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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Enhancing Multi-label text classification using adaptive promptify concepts 15

Enhancing Multi-label text classification using adaptive pro...

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

作者： Reddy, Veerababu Uppukonda, Usha Rani Veeranjaneyulu, N. Vignan's Foundation for Science Technology & Research Dept. of Computer Science & Engineering Guntur India Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India Vignan's Foundation for Science Technology & Research Dept. of Information Technology Guntur India

ISBN: (纸本)9798350370249

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt Engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models. © 2024 IEEE.

关键词： Fine-Tuning GPT Multi-Label Text classification NLP (Natural Language Processing) Pre-trained models Prompt Engineering Transformers

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Neural Networks for Natural Language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, Devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural Language Processing (NLP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NLP recurrent neural networks RNNs

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A Real-Time Road Crash Prediction Model by Hybridizing Multiple Learning Classifiers 4th

A Real-Time Road Crash Prediction Model by Hybridizing Multi...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Arun, G. Anuguraju, K. Sangeetha, A. Babu, K. Trichy India Department of Computer Science and Engineering Gnanamani College of Technology Pachal Namakkal India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789819914784

The majority of critical injuries caused by road accidents occur in developing nations, where they are a major global concern. Due to these traffic incidents, many individuals have lost loved ones. Consequently, a system that has the potential to save lives is needed. The approach identifies key contributing factors for accidents or establishes a connection between accidents and different causes of accidents. This study suggests a system for predicting accidents that can assist in analyzing potential safety concerns and foretelling whether an event will happen or not. In order to identify key qualities and forecast accident risk in single- and multiple-crashes, this research provides a hybrid feature selection-based classification-based machine learning (ML) method. It took into account classifiers like the random forest, k-nearest neighbor, and XGBoost. A few evaluation criteria, including precision, root mean square error, accuracy, recalls, and receivers operating output characteristics, were used to assess these classifiers. When the accuracy of the ML algorithms was compared, the accuracy of the proposed model attained as 99.78%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Advanced sound-based pest detection in agriculture using deep learning and adaptive optimization

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International Journal of information technology (Singapore) 2025年 1-11页

作者： R, Sahana Lokesh SailajaKumar, K. S.Soundariya, R. K S, Kavitha Reshma, S. Department of Computer Science & Engineering (Cyber Security) Ramaiah Institute of Technology Bengaluru India SoCSE RV University Bangalore India Department of Computer Technology Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bengaluru India

Implementing sound-based pest detection systems in large fields comes with challenges, including the complexity of acoustic data, interference from environmental noise, and the need for scalable, real-time solutions capable of processing data over vast areas. To address these challenges, we propose an innovative pest detection system that integrates advanced sound analytics with deep learning techniques. This study proposes a feature selection technique that includes the real-valued discrete Gabor transform algorithm and linear frequency Cepstrum coefficients (LFCC) algorithm, which helps analyze the frequency spectrum and capture critical features. The approach operates in four stages: pre-processing, feature selection, feature matrix generation and sound classification. This study proposes primarily, that data is collected and pre-processed, followed by the feature selection technique including the Real-Valued Discrete Gabor Transform algorithm and Linear Frequency Cepstrum Coefficients (LFCC) algorithm, which help analyze the frequency spectrum and capture critical features. These selected features are then generated as a feature matrix. After that, the pest sound is classified using Modified EfficientNet. To optimize the model’s parameters and enhance its performance and accuracy, the Hippopotamus Optimization Algorithm (HOA) is integrated with EfficientNet. The dataset was utilized to validate the suggested approach, with accuracy levels of over 98% and specificity is 97%, respectively. These results demonstrate superior classification efficiency compared to existing pest detection systems. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Feature matrix Hippopotamus Optimization Algorithm Linear Frequency Cepstrum Coefficients algorithm Modified EfficientNet Real-Valued Discrete Gabor Transform algorithm

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Judicial System in Bangladesh Using Distributed Blockchain technology 3

Judicial System in Bangladesh Using Distributed Blockchain T...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Rakibul Hasan, M.M. Kashem, Mohammod Abul Fatin, Md. Al-Abrar Roni, Md. Mainul Islam Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

The current judicial system process, which is conducted manually in Bangladesh, is characterized by a high degree of uncertainty and risk, as well as a wide range of possibilities for fraudulent documents and the preservation of the ownership details of cases. Therefore, the current system does not appear to be feasible, effective. We have created our proposed blockchain-based judicial management system with the SHA-256 data encryption technique. This system will ensure the safety of the case transactions and will be able to store, authenticate, and maintain entire pertinent information of the case document. The mentioned architecture will be able to revoke and to detect attempts to change the case by using a variety of technologies, including IPFS, Proof of Stack (PoS), and public-key cryptography. These techniques will lead to subsequent more efficient solutions than the existing systems and improve how the general public views Bangladesh's judicial system. © 2024 IEEE.

关键词： Blockchain

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Enhancing the Performance of Detect DRDoS DNS Attacks Based on the Machine Learning and Proactive Feature Selection (PFS) Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第2期49卷 511页

作者： Nuiaa, Riyadh Rahef Manickam, Selvakumar Alsaeedi, Ali Hakem Alomari, Esraa Saleh Universiti Sains Malaysia Malaysia Department of Computer College of Education for Pure Sciences Wasit University Iraq College of Computer Science and Information Technology Universitas of Al-Qadisiyah Iraq

The Domain Name System (DNS), which converts domain names to IP addresses, is a critical component of the internet infrastructure. Attackers exploit the existing potential vulnerabilities in this network protocol to launch their attacks. Distributed Reflection Denial of Service (DRDoS) DN S attacks are a type of Distributed Denial of Service (DDoS) attack that uses DNS vulnerabilities to carry out their attacks. These types can rapidly deplete the resources of the targeted victim system (computational and bandwidth). With the advancement of attack methods, both quantitatively and qualitatively, traditional methods used to detect DNS threats, particularly DRDoS attacks, became ineffective. Therefore, in this paper, a new model called proactive feature selection model PFS for early detection of DRDoS attacks based on DNS responses. The PFS model is divided into two stages: features selection stage and detection DRDoS attacks stage. The PFS model was validated using the standard CICDDoS2019 dataset. The results show that the PFS model achieves a high accuracy of 91.4368% and a very low FPR while reducing the number of features from 88 to 19 in the standard CICDDoS2019 dataset © 2022. IAENG International Journal of computer *** Rights Reserved.

关键词： Denial-of-service attack

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Enhancing Security and Privacy in Cloud-Based Medical Data Systems using AES Cryptography and Digital Envelopes

Enhancing Security and Privacy in Cloud-Based Medical Data S...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Pendam, Triveni Baig, Mirza Moiz Sonekar, Shrikant Sawwashere, Supriya S. Jd College of Engineering and Management Department of Computer Science & Engineering Maharashtra Nagpur India Jd College of Engineering and Management Department of Information Technology Maharashtra Nagpur India

ISBN: (纸本)9798350348460

The integration of healthcare data storage and management within cloud systems has revolutionized accessibility and administration in the medical field. This paper, proposes a security framework for medical data over the cloud. The medical data can be encrypted using AES algorithm. Preprocessing and data aggregation are pivotal in creating protected training files sent to the cloud. Cloud processing utilizes an SVM algorithm for data analysis, enabling informed medical decisions. The study findings affirm the system's robustness and security in handling diverse security and privacy concerns linked with cloud-based medical data. By employing digital envelopes and encryption standards, this system offers a dependable and efficient approach to accessing and processing healthcare information. © 2024 IEEE.

关键词： Big Data Cloud Computing Data Security Healthcare information System Internet of Things (Iot) Machine Learning Privacy-Preserving Data Sharing Secure Medical Support System

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AI-Based Vehicle Detection and Its Emission Impact on AQI 1

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19th Control Instrumentation System Conference, CISCON 2022

作者： Bhat, Suhas Sudhir Pai, M. D. Varun Holla, B. Ashutosh Pai, M. M. Manohar Department of Computer Science and Engineering Manipal Institute of Technology Manipal576104 India Department of Computer Science and Engineering Government Polytechnic Karkala Karkala574104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal576104 India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

Vehicle traffic has a significant impact on urban quality of life. One major impact is on the air quality of the surrounding area. With increasing vehicle traffic, air quality gets drastically affected in urban cities. A dense accumulation of vehicles is commonly observed across a network of roads during peak hours, drastically affecting the air quality index of the surrounding environment. Adverse measures and prevention are required to control air quality to maintain a healthier environment. Hence, determining vehicle emission details is necessary to address the overall impact in the surrounding areas. For any organization/gated campus, it is required to minimize the air pollution caused by vehicles that regularly visit the campus. By identifying the vehicles appearing on the campus premises, their emission impact on the surrounding can be determined using the PUC certificate of the vehicle. This study performs a vehicle emission impact on a gated campus using a deep learning approach. Real-time surveillance footage is processed with a deep learning model to detect vehicle and its license plate. Furthermore, air quality sensors deployed at strategical locations provide real-time data on pollutant concentration. This combined information is further utilized by a Web application to provide statistics of AQI in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus

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computers, Materials & Continua 2022年第1期70卷 1089-1109页

作者： Tehreem Aslam Ayesha Maqbool Maham Akhtar Alina Mirza Muhammad Anees Khan Wazir Zada Khan Shadab Alam Department of Computer Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Management Studies Bahria Business SchoolIslamabad44000Pakistan Department of Computer Science Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computer Science and ITJazan UniversityJazan67714Saudi Arabia

Enterprise Resource Planning(ERP)software is extensively used for the management of business *** offers a system of integrated applications with a shared central *** all business-critical information in a central place raises various issues such as data integrity assurance and a single point of failure,which makes the database *** paper investigates database and Blockchain integration,where the Blockchain network works in synchronization with the database system,and offers a mechanism to validate the transactions and ensure data *** research exists on Blockchain-based solutions for the single point of failure in *** established in our study that for concurrent access control andmonitoring of ERP,private permissioned Blockchain using Proof of Elapsed Time consensus is more *** study also investigated the bottleneck issue of transaction processing rates(TPR)of Blockchain consensus,specifically ERP’s *** paper presents systemarchitecture that integrates Blockchain with an ERP system using an application interface.

关键词： Transaction security enterprise systems blockchain transaction integrity hyperledger sawtooth PoET consensus

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