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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： mohammed I. Habelalmateen Amandeep Kaur Annapurnanand Tiwari Shalini m melvin Victor Neelam Sanjeev Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P. India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India School of Business and Management Christ (Deemed to be University) Department of Computer Science & Engineering (E.Tech.) SRM Institute of Science & Technology Chennai

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The research in this study aims at implementing the ABC algorithm to enhance the communication within UWSNs. The ABC algorithm, motivated by the CPG approach being analogous to that of honey bees searching for food, specifies optimal values for critical parameters of the network such as energy consumption, reliability in data transfer, and scalability. From the analyses conducted in this exposition, it is apparent that the envisaged methodology outperforms other conventional routing parlances in the following ways: minimal energy usage, high data delivery ratios, low packet drops, and longest network lifetime. Therefore, from the above results it can be concluded that, the said ABC algorithm is helping in achieving a better result in terms of improved underwater communication as well as in mitigating with the difficulties of UWSNs.

关键词： Wireless communication Wireless sensor networks Underwater communication Scalability Signal processing algorithms Signal processing Artificial bee colony algorithm Routing Robustness Telecommunication computing

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Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments

Deep Reinforcement Learning for Autonomous Drone Navigation ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Gautam Solaimalai Kode Jaya Prakash Sampath Kumar S A Bhagyalakshmi P Siddharthan K. R Senthil Kumar Software Engineering Manager U.S. Bank Georgia United States of America Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Civil Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Mechanical Engineering R.M.K. Engineering College Thiruvallur Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This exploration paper investigates the operation of deep underpinning literacy(DRL) for enabling independent drone navigation in cluttered surroundings. Navigating drones in cluttered spaces poses significant challenges due to the presence of obstacles and dynamic environmental conditions. Traditional navigation approaches frequently struggle to acclimatize to these complications. In this study, we propose a new frame using DRL ways to enable drones to autonomously navigate through cluttered surroundings while avoiding obstacles. The frame employs a deep neural network to learn a policy that guides the drone’s conduct grounded on environmental compliances. Through expansive simulations and real-world trials, we demonstrate the efficacity of the proposed approach in achieving robust and adaptive drone navigation in cluttered surroundings. The findings of this exploration have significant counteraccusations for colorful operations, including hunt and deliverance operations, surveillance, and package delivery, where independent drone navigation in cluttered spaces is pivotal.

关键词： Adaptation models Navigation Surveillance Artificial neural networks Aerospace electronics Deep reinforcement learning Product delivery

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A Superficial Learning Strategy to Identify Email Spams Based on Hybrid Data Filtration and Classification methodology

A Superficial Learning Strategy to Identify Email Spams Base...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： A. Alekhya Tagore Kumar P Bharaneedharan m S. Kavitha C. Geetha C. Thyagarajan Department of Electrical and Electronics Engineering QIS College of Engineering and Technology Ongole Andhra Pradesh India Department of Mechanical Engineering QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE S.A. Engineering College Chennai Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Computer Science and Engineering R.M.K. Engineering College Chennai Chennai Department of CSE Panimalar Engineering College Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Spam email detection is a critical task in improving email security, especially with the increasing volume of spam emails that threaten users' privacy and productivity. In this study, we propose a hybrid approach combining Gated Recurrent Units (GRU) and Support Vector machine (SVm) to identify spam emails effectively. The GRU component captures temporal dependencies in the email text, while SVm classifies the extracted features into spam or non-spam categories. The model was evaluated using various performance metrics, including accuracy, precision, recall, and F1-score, and compared with other models like standalone GRU, SVm, CNN, and Naive Bayes. The proposed hybrid GRU-SVm model achieved a superior accuracy of 96.8%, outperforming GRU (93.2%), SVm (91.5%), CNN (94.1%), and Naive Bayes (89.4%). Additionally, the model demonstrated a high true positive rate (98.2%) and a low false negative rate (1.8%) on a spam-heavy dataset, indicating its robustness in identifying spam emails. The results suggest that the hybrid model can significantly enhance spam email detection systems by improving classification accuracy and reducing false positives.

关键词： Support vector machines Productivity Accuracy Unsolicited e-mail Computational modeling Text categorization Feature extraction Robustness Bayes methods Security

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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks

Endoscopic Image Processing: Evaluating the Robustness of De...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： C N Gireesh Babu Kaparapu Satish Babu Sajja Suneel K.Srinivasa Rao Divya muralitharan R Aarthi Dept.of CSE/ISE BMS Institute of Technology and Management Bengaluru Karnataka Department of Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of AI&ML K.S.R.M. College of Engineering Kadapa Andhra Pradesh Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The Hybrid CNN-Transformer model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare.

关键词： Training Visualization Accuracy Transformers Feature extraction Gastrointestinal tract Robustness Convolutional neural networks Image classification Context modeling

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A Novel Approach to Social media Guideline Design and Its Application to Automated Vehicle Events

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Proceedings of the Human Factors and Ergonomics Society Annual meeting 2021年第1期65卷 1510 - 1514页

作者： Hananeh Alambeigi Alec Smith Ran Wei Anthony mcDonald Chidubem Arachie Bert Huang Department of Industrial and Systems Engineering Texas A&M University Department of Computer Science Tufts University Department of Computer Science Virginia Tech

Public perception of and attitudes towards automated vehicle (AV) technologies significantly influence the acceptance and adoption of AVs. Users’ perceptions are increasingly driven by media exposure. Prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs. Guiding users’ expectations and exposure to information through accessible media resources should lead to calibrated expectations and appropriate use of automation, ultimately leading to fewer safety incidents. We used a two-component analysis of (1) AV event-related tweets, through topic modeling and sentiment analysis, and (2) subject-matter expert interviews, through thematic analysis, to design basic guidelines for AV communication. The use of these guidelines should increase public knowledge of AV capabilities and aid in the proper calibration of public trust and acceptance of AVs.

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Cardiovascular Abnormalities Identification by machine Learning Classifiers and its Comparision

Cardiovascular Abnormalities Identification by Machine Learn...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： S. Surendran B.malakonda Reddy P.muthu Krishnammal B. Venkataramanaiah S. murugesan Joany. R m Department of Computer Science and Engineering Tagore Engineering College Chennai India Narayana Engineering College Gudur Andhra Pradesh India School of Electronics Engineering line 3: VIT-AP University Amaravati Andhra Pradesh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.D Engineering College Chennai India Department of ECE Sathyabama Institute of Science and Technology Chennai India

Heart diseases related to the heart increasing everywhere. Human beings are suffering from most dangerous cardiovascular disorders (CVD). Features are main parameters to identify diseases. The feature extraction is done from the signals and make ready for the analysis. Where machine learning (mL) is a technique that tells the potential emergence of cardiac disease. Information science uses machine learning to address a variety of difficulties. The basic use of machine learning is the forecasting of a result based on data that is already available. The machine applies the designs from the current dataset to a hidden dataset in order to predict the disorders. There are several arranging calculations to be completed with sufficient accuracy. We have used various machine learning classifiers for identifying CVDs. Random forest (RF) classifier producing highest accuracy compared to other classifiers.

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. Rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic minority Over-Sampling technique (SmOTE), and data preparation with SmOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： measurement Support vector machines Logistic regression machine learning algorithms Decision making Transportation Receivers Data models Classification algorithms Random forests

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The Role of Edge Computing in Enhancing Cloud Performance and Reducing Latency

The Role of Edge Computing in Enhancing Cloud Performance an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Sharma J Shyamala Devi K manikandan R Ani minisha R Harini S. Karthik School of CS & AI SR University Warangal Telangana India Rajalakshmi Institute of Technology Chennai School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Artificial Intelligence and Data Science R.M. K. Engineering College Department of Computer Science and Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.

关键词： Cloud computing machine learning algorithms Job shop scheduling Smart cities Processor scheduling Security Reliability Low latency communication Random forests Edge computing

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mobile Blaze: Harnessing mobile Net Architecture for Efficient Forest Fire Detection

Mobile Blaze: Harnessing Mobile Net Architecture for Efficie...

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Trends in Quantum Computing and Emerging Business technologies (TQCEBT), International Conference on

作者： S. R. Barkunan S R Raja Vedamoorthy. R L. Kannagi P. maniraj Nookala Venu Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Centre for Open and Digital Education Hindustan Institute of Technology and Science (HITS) Chennai Placement Officer Training and Placement Sri Sairam Engineering College Department of Computer and Communication Engineering Sri Sairam Institute of Technology Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Tamilnadu Centre for IoT Madhav Institute of Technology & Science (MITS) Gwalior A Govt. Aided UGC Autonomous Institute Madhya Pradesh India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The mobile Blaze methodology is an innovative strategy that employs the mobile Net architecture to improve the effectiveness of systems designed for detecting forest fires. Given the substantial risks forest fires pose to ecosystems and human safety, there is a pressing need for cutting-edge technologies to ensure prompt detection and response. In this project, we adapt the lightweight and efficient mobile Net architecture, originally designed for mobile devices, to create a streamlined and high-performance model for forest fire detection. The proposed mobile Blaze system integrates state-of-the-art deep learning techniques with the unique characteristics of mobile Net, enabling real-time processing of high-resolution imagery from various sources, including satellite and surveillance cameras. Our model is trained on a diverse dataset of forest fire scenarios, encompassing different environmental conditions and fire intensities. The use of transfer learning facilitates the efficient transfer of knowledge from pre-trained models, enhancing the model’s generalization capabilities. To evaluate the performance of mobile Blaze, extensive experiments are conducted on benchmark datasets, demonstrating superior accuracy and speed compared to traditional approaches. The lightweight nature of mobile Blaze enables deployment on resource-constrained devices, making it suitable for edge computing applications in remote forest areas. Furthermore, the system is designed to provide reliable fire detection with minimal false positives, ensuring prompt and accurate response to potential fire incidents. mobile Blaze represents a significant advancement in forest fire detection, offering a scalable and efficient solution that can contribute to early intervention and mitigation efforts. The integration of the mobile Net architecture showcases the potential for adapting existing deep learning models to address specific challenges in environmental monitoring and disaster management.

关键词： Deep learning Adaptation models Computational modeling Ecosystems Transfer learning Forestry computer architecture

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The technical use of mobile Robots in Developing Smart Cities in Integration with Vins

The Technical use of Mobile Robots in Developing Smart Citie...

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Advance Computing and Innovative technologies in engineering (ICACITE), International Conference on

作者： S. Rathika Shikha Chourasia E. Arunkumar Krishna Kant Dixit D. Satishkumar R. Sreelakshmy Department of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Science & Engineering (cse) IES College of Technology Bhopal M.P India Department of Electronics and Communication Engineering Karpagam College of Engineering TN Department of Electrical Engineering GLA University Mathura Department of Mechatronics Engineering Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The handling of independent mobile robots in complex settings presents a difficult task. Overcoming problems related to incorrect labeling and frequent tracking loses, particularly in difficult scenes, goes beyond the powers of point-based visual SLAm and simultaneous mapping (vSLAm) methods. This study presents a creative real-time, alongside resilient point-line oriented monocular visual inertial Navigation (VINS) system designed for mobile robots in urban areas that are ready for 6 G advances. To enhance resilience in tracking difficult scenes, we implement EDLines alongside adaptive kappa correction for swift capture of a higher share of extended line attributes within all derived line features. A new real-time line feature-mapping technique is presented to track this retrieved line features between subsequent frames minus the necessity about computing descriptors, giving an increase of three in performance compared with the traditional LSD and KNN matching strategy based on LBD descriptors. moreover, a tightly linked sensor fusion optimization scheme is built to attain precise state estimates, covering point-line characteristic reprojection errors and accelerometer device (ImU) residuals. Through tests on freely available standard datasets, our VINS system shows better localization precision real-time performance, and stability when compared to other modern SLAm systems. This VINS system enables the accurate location of robots on the move in smart cities defined by complicated surroundings.

关键词： Performance evaluation Location awareness Visualization Accuracy Simultaneous localization and mapping Smart cities Nearest neighbor methods

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