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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Roy, Bhumika Nivethika, S. Deepa Manju, G. Pandian, M. Senthil School of Computer Science and Engineering Vellore Institute of Technology University Chennai600127 India School of Civil Engineering Vellore Institute of Technology University Chennai600127 India

ISBN: (纸本)9798350394580

Aim: This paper presents a three-phase approach to smart home automation using Cisco Packet Tracer. Phase 1 establishes the foundational network and integrates IoT devices, ensuring successful packet transfer. In Phase 2, the focus shifts to fire detection and prevention, utilizing smoke detectors and automated water sprinklers. Phase 3 enhances network security through firewall implementation, restricting access to specific IP addresses and blocking ICMP and PING messages. These phases collectively create a robust smart home system, combining device integration, safety measures, and network security, offering enhanced convenience and safety for residents. This work aims to use Cisco Packet Tracer and implement it to design a Smart Home for fire detection and prevention, where home appliances are connected to the router, which in turn is connected to the internet and set up a firewall that adds up network security to the system. © 2023 IEEE.

关键词： automation fire alarm home security Smart

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer Systems science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques 3

Pancreatic Cancer Diagnosis using Swarm Optimization Combine...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Bharanidharan, N. Aggarwal, Pratham Batra, Aryan Shah, Deep Sannasi Chakravarthy, S.R. Rajaguru, Harikumar Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India

ISBN: (纸本)9798350370867

When compared to different types of malignant tumors, pancreatic malignancy is the 12th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well. © 2023 IEEE.

关键词： Gene expression

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Aviation Safe Landing Prediction Utilizing Long-Short Term Memory Algorithm 6

Aviation Safe Landing Prediction Utilizing Long-Short Term M...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Krishna, Raji Sariki, Tulasi Prasad Sigamsetty, Siri Mahalakshmi Kumar, G. Bharadwaja Benil, T. Vellore Institute of Technology School of Electrical Engineering Chennai Campus Chennai India Vellore Institute of Technology School of Computer Science Engineering Chennai Campus Chennai India

ISBN: (纸本)9798350394702

The aviation industry is significantly dependent on sophisticated technology, such as engines, avionics, and air traffic control systems, to assure the safety and efficiency of its operations. A significant proportion of commercial aviation accidents may have been averted by implementing a flying procedure wherein an incoming aircraft discontinues its landing attempt and reenters the queue for landing. The timely implementation of this approach can substantially impact mitigating the overall accident rate within the aviation industry. Hence, the main aim of this article is to effectively forecast flight landings, improve safety measures and enable informed decision-making in complex situations for both autonomous and human-operated aircraft using a Deep Learning algorithm such as long short term memory (LSTM). The performance of LSTM compared with machine learning (ML) technique like Support Vector Machines (SVM) and logistic regression algorithm in terms of sensitivity and specificity © 2023 IEEE.

关键词： Long short-term memory

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Deepfake Video Detection Using Ada-Boosting on the DFDC Dataset

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Procedia computer science 2025年 258卷 1091-1101页

作者： Battula Thirumaleshwari Devi Rajkumar Rajasekaran Department of Computer Science and Engineering Vellore Institute of Technology Vellore 632 014 India

The rapid proliferation of deepfake videos, generated using advanced machine learning techniques to create highly realistic but misleading content, poses significant challenges across various sectors, including cybersecurity, media integrity, and personal privacy. Detecting these deepfakes has become essential for maintaining trust in digital media and preventing malicious exploitation. This paper presents a novel approach to deepfake video detection by employing the AdaBoost algorithm, a powerful ensemble learning method recognized for its ability to improve classification performance by focusing on difficult-to-classify instances. Using the Deepfake Detection Challenge (DFDC) dataset, our study demonstrates that the AdaBoost classifier, when coupled with a carefully designed feature set, achieves competitive accuracy in detecting deepfake videos. Our results show that this approach provides an effective solution for deepfake detection, with strong recall performance, making it a viable method for real-world applications.

关键词： Deepfake detection AdaBoost DFDC dataset ensemble learning machine learning video forensics

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T5LSTM-RNN based Text Summarization Model for Behavioral Biology Literature

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Procedia computer science 2023年 218卷 585-593页

作者： Shivangi Chaurasia Debalay Dasgupta Rajeshkhannan Regunathan School of Computer Science and Engineering Vellore Institute of Technology Vellore Professor School of Computer Science and Engineering Vellore Institute of Technology Vellore

Behavioral biology is one of the crucial and trending topics these days which needs proper attention by scholars for rapid development in this field. Therefore, the purpose of this paper is to ease the process of collecting all kinds of relevant and vital data available on the internet regarding this topic in different forms of media, such as news articles, research papers, and YouTube lecture videos, all into one place into a single document in a proper summarized form. For proper training of the LSTM model, the lengthy video and journal datasets are pre-processed using the T5 transformer model to generate a uniform training dataset. So, in this work, a comprehensive approach is proposed based on an abstractive form of text summarization using the seq2seq encoder-decoder model combined with a stacked LSTM layer with an attention mechanism and a T5 transformer model pre-processor. Therefore, a proper hybrid model, T5LSTM-RNN, is implemented to generate the summarized data.

关键词： Seq2Seq Text Summarization Abstractive Summary T5 Transformer Behavior Biology LSTM RNN

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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computer Systems science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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