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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Optimized Load Balancing Technique for Software Defined Network

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computers, Materials & Continua 2022年第7期72卷 1409-1426页

作者： Aashish Kumar Darpan Anand Sudan Jha Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Chandigarh UniversityMohaliPunjab140413India School of Computational Sciences CHRIST(Deemed to be University)Ghaziabad201003India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Automotive ICT Convergence Engineering Daegu Catholic UniversityGyeongsan38430Korea

Software-defined networking is one of the progressive and prominent innovations in Information and Communications *** mitigates the issues that our conventional network was ***,traffic data generated by various applications is increasing day by *** addition,as an organization’s digital transformation is accelerated,the amount of information to be processed inside the organization has increased *** might be possible that a Software-Defined Network becomes a bottleneck and *** models have been proposed in the literature to balance the ***,most of the works consider only limited parameters and do not consider controller and transmission media *** loads also contribute to decreasing the performance of Software-Defined *** work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the *** proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network *** simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques.

关键词： SDN software-defined networks load balancing performance enhancement

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Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

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Intelligent Automation & Soft Computing 2023年第3期35卷 2909-2924页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.BOX 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The main task of thyroid hormones is controlling the metabolism rate of humans,the development of neurons,and the signiﬁcant growth of reproductive *** medical science,thyroid disorder will lead to creating thyroiditis and thyroid *** two main thyroid disorders are hyperthyroidism and *** research works focus on the prediction of thyroid *** improve the accuracy in the classiﬁcation of thyroid disorder this paper pro-poses optimization-based feature selection by using differential evolution with the Butterﬂy optimization algorithm(DE-BOA).For the classiﬁer fuzzy C-means algorithm(FCM)is *** proposed DEBOA-FCM is evaluated with para-metric metric measures of sensitivity,speciﬁcity,and *** this work,the thyroid disease dataset collected from the machine learning University of Cali-fornia Irvine(UCI)database was *** accuracy rate for the Differential Evo-lutionary algorithm got 0.884,the Butterﬂy optimization algorithm got 0.906,Fuzzy C-Means algorithm got 0.899 and DEBOA+Focused Concept Miner(FCM)proposed work 0.943.

关键词： Fuzzy butterﬂy differential evolution thyroid hyperthyroid

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Dynamic Bidirectional Translation for Sign Language by Using Machine Learning-Infused Approach with Integrated computer Vision 2

Dynamic Bidirectional Translation for Sign Language by Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Somya Diwakar, Shikha Yadav, Neha G.L. Bajaj Institute of Technology and Management Engineering/ Computer Science Artificial Intelligence Department of Applied Computational Science India School of Information & Communication Technology Gautam Buddha University Engineering/Artificial Intelligence India G.L. Bajaj Institute of Technology and Management Department of Applied Computational Science India

ISBN: (纸本)9798350371055

This research aims to break communication barriers for the deaf and hard-of-hearing by pioneering a real-time, dynamic sign language translator. Unlike existing apps, it uses a CNN to translate simultaneously between English and American Sign Language (ASL). It features an animated avatar for interactive communication, and goes beyond translation with chat, video calls, and precise hand motion capture. This innovative approach paves the way for inclusive and secure communication within the deaf community. © 2024 IEEE.

关键词： ASL avatar CNN communication barriers deaf inclusive real-time Sign language translation

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Optimizing Healthcare Accessibility and Effectiveness through the Implementation of AI Solutions for Remote Patient Monitoring 3

Optimizing Healthcare Accessibility and Effectiveness throug...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Detection analysis of malicious cyber attacks using machine learning algorithms

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Materials Today: Proceedings 2022年 68卷 26-34页

作者： Karthika, R.A. Maheswari, M. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Computational Intelligence SRM Institute of Science and Technology KTR Chennai 603203 India

Cybersecurity is the practice of safeguarding information and the systems that store or process information. Cybersecurity violations are the foremost persecution instigated by cyber attackers through one or more systems on single or several networks or systems. This kind of cyber threats can ruthlessly trigger data loss or theft besides halting the network systems partially or fully. Among various cybersecurity attacks, the Denial of Service (DoS) attack is one of the most dominant hacking modus operandi over the internet. The hackers use the weapons at their disposal to disrupt traffic in and around the network surroundings or the target system by bombarding it with a lot of malicious requests. This paper introduced machine learning algorithms such as Gaussian Naïve Bayes and Nearest Centroid to identify the attacks via classifying the given dataset into normal and malicious traffic. Furthermore, metrics such as accuracy, f-score, FNR, precision, and prediction time etc., were calculated to identify the best performing model. © 2022

关键词： Denial-of-service attack

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Removing Gaussian Image Noise Using Advanced Non-Local Means Filtering Techniques

Removing Gaussian Image Noise Using Advanced Non-Local Means...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Suresh, A. Waris, Saiyed Faiayaz Mohankumar, B. Arumuga Arun, R. Saravanan, G. Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Vellore India Vignan's Foundation for Science Technology & Research Vadlamudi Department of Computer Science and Engineering Andhrapradesh Guntur India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350317060

Our system is aimed at improving the application of image de-noising processes, even though the presence of noise has a negative impact on image quality. End-user applications suffer as a result of the focusing Gaussian noise. Introduce the Non-local means (NLM) filter to take advantage image similarities and analyse data preservation in de-noising. Even though the NLM approach eliminates some noises, like salt and pepper, an estimate weighted between the input images and edge preservation is still calculated. It could experience loss of visual structure, noise reduction, and blurring. This research shows the enhancement of the threshold level by combining stationary wavelet transform with NLM. This type of Advanced NLM method is effective at removing high noisy regions like Gaussian noise levels. The proposed ANLM method improves edge preservation and image structure similarities. This article uses some noisy images as input, then employs quantitative parameters to perform the measurement. Finally, this method demonstrates the effective removal noise and edge preservation of weighted refined structures. PSNR, COC, and MSE are the measuring parameters. © 2023 IEEE.

关键词： Mean square error

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Semantics Preserving Emoji Recommendation with Large Language Models

Semantics Preserving Emoji Recommendation with Large Languag...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Qiu, Zhongyi Qiu, Kangyi Lyu, Hanjia Xiong, Wei Luo, Jiebo Georgia Institute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology School of Computer Science AtlantaGA United States University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350362480

Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text. However, they ignore the essence of users' behavior on social media in that each text can correspond to multiple reasonable emojis. To better assess a model's ability to align with such real-world emoji usage, we propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text. To evaluate how well a model preserves semantics, we assess whether the predicted affective state, demographic profile, and attitudinal stance of the user remain unchanged. If these attributes are preserved, we consider the recommended emojis to have maintained the original semantics. The advanced abilities of Large Language Models (LLMs) in understanding and generating nuanced, contextually relevant output make them well-suited for handling the complexities of semantics preserving emoji recommendation. To this end, we construct a comprehensive benchmark to systematically assess the performance of six proprietary and open-source LLMs using different prompting techniques on our task. Our experiments demonstrate that GPT-4o outperforms other LLMs, achieving a semantics preservation score of 79.23%. Additionally, we conduct case studies to analyze model biases in downstream classification tasks and evaluate the diversity of the recommended emojis (https://***/VIStA-H/SemanticsPreservingEmojiRec). © 2024 IEEE.

关键词： Semantics

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A Novel Enhanced Cuckoo Search-SVM Model for Robust Brain Tumor Segmentation 9

A Novel Enhanced Cuckoo Search-SVM Model for Robust Brain Tu...

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

作者： Alphonsa, J. Kumari, V. Sheeja Roslin Dayana, K. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600025 India R.M.D Engineering College Department of Computer Science Kavaraipettai Tiruvallur Chennai601206 India

ISBN: (纸本)9798350377972

The accurate segmentation of brain tumors from medical imaging is a critical task that aids in early diagnosis and treatment planning. Existing techniques often need help with issues like high computational demands, sensitivity to image noise, and suboptimal performance in varied imaging conditions. Enhanced Cuckoo Search-SVM (ECS-SVM) model that integrates an optimization-driven approach with machine learning for robust and efficient segmentation. We utilize the ECS algorithm to fine-tune the hyperparameters of a Support Vector Machine (SVM), which classifies extracted features from MRI images. The proposed approach leverages the search capabilities of the enhanced Cuckoo Search algorithm to identify optimal parameters that enhance the SVM's performance. With the valid application on a comprehensive dataset, the proposed method obtains a high accuracy of 95.3%. In comparative experiments, it is shown that ECS-SVM outperforms traditional and existing hybrid models, making it a great option for automated and reliable brain tumor segmentation. The results indicate significant improvements in segmentation precision, robustness, and computational efficiency, marking a notable advancement in the field. © 2024 IEEE.

关键词： Support vector machines

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