset of photos of bell pepper plants. Since that early disease identification has a direct impact on crop quality, yield, and economic viability, its importance in agriculture cannot be emphasized. Our methodology, which makes it easier to identify sick bell pepper plants quickly, is a key component of proactive management plans that reduce agricultural losses and stop the spread of disease. This study emphasizes how critical it is to eliminate diseased plants as soon as possible in order to prevent illness from spreading across the garden and preserve crop health in general. As a result of these efforts, our CNN model was able to attain an incredible 96.5% accuracy, highlighting its potential to be a game-changing instrument in contemporary agriculture.

关键词： Training Plant diseases Accuracy Plants (biology) Neurons Crops Agriculture Convolutional neural networks Standards Testing

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Asteroid Hazard Prediction Using Machine Learning: A Comparative Analysis of Different Algorithms

Asteroid Hazard Prediction Using Machine Learning: A Compara...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Tushar Sharma Shamneesh Sharma Ajay Sharma Aman Kumar Arun Malik Abhimanyu Sharma Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited India

The prediction of asteroid risk is a crucial topic in the realm of space technology. The utilization of machine learning techniques has enabled the identification of asteroids in photographic and video data obtained from telescopes and satellites, prediction of their trajectory, simulation of their potential impact on Earth, assessment of the probability and magnitude of such an impact, and provision of support to decision-makers in the event of an impact. The implementation of these techniques can enhance our comprehension and preparedness for prospective asteroid collisions, along with our capacity to effectively rank initiatives and distribute resources targeted at mitigating the gravity of such an impact. Furthermore, the utilization of these techniques can enhance our readiness for prospective collisions with asteroids. The present study introduces a novel model for predicting asteroid hazards, which incorporates multiple variables such as asteroid dimensions, speed, path, and atmospheric conditions. The employed methodology involves the utilization of machine learning algorithms to scrutinize past records of asteroid collisions and replicate plausible impact scenarios. The training process involves utilizing 70% of the available data for training the model, while the remaining 30% is reserved for testing purposes. By implementing these methods in current research, researchers have achieved a noteworthy accuracy of 99.99% in the task of classifying asteroids. The model is subjected to comparative analysis with alternative methods, and it exhibits superior precision in comparison to the latter.

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An AI-Centred Adaptive Cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Beyond Traditional Methods: Exploring Deep Learning Techniques for Accurate Traffic Projection

Beyond Traditional Methods: Exploring Deep Learning Techniqu...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Avinash Jha Shresthi Singh Shamneesh Sharma Isha Batra Arun Malik Saira Muzzaffar Yadaiah Nirsanametla School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Predicting traffic flow is a crucial component of the intelligent transportation system. This is a precise projection of the volume of traffic in a specific area at a specific future period. Researching traffic forecasts is beneficial in reducing traffic and facilitating safer, more affordable travel. Although classic models make use of shallow networks, the number of cars has grown exponentially in recent years, making these traditional machine learning techniques inapplicable to contemporary situations. We discuss some of the most recent developments in deep learning for traffic flow prediction in our article. The Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto are among the many deep learning designs. Encoder (SAE). These deep learning models gradually extract higher level information from raw input by using numerous layers. The most recent deep learning models created to address this specific issue are examined, and because transport networks are complicated, this review also informs the reader about the aspects that affect these models and which models perform best in particular situations.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Market research Convolutional neural networks Data mining Automobiles Long short term memory Intelligent transportation systems

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Need for Integration of Blockchain Technology in Supply Chain Management of Health Supplements

Need for Integration of Blockchain Technology in Supply Chai...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sahibdeep Singh Arun Malik Isha Batra Shamneesh Sharma Poongodi M Department of Computer Science Engineering Lovely Professional University Jalandhar Punjab India Delivery and Student Success UpGrad Campus UpGrad Education Private Limited India College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

By providing transparency, immutability, and security in the tracking of products from raw materials to end-users, blockchain technology has the potential to revolutionize the supply chain management of health supplements. Blockchain technology has the potential to revolutionize the supply chain management of health supplements. The decentralized structure of blockchain makes it possible for the information to be shared among stakeholders in a way that is both secure and unobtrusive. This helps to reduce the probability of counterfeit goods being sold and ensures that regulatory requirements are met. The uses of blockchain technology in the health supplement sector are investigated in this article. These applications include traceability, inventory management, and payment processing. In addition to this, it offers a discussion of the difficulties and constraints that may arise from applying blockchain technology to the management of the supply chain for nutritional supplements, as well as potential solutions for overcoming these obstacles. In the health supplement industry as a whole, the implementation of blockchain technology has the potential to promote transparency, accountability, and trust, which would ultimately be to the advantage of both customers and enterprises. The promising findings of this study done thus far highlight the necessity of future research and the use of blockchain technology in the health supplement business.

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HTML Smuggling: Attack and Mitigation

HTML Smuggling: Attack and Mitigation

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Security of Information and Networks (SIN), International Conference on

作者： Keshav Kaushik Govind Sharma Preeti Narooka Gunjan Chhabra Ankit Vishnoi School of Computer Science University of Petrolem and Energy Studies Dehradun India Student School of Computer Science University of Petrolem and Energy Studies Dehradun India Department of AIML School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of CSE Graphic Era Hill University Dehradun India Computer Science and Engineering Symbiosis Institute of Technology (SIT) Pune Symbiosis International (Deemed) University Pune India

HTML smuggling is a type of web-based attack that exploits vulnerabilities in web applications to deliver malicious content to unsuspecting users. This attack has become increasingly popular in recent years, as cybercriminals continue to look for new ways to circumvent security measures and deliver malware to users. Mitigating HTML smuggling attacks is a complex task, as it requires a deep understanding of web application security and the ability to identify and mitigate vulnerabilities in web applications. In this research paper, the authors provide a comprehensive overview of HTML smuggling attacks and their mitigation. They explore the techniques used by attackers to carry out the attack, including the use of steganography and obfuscation to hide malicious code within legitimate HTML code. They also discuss the impact of the attack on web applications and users, including the delivery of various types of malwares.

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Weather Prediction on Different LSTM Techniques for Time Series Data in Chennai, India

Weather Prediction on Different LSTM Techniques for Time Ser...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Harikishan Tk S Sasikiran G Manoj Kumar Ferdin Joe John Joseph Mithileysh Sathiyanarayanan Student of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Thai-Nichi International College Thai-Nichi Institute of Technology Bangkok Research & Innovation MIT Square London

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The prediction of weather has always been an integral factor in our lives as it determines if the day of activity leads to favorable or unfavorable conditions. The existence of different deep learning models has facilitated several major problems which without such techniques would have been a very time-consuming and exhaustive process. In this following paper, we explore various types of LSTM models and analyze which among them provides the best fit for the Time Series data that is present in the Weather data. Upon gathering the datasets, a strategy called Long Short-Term Memory (LSTM) is carried out. LSTM is a deep learning model which is primarily focused on time-series data. By employing such technical improvisations on the same data with multiple models each with their own unique characteristics, we can ultimately determine the best model which has an overall better accuracy. Based on the metrics produced by employing each technique, it is found that our LSTM model which proposed here has the best performance in terms of predicting the data with an overall low error rate and loss during the training phase.

关键词： Deep learning Training Measurement Error analysis Time series analysis Weather forecasting Predictive models

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Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

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Architecture Design of B5G and 6G Millimeter-Wave Radio Access Network: Using Wireless Communications to Increase Coverage, Capacity and Performance 16

Architecture Design of B5G and 6G Millimeter-Wave Radio Acce...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Al-Jumaily, Abdulmajeed Alrubaee, Saif H. Jiménez, Víctor P. Gil Al-Saegh, Ali M. Mahmood, Ahmed A. Al-Jumeily, Dhiya Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Madrid Spain University of Technology Department of Registration and Student Affairs Baghdad Iraq Middle Technical University Department of Space Technology Engineering Baghdad Iraq Universidad Carlos Iii de Madrid Department of Electrical Engineering Electronics and Automation Madrid Spain Liverpool John Moores University School of Computer Science & Mathematics Liverpool United Kingdom

ISBN: (纸本)9798350381344

In the Beyond-5G (B5G) and sixth generation (6G), several advanced techniques are based on high-frequency millimeter-wave wireless communications. This paper proposes the design of a novel B5G and 6G wireless radio access network (RAN) architecture. This architecture aims to be a key enabling factor for significantly increasing network capacity, promoting the deployment of new services, and integrating with the evolution of the previous cellular wireless access network architecture. Furthermore, the proposed architecture is adaptable to the various characteristics of high-frequency millimeter-wave communication. It leverages on the allocation of each layer resources of the different networks for more efficient deployment. © 2023 IEEE.

关键词： Millimeter waves

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