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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sainadh, K. Vishnu Satwik, K. Ashrith, V. Vekkot, Susmitha Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Electronics & Communication Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350335095

In recent years, speech recognition has become increasingly well-known as a result of the numerous applications it has in virtually every industry. These applications include wake-word, sentiment, command, and interactive game play. Interest in voice in games has increased recently. Voice-controlled interaction expanded video game accessibility. However, in order to prevent an unwelcome delay, using one's voice as a controller for a game calls for real-time processing. This research paper puts forward the idea of controlling the well-known maze game with speech commands recognized by a trained convolutional neural network (CNN). A dataset of the speech commands "Left", "Right", "Up", "Down", "Go", and "Stop"was prepared for training, validation, and testing. In order to recognize all six speech commands, an optimal bark spectrogram and the CNN-based spoken command recognition model is used. The results from this work indicated that, the proposed algorithm was able to recognize all six commands with a high recognition accuracy of 94.57% and recall, precision, F1-Score of 94.8%, 94.43%, 94.53% respectively. The proposed algorithm is incorporated into a maze game in the final step. © 2023 IEEE.

关键词： Statistical tests

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Enhanced Security for IOMT Devices: Deep learning Techniques for Network Intrusion Detection 5

Enhanced Security for IOMT Devices: Deep learning Techniques...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Gopala Krishnan, D. Diviya, M. Jyoshika Shri, K. Subramanian, M. Amrita School of Computing Department of Computer Science and Engineering Chennai India St Joseph's College of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350379884

The increasing popularity of Internet of Medical Things (IoMT) devices, like wearable sensors, has greatly improved patient care by allowing continuous monitoring and real-time data transfer to the cloud. However, this progress also raises security concerns, as the data sent from these devices can be at risk of tampering or hacking during transmission. Keeping this sensitive medical data safe is crucial for patient safety. To address these issues, a fused CNN and LSTM are developed to check whether the data from wearable IoMT devices has been altered or attacked during transmission to the cloud. The CNN part of the model helps to pull out important features from the data, while the LSTM part looks for patterns to spot any signs of tampering. The model uses two convolutional layers each of which is followed by a MaxPooling layer. Our model, tested on a large dataset, achieved an accuracy of 97.06%, showing its ability to protect the integrity of transmitted medical data. © 2024 IEEE.

关键词： Network intrusion

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Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

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3rd International Conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

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Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1973-1986页

作者： Shouyong Jiang Jinglei Guo Yong Wang Shengxiang Yang School of Automation Central South UniversityChangsha 410083China Department of Computing Science University of AberdeenAberdeen AB243FXUK School of Computer Science Central China Normal UniversityWuhan 430079China School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective ***,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation *** paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective *** bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower *** subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the *** bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective *** analysis and component analysis have been also carried out to further justify the proposed algorithm.

关键词： Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation

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Real Time Identification of Harmful Birds in Agriculture Field using computer Vision 7

Real Time Identification of Harmful Birds in Agriculture Fie...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sudhesh, K.M. Akhila, T. Thangavel, Senthil Kumar Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Ashland University Department of Mathematics And Computer Science OH United States

ISBN: (纸本)9798350359299

Agriculture is essential to the global economy and food security. Insect and bird attacks have now been found in recent research that could cause crop loss. Crops are prone to bird attacks throughout the early stages of crop production and are particularly sensitive from the milky stages up to maturation. Observing such birds, which are harmful to agriculture, is an expensive, time-consuming, and possibly error-prone operation. Furthermore, a vast area necessitates a huge labour force for bird scaring. The concept of automated monitoring through computer vision holds significant appeal. This is because it has the potential to facilitate the collection of comprehensive data on a much larger scale than is currently feasible. In this work, we leverage effective characteristics from images of 10 different hazardous birds, Especially in the realm of identification we employing transfer-learned deep learning models which holds promise. The resemblance of various bird features, as well as the difficulty of distinguishing them at a glimpse, has become a significant challenge. We used more than 1000 images of harmful birds from various species for model training. In experiments on two different transfer learned deep CNN models, the xceptionNet model showed the best performance with a test accuracy of 94% when compared to the relatively low memory model VGG19, which had a test accuracy of 80%.The analysis also revealed that the xception model performed better in terms of accuracy, precision, recall, and F1-score. © 2024 IEEE.

关键词： Birds

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Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

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Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

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Tsinghua science and Technology 2023年第5期28卷 952-964页

作者： Shan Cui Tao Zhu Xiao Zhang Liming Chen Lingfeng Mao Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Computer School University of South ChinaHengyang 421001China School of Computing Ulster UniversityBelfastBT151APUK

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily *** uses sensor devices to collect user behavior data,obtain human activity information and identify *** Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and *** can solve the problems of complexity and uncertainty,and has good knowledge expression ***,MLN structure learning is relatively weak and requires a lot of computing and storage ***,the MLN structure is derived from sensor data in the current *** that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be *** this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN *** a rulebase can reduce the time required for MLN structure *** apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning *** addition,we evaluate the parameters of the rulebase building scheme to check its stability.

关键词： Markov Logic Network(MLN) structure learning rulebase construction probabilistic latent semantics

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Prediction of Neuro Cognitive Disorders using Supervised Comparative Machine Learning Model & Scanpath Representations 8

Prediction of Neuro Cognitive Disorders using Supervised Com...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Vinayak, Vineet Paliwal, Mohan Amudha, J. Jyotsna, C. Amrita School of Computing Bengaluru Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham India Amrita School of Computing Bengaluru Department of Computer Science Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350334012

Dementia has become a pressing public health issue worldwide, with the number of affected individuals steadily increasing. As a syndrome, it is characterized by a decline in cognitive performance that extends beyond normal biological aging, caused by a diverse range of brain disorders and diseases. Alzheimer's disease is the most prevalent form of dementia, and it constitutes the majority of dementia cases. In addition to its physical and psychological impacts, dementia is also a significant economic burden on families and society at large, given the extensive care required. One potential approach to understanding the cognitive performance of individuals with dementia is the use of scan path representations. A scan path is a visual representation of eye movements and is created by an ordered set of fixations connected by saccades. By analyzing these patterns, researchers aim to better understand the visual behaviors of people with dementia and potentially develop more effective treatment options. To achieve this goal, the proposed supervised comparative machine learning model utilizes scan path representations to provide a more comprehensive understanding of dementia. By exploring the visual behaviors of individuals with the condition, the model aims to provide insights into the use of supervised machine learning algorithms in trail making tests to better classify the dementia patients using their scanpath representations. This research paper aims to contribute to the ongoing efforts to combat the global challenge of dementia and provide a more nuanced understanding of the condition. © 2023 IEEE.

关键词： Eye movements

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in engineering & sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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