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A deep learning model based glaucoma detection using retinal images

Journal of Intelligent and Fuzzy systems 2024年第1期1卷 1-12页

作者： Ruby Elizabeth, J. Kesavaraja, D. Ebenezer Juliet, S. Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology T.M. Palayam Tamil Nadu Coimbatore India Department of Computer Science and Engineering Dr. Sivanthi Aditanar College of Engineering Tuticorin District Tamil Nadu Tiruchendur India Scope Vit Tamil Nadu Vellore India

The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achie

关键词： Ophthalmology

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Cryptography machine: Encryption and decryption model for multimedia data 1st

Cryptography machine: Encryption and decryption model for mu...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access is a top priority in today’s digital world. Cryptography is used to ensure secure communication over the internet, including email, instant messaging, and web browsing. Our paper’s primary goal is to develop a cryptography machine capable of encrypting and decrypting different data types, such as plain text, images, audio and videos using the cryptography of the symmetric key. The proposed model will be implemented using Python and Java. Developing the cryptography machine will significantly protect sensitive information and ensure its confidentiality and integrity. The machine’s ability to encrypt and decrypt different data types using symmetrickey cryptography will provide an added layer of security to individuals, organizations, and governments in the digital era. © 2025 the Author(s).

关键词： algorithms Cryptography decryption encryption keys machine

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年第2期12卷 1488-1499页

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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Rainfall Prediction using Machine Learning Techniques - A Comparative Approach 3

Rainfall Prediction using Machine Learning Techniques - A Co...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sivanantham, S. Kumar, P. Hemanth Vardhan, S. Naga Kumar, S. Chaithanya Kumar, T. Arun Pradeep, T. Department of Computer Science and Systems Engineering Tirupati India

ISBN: (纸本)9781665462167

India depends heavily on agriculture for its survival. Rainfall is crucial to agriculture. Predicting rainfall has become a significant issue recently. People are made aware of the possibility of rain and are better prepared to protect their crops by taking preventative measures when rain is predicted. Making an accurate rainfall prediction has always been challenging, but in recent years, because of climatic variability and change, it has become even more challenging. The effectiveness of classification systems for predicting rainfall has soared. For the purpose of predicting rainfall in the Indian states, this research employs a variety of categorization techniques. The classification algorithms are Logistic Regression by Lesso, Decision Tree, Light GBM, Extreme Gradient Boosting, Random Forest Method, K Nearest Neighbor Technique, Naive Bayes and Support Vector Machine Techniques. Dataset, which included several meteorological parameters, was obtained via Kaggle. With a testing data ratio of 70:30. Based on precision, accuracy, recall, f1-score, and execution time, the efficacy of the classification algorithms was assessed. There are few challenges with the existing system with data and with accuracy which are the major problems in predictions and to understand which algorithm is best especially for rainfall prediction our aim is to find measures for all possible algorithms and provide a conclusion saying the algorithm that fits for rainfall prediction. © 2023 IEEE.

关键词： Rain

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Android offloading computation over the cloud 1st

Android offloading computation over the cloud

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

ISBN: (纸本)9781032874036

This paper analyzes whether Android apps may outsource computational activities to cloud servers. Due to the complexity of mobile apps, shifting computing operations to the cloud has become popular to improve performance and reduce energy consumption. This paper explains how to deploy an Android app on Cloudflare and tests it under various settings. The testing showed that cloud computing can boost Android app efficiency. The findings show that shifting computing workloads to the cloud reduces mobile device CPU utilization. This frees up device resources, allowing it to perform other tasks. However, cloud processing has limitations. Network latency between the mobile device and the cloud server may affect application performance. In addition, the cost of offloading computation to the cloud can be a concern for some users, especially in high data consumption scenarios. Our findings can be helpful for developers who want to optimize the performance of their Android applications and for researchers who wish to investigate this topic further. Future research could explore ways to minimize the impact of network latency on offloading computation and investigate the cost-effectiveness of this technique for different types of applications. © 2025 the Author(s).

关键词： Cloud security

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A Survey on the Role of Complex Networks in IoT and Brain Communication

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computers, Materials & Continua 2023年第9期76卷 2573-2595页

作者： Vijey Thayananthan Aiiad Albeshri Hassan A.Alamri Muhammad Bilal Qureshi Muhammad Shuaib Qureshi Cybersecurity Department Faculty of ComputingEngineering and ScienceUniversity of SouthWalesWalesUK Department of Computer Science Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science&IT University of Lakki MarwatLakki Marwat28420KPKPakistan Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this *** benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication *** data traffic,huge capacity,minimal level of dynamic latency,*** some of the future requirements in 5G+and 6G communication *** emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain *** this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain ***,we measure the state of the complex system through *** 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of *** IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be ***,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and ***,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability *** this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex *** analyze the observability recovery,we can use a contraction detection algorithm with complex network *** survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection *** addition,we discuss the scalability of IoT communication using 5G+/6G

关键词： Complex networks emerging communication IoT based on 6G systems neuroscience photonic technology

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Suicide risk assessment using word-level model with dictionary-based risky posts selection

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Multimedia Tools and Applications 2024年第7期83卷 21435-21454页

作者： Tsai, Yun Sheng Chen, Arbee L. P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find relevant suicide posts. This problem becomes more serious in the feature-based post embeddings since each post is converted into a single vector to serve as the input of the model, resulting in the loss of word-level information during training. In this paper, we addressed this problem by introducing a novel word-level model including a post-selectin layer as a solution. Firstly, we utilize a suicide keyword dictionary to identify risky posts that may be missed by the post-level attention mechanism. We then convert the words in the risky posts into word embeddings and use self-attention to generate the post embeddings for the risky posts. Finally, we pass the post embeddings to a multilayer perceptron to classify the suicide risk. We also demonstrate that the FScore used in previous studies can be reduced to a function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score a nearly 4% improvement compared to previous studies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Embeddings

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FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

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Parallelizing Random and SAT-based Verification Processes for Improving Toggle Coverage

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IPSJ Transactions on System LSI Design Methodology 2023年 16卷 45-53页

作者： Hamaguchi, Kiyoharu Department of Information and Computer Science Faculty of Engineering Kyoto Tachibana University Kyoto607–8175 Japan

Coverage-driven verification based on simulation has been a widely accepted methodology for verifying hardware logic designs. The goal of this methodology is to improve a metric called coverage. In this paper, we adopt toggle coverage as a target. In our prior work, we have shown an approach which combines random simulation with input pattern generation using a SAT solver. The approach has been shown to be effective throughout experiments. The run-times of the SAT-solver, however, were dominant in the verification process, which could prevent progress of verification. In order to improve the approach, we extended this prior work by parallelizing the random/SAT-based processes. Experimental results show that the parallelization can be effective for achieving higher coverage. © 2023 Information Processing Society of Japan. All rights reserved.

关键词： Model checking

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma 2

Performance Analysis of the Machine Learning Algorithms for ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Vanitha, K. Karpagavalli, S.M. Mahesh, T.R. Ali, A. Althaf Sridhar, T. Anitha, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Al Ameen Engineering College Department of Computer Science and Engg Tamilnadu Erode India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh India Alliance University Alliance College of Engineering and Design Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350360523

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.

关键词： Hormones

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