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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sangeetha, K. Vishnu Raja, P. Siranjeevi, S. Suman, J. Rohith, S. Kongu Engineering College Faculty of Computer Science and Engineering India Kongu Engineering College Cse Department India

ISBN: (纸本)9798350385649

Fruit classification is an indispensable component of the modern world, with applications ranging from agriculture and food production to retail and distribution. Accurate classification of fruits ensures quality control and helps in streamlining supply chains. However, fruit classification is a complex endeavor, primarily due to the intrinsic diversity of fruits in terms of size, shape, color, and other characteristics. The challenge intensifies when the goal is not only to identify fresh fruits but also to detect and classify rotten or spoiled ones. The existing models and systems designed for fruit classification have been proficient in categorizing fresh, visually appealing fruits. These models have found widespread utility in industries such as agriculture and supermarkets, where the goal is to separate fruits that meet certain quality standards. However, they fall short when it comes to addressing the critical issue of identifying and classifying fruits that are no longer fit for consumption, which is equally important to prevent waste and maintain quality control. To bridge this gap, this project develops a comprehensive approach. It begins with the acquisition of a dataset that includes both fresh and rotten fruits. By combining the power of deep learning, specifically Convolutional Neural Networks (CNN), the project aims to classify fruits into distinct categories. The CNN model is trained to differentiate between fresh and rotten fruits by learning from a diverse set of images. In addition to classification, the project employs the capabilities of OpenCV, a popular computer vision library, to assess the ripeness of fruits based on the color. OpenCV provides a robust platform for analyzing color variations in fruit images. By leveraging this color analysis, the project can not only classify fruits but also determine their ripeness levels, providing a more holistic evaluation of fruit quality. The integration of CNN -based classification and OpenCV-driven ri

关键词： Convolutional neural networks

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Forecasting Power Prices for Cloud Computing using an Enhanced Machine Learning 15

Forecasting Power Prices for Cloud Computing using an Enhanc...

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

作者： Nithisha, C. Tejaswini, Chillara Pavana Samyuktha, Busireddy Harika, Banda Chatrapathi, Gantala Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

The use of several machine learning regression models for the prediction of power costs is explored in this work. The following models are taken into consideration: Gradient Boosting, Adaboosting, Lgbmregressor, Cataboost regressor, Random Forest Regressor, XGBoost Regressor, Support Vector Regressor, and Stacking Regressor. Historical power pricing data with variables like time, demand, and weather make up the training and assessment dataset. A portion of the dataset is used to train each model, which is then adjusted to maximize prediction performance. To evaluate each model's precision, resilience, and computational effectiveness, a comparative study is done. Insights into the applicability of various regression models for the forecast of power prices are provided by the study's findings, which may help decision-makers optimize the planning and management of energy resources. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Stress Prediction using Machine Learning Algorithms 15

Stress Prediction using Machine Learning Algorithms

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

作者： Sivaiah, B.V. Gayatri, Tangi Setti Umadevi, Thati Reddy, Rajula Partha Siva Sravya, Modiboina Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Many people nowadays are dealing with psychological trauma. referred to as stress. Stress is defined as "a state of tension and pressure brought on by any thought or incident that makes you feel anxious, agitated, or frustrated". Many people have experienced stress as a result the current situation, particularly youth and working professionals. These days, a rise in stress can cause a number of issues, including heart attacks, depression, and suicide. Many academics have turned their attention to stress identification as mounting data indicates that stress related health illnesses associated with the demanding modern lifestyle are becoming more and more common. Consequently, this study's main objective is to predict stress levels, using user-retrieved data that is then confirmed using a subjective stress scale. In this case, a ML algorithm should be trained on the data to forecast the stress level. With the help of our stress prediction, you can quickly determine your current stress level and steer clear of any unfavorable circumstances. With the help of this approach, people may proactively handle future problems and avoid unfavorable consequences by having correct insights into their stress levels. The project's ultimate goal is to improve mental health by providing a tool for efficiently determining and controlling stress levels and encouraging a better way of living. © Grenze Scientific Society, 2024.

关键词： Diseases

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Tag Prediction of Stack Overflow Questions using NLP and ML 15

Tag Prediction of Stack Overflow Questions using NLP and ML

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

作者： Prasad, P. Yogendra Priyanka, Mylavarapu Amreen, Shaik Reddy, N. Desai Sreenivas Teja, Muppala Bhanu Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Technology is growing in great leaps and bounds and the number of innovations is increasing every day with major contributor being the advancement in the software development environment. Programming environment includes plethora of tools and support platforms that gives a complete assistance to the programming designers. These platforms can be question- answer based or coding based or tutorial based. Stack Overflow is a very popular online question- answer platform in the current generation and is growing to be the greatest question-answer gathering for programmers and software developers. The amount of information in this forum is so huge that one of the main functionalities of this platform would be tagging i.e., identifying the label or class to which the piece of information belongs to. Tagging helps in easy retrieval and organization of data. We propose a multi label classification system that labels all the questions on the forums with their appropriate tags autonomously. This auto-labelling framework makes it very easy for the users to find answers for the inquiries posted, given significant tags for the same and also helps the site in organizing their questions in a better way. © Grenze Scientific Society, 2024.

关键词： Software design

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Efficient Text Extraction and Summarization using Easyocr and GPT-3 15

Efficient Text Extraction and Summarization using Easyocr an...

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

作者： Sriharsha, A.V. Bhavana, Mekala Tejaswee, Syamala Ahmed, Mynasaheb Bushra Reddy, Peddi Reddi Jaipal Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Optical character recognition (OCR) extracts text from images while large language models (LLMs) understand and generate human-like text. OCR engines like EasyOCR transform document images into machine-readable text. But this raw extracted text contains artifacts. LLMs like GPT-3 require clean embedding vectors instead of text. We propose an integrated pipeline combining EasyOCR and GPT-3 for enhanced text comprehension from images. EasyOCR optically recognizes text from document images. The extracted text is cleaned and converted into embeddings that are fed to GPT-3. GPT-3's deep language model then provides contextual understanding of the extracted text. This enables interpreting complex concepts, resolving ambiguities, summarizing key ideas, and generating natural language descriptions. Our pipeline establishes a paradigm for augmenting text understanding through synergistic combination of OCR and LLMs. It has diverse applications in document analysis, information retrieval, question answering, and other NLP tasks involving documents. The integrated model outperforms previous approaches on benchmarks for comprehension of extracted text. It demonstrates the benefits of complementing optical text extraction with LLMs' innate language abilities. This paves the way for advanced OCR systems that not just read but also understand text in documents. © Grenze Scientific Society, 2024.

关键词： Deep neural networks

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Lithium mapping in a Mg-9Li-4Al-1Zn alloy using electron energy-loss spectroscopy

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Journal of Magnesium and Alloys 2023年第4期11卷 1170-1179页

作者： Yu Zhang Laure Bourgeois Yu Chen Zhuoran Zeng Nick Birbilis Philip N.H.Nakashima Department of Materials Science and Engineering Monash UniversityVIC 3800Australia Monash Centre for Electron Microscopy Monash UniversityVIC 3800Australia College of Engineering and Computer Science Australian National UniversityACT 2601Australia Faculty of Science Engineering and Built EnvironmentDeakin UniversityVIC 3125Australia

Magnesium-lithium alloys with high lithium content have been attracting significant attention because of their low density,high formability and corrosion *** properties are dependent on the distribution of lithium,which is difficult to map in the presence of *** this work,a ratio spectrum-imaging method with electron energy-loss spectroscopy(EELS)is demonstrated,which enables the mapping of *** application to LAZ941(Mg-9Li-4Al-1Zn in wt.%),this technique revealed that a key precipitate in the microstructure,previously thought by some to be Mg_(17)Al_(12),is in fact rich in *** result was corroborated with a structural investigation by high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM),showing this phase to be Al_(1-x)Zn_(x)Li,with x<<*** work indicates the potential offered by this technique for mapping lithium in materials.

关键词： Lithium mapping Magnesium alloys Electron energy-loss spectroscopy(EELS) High-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM)

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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Drug Targets Prediction of Mycobacterium tuberculosis H37Rv Using In Silico Genome Analysis 2

Drug Targets Prediction of Mycobacterium tuberculosis H37Rv ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Abhilash Mahapatra, Rajendra Prasad Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India

ISBN: (纸本)9798350364699

Mycobacterium tuberculosis, the causal agent of tuberculosis, is a major global health concern. The most widely studied strain for understanding the mechanism of drug resistance is H37Rv. To identify possible therapeutic targets, in silico analyses of Mycobacterium tuberculosis H37Rv genome were carried out. The genome sequence was downloaded from the website of NCBI (National Center for Biotechnology Information). To identify essential genes, the DEG (Database of Essential Genes) was consulted. Additionally, homology searches with the human genome were conducted using BLASTX. Out of a total of 3924 genes, 594 were determined to be essential for Mycobacterium tuberculosis, of which 366 had no sequence similarity to the human genome. Out of 366 genes, 242 potential drug targets have been found after being screened for fictitious and unidentified genes. © 2024 IEEE.

关键词： Genome

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