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International Conference on Computing and Networking Technology (ICCNT)

作者： N. V. Balaji Madhur Grover T Vignesh Kanika Seth K Gopalakrishna Vikas K. Kolekar Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The motive of this technical summary is to have a look at the performance of interference-conscious routing protocols for wireless Mesh Networks (WMNs). Interferenceconscious routing models help to lessen interference among WMN nodes associated with transmissions at the identical frequency channel. This type of interference can degrade the throughput of a network and reduce communication performance. The efficacy of a routing protocol is typically measured via parameters consisting of packet transport ratio, throughput, and end-to-stop delay. This AC will compare and assess unique algorithms for interference-conscious routing and evaluate their overall performance in terms of these parameters. Initial simulations will be carried out on diverse topologies of WMNs to determine the effect of interference-aware routing protocols on packet delivery ratio, throughput, and stop-to-stop put-off. The outcomes may be used to put together an assessment among exceptional algorithms and determine the most effective technique. In addition, the abstract will discover approaches to optimize the layout of interference-conscious routing protocols to attain the most green conversation overall performance. Subsequently, the paper will finish with a dialogue of capability demanding situations and important future studies guidelines in this area.

关键词： Wireless mesh networks Layout Interference Oral communication Throughput Routing Routing protocols Topology Standards Guidelines

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engineering Educators’ Adoption and implementation of Project-Based Learning: Experiences from a South Indian University

Engineering Educators’ Adoption and implementation of Proje...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Anjal P Dijo Anantha Krishnan K. P. N. P. Subheesh Aby John S M Anzar Sajin Sachidananthan Department of Computer Science and Engineering Federal Institute of Science and Technology India Department of Computer Science and Engineering College of Engineering Kerala India Teaching Learning Centre SRM University - AP Andhra Pradesh India Faculty of Philology RUDN University Moscow Russia Department of Electronics and Communication Engineering TKM College of Engineering Kollam India Curriculum Research and Development U-DNA Educational Foundation Anantapur Andhra Pradesh India

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

This research investigates the attitudes and perceptions of engineering educators in Kerala, India, towards the implementation of Project-Based Learning (PjBL) within the framework of APJ Abdul Kalam Technological University (APJAKTU), Kerala. Despite the growing emphasis on experiential learning, only limited research studies focus on understanding the attitudes and perceptions of engineering faculty members towards PjBL. Against this backdrop, the present study aims to critically examine the readiness of engineering educators in adopting and identifying PjBL strategies for its effective integration into engineering curricula. The paper explores various dimensions of engineering educators’ engagement with PjBL, including faculty interest levels, satisfaction of implementation, perceived effectiveness, and career implications. The data were collected from engineering faculty members by distributing a comprehensive questionnaire titled Project-Based Learning in engineering Education in Kerala (PLEEK), uniquely designed by the researchers. The research findings reveal that over 90% of the surveyed faculty members have shown keen interest in implementing PjBL, while the remaining respondents exhibited a neutral stance. This underscores a noteworthy receptivity to PjBL among engineering educators in Kerala. Suggestions from participants indicate that, if implemented correctly, PjBL would be highly effective, although time constraints pose a significant challenge. The results obtained are pivotal as they provide an explicit idea of the current status of the engineering education settings and the discrepancies between traditional theoretical instruction and project-based learning.

关键词： engineering profession Time factors engineering education

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Chhavi Bajpai Manish Gaur J Roselin Suganthi Vinayagam S S. Kaliappan Manoj Senthil K Department of Computer Science & Engineering Centre for Advanced Studies Lucknow India Department of Computer Science & Engineering Institute of Engineering and Technology Lucknow India Department of ECE K. Ramakrishnan College of Engineering Trichirapalli India Department of Bioscience Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Research and Development Lovely Professional University Jalandhar India Department of Electronics and Communication Engineering Kongu Engineering College Erode India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM.

关键词： Support vector machines Training Accuracy Computational modeling Crops Color Feature extraction Graph theory Medical diagnosis Diseases

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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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An Enhanced SVM Model for Eye Cancer Diagnosis Using Image Feature

An Enhanced SVM Model for Eye Cancer Diagnosis Using Image F...

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Automation and Computation (AUTOCOM), International Conference on

作者： Lakshmisree Panigrahi Lalit Khanna Vinay Kumar S B Ankita Agarwal T Pushpalatha Shriya Mahajan Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Eye cancer., also called ocular cancer., affects the eye and its structure. As with other conditions., early detection and correct diagnosis are key to effective treatment; however., this can be difficult for healthcare professionals. It makes diagnosis time-consuming and a bit subjective as well. Hence., a routine and diagnostic tool is required. This paper presents an improved SVM (Support Vector Machine) model for image feature-based eye cancer diagnosis. In this model., eye images are first processed to extract features., and then these extracted features will be classified into cancerous or noncancerous using SVM. Additionally, a feature selection algorithm is incorporated into this model to detect essential features that help further curtail the data while increasing classification performance. We tested our model on 500 images and achieved an accuracy of --92.0%. Besides, we have also compared our model with other state-of-the-art methods and achieved better accuracy, sensitivity, and specificity. Conclusions: In summary, we found that our proposed improved SVM model of eye cancer diagnosis did good results, which are acceptable and may be useful to medical doctors for a valid early detection of eye cancer. And this can, in turn, lead to improved treatment results for patients.

关键词： Support vector machines Accuracy Computational modeling Medical services Sensitivity and specificity Feature extraction Robustness Real-time systems Medical diagnostic imaging Cancer

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LSTM-CNN Ensemble Methods for Predicting Malignant Brain Tumors

LSTM-CNN Ensemble Methods for Predicting Malignant Brain Tum...

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Automation and Computation (AUTOCOM), International Conference on

作者： Ayaan Faiz K Gopalakrishna Ankita Agarwal R Suchithra Pavas Saini Ishan Ayus Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The LSTM-CNN ensemble method performs well in predicting malignant brain tumors. The model above combines the best of LSTM and CNN to enhance accuracy and consistency in forecasts. An LSTM model is an RNN architecture that allows the collecting and processing of repeatable data, e.g., time series or language text. It is one of the best tools for medical data analysis, such as brain tumor image scans. It will allow the LSTM model to learn patterns and correlations in your data regarding precision when predicting future observations. Meanwhile, the CNN model is best for interpreting high-dimensional data and then amalgamating them to make predictions. It renders it a beneficial tool for analyzing brain tumor imaging scans and predicting features indicative of a malignant nature. It unites the best of abilities in both models and helps to make up for their shortcomings, respectively. It leads to a more reliable and precise prognosis of brain cancers, both for early detection as well as treatment planning. Additionally, employing deep learning methods such as LSTM-CNN ensemble may greatly help enhance brain tumor diagnosis and therapy efficiency.

关键词： Deep learning Brain tumors computer architecture Predictive models Brain modeling Feature extraction Planning Ensemble learning Long short term memory Medical diagnostic imaging

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Adaptive Mutation Strategies in Non-Convex Optimization for Big Data Processing

Adaptive Mutation Strategies in Non-Convex Optimization for ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Takveer Singh Ashish Kumar Noopur Pandey Mamatha G N Daljeet Pal Singh Kartik N Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

It has been argued that adaptive mutation mechanisms in nonconvex optimization can efficiently handle massive data sets, the curse of big data. Big data is high-dimensional and complex compared to traditional optimization strategies, which can be computationally slow and sub-optimal. Adaptive changes to these parameters in real-time are known as adaptive mutation strategies that adjust them throughout training based on data properties and where they are within the optimization process. This facilitates more accurate and stable solutions, even when the data has noise or uncertainty. In addition, the adaptively of these strategies prevents falling into local optima by exhibiting better global optimization performance. As a result, integrating adaptive mutation strategies into nonconvex optimization for big data processing improves computational efficiency and effectiveness in solving sophisticated, complex problems, thereby making them useful within an era where huge volume exists as well-known with big-data foes.

关键词： Training Industries Uncertainty Social networking (online) Noise Medical services Big Data Real-time systems Optimization Systematic literature review

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Integrating Regression Models and Deep Learning for Lung Cancer Risk Prediction

Integrating Regression Models and Deep Learning for Lung Can...

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Automation and Computation (AUTOCOM), International Conference on

作者： Divya Sharma Sunil. MP Dhananjay Kumar Yadav Raghunathan S Preetjot Singh Soumen Nayak Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Early diagnosis is vital for successful treatment of lung cancer, as the disease remains one of the most common causes of death by cancer worldwide. Historically, the prediction of lung cancer has been based on regression models and disemboweled by the fact that it cannot optimally handle complex data. In contrast, deep learning techniques have exhibited great potential in developing precise predictive models of disease based on more complicated forms of data; nevertheless, these methods have been less rigorously applied specifically for predicting lung cancer risks. This project aims to combine conventional regression models and deep learning techniques to achieve better performance with more generalizable accuracy for lung cancer risk prediction. A hybrid framework will be developed, exploiting the strengths of regression models in being interpretable and deep learning for its superior prediction. Our proposed methodology will also tackle the issue of high-dimensional imaging data needed for lung cancer risk prediction. These advances in notably conjunctional regression models and deep learning may contribute to better prediction models for lung cancer risk by enhancing the accuracy and translation ability of the developed scores. It will help improve the identification of people at high risk for lung cancer and, on that note, also target the population likely to benefit, thereby ensuring early controls with better outcomes.

关键词： Deep learning Translation Accuracy High dimensional data Computational modeling Noise Neural networks Lung cancer Predictive models Reliability

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Application Of Mobile Cloud Computing Using Crowdsourcing to Provide Quality of Service

Application Of Mobile Cloud Computing Using Crowdsourcing to...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Rajan Verma Rishu Chhabra Rakesh Kumar A Kakoli Rao Navneet Kumar Anita Gehlot Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of Computer Engineering & Applications GLA University Mathura Lloyd Institute of Engineering & Technology Greater Noida Lloyd Law College Greater Noida Department of Electronics & Communication Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Mobile cloud computing represents a dynamic integration of mobile technology and cloud computing, fundamentally reshaping how services are accessed and utilized via mobile devices. Within this paradigm, crowdsourcing emerges as a potent tool for augmenting service quality across mobile applications and systems. This study delves into the synergy between mobile cloud computing and crowdsourcing, aiming to enhance service quality across diverse domains such as healthcare, transportation, and urban planning. By harnessing the collective intelligence and resources of the crowd, mobile cloud computing introduces innovative solutions to optimize resource allocation, diminish latency, and elevate user experiences. Our investigation explores the application of this symbiotic relationship, unveiling its multifaceted benefits, confronting challenges, and envisioning future potentials. Central to our research is the development of a framework that dynamically adapts cloud services based on various contextual factors including user activity, social dynamics, service specifics, and device attributes. This context-awareness-driven management approach ensures the provision of reliable cloud services, thereby bolstering the Quality of Service experienced on mobile devices. Through empirical analysis and theoretical exploration, we illuminate the efficacy of this framework in fostering a robust cloud service platform tailored to mobile environments. Ultimately, our findings underscore the transformative potential of integrating mobile cloud computing with crowdsourcing, heralding a new era of enhanced service delivery and user satisfaction in the mobile landscape.

关键词： Crowdsourcing Symbiosis Cloud computing Technological innovation Urban planning Transportation Quality of service

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