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检索条件"机构=Dept. of Computer Engineering and Application"

共 252 条记录，以下是51-60 订阅

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Spam Email Detection using K-Nearest Neighbors: An Enhanced Approach

Spam Email Detection using K-Nearest Neighbors: An Enhanced ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sumit Saklani Kalyan Thapa Devendra Singh Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Spam emails represent a large portion of global email traffic and pose several risks, such as phishing, fraud, and malware attacks. Traditional spam filters are ineffective in combating spam that is ever-evolving, which makes the problems more complex in nature. Therefore, advanced machine learning techniques need to be adopted. This study attempts to integrate an optimized K-Nearest Neighbors (KNN) model for spam detection that implements Term Frequency-Inverse Document Frequency (TF-IDF) for text representation, along with Principal Component Analysis (PCA) for feature selection. Classification is performed with relevance to the neighbors with more distance using an adaptive distance weighting algorithm. Enhanced KNN techniques improve the metrics on Naive Bayes and Support Vector Machines (SVM) with respect to accuracy, precision, recall, and F1 score. Experimental results demonstrated the classification accuracy to amount 96.4 %, exceeds the existing systems. This study develops efficient spam detection systems having the capability of adapting novel spam methods.

关键词： Support vector machines Adaptation models Accuracy Computational modeling Unsolicited e-mail Heuristic algorithms Nearest neighbor methods Feature extraction Bayes methods Principal component analysis

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360° Security in Cameras Using Linear Binary Pattern Histogram

360° Security in Cameras Using Linear Binary Pattern Histog...

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International Conference on Inventive Research in Computing applications (ICIRCA)

作者： Neetu Faujdar Ankur Chaudhary Dept. of Computer Engineering and Application GLA University Mathura India

This research study proposes a novel approach for enhancing the security of surveillance cameras by using a combination of panoramic cameras and linear binary pattern (LBP) histograms. The proposed method can detect and track intruders in a 360° field of view, which is achieved by stitching together multiple images captured by the panoramic camera. The LBP histogram is then used to extract features from the stitched image, which are fed into a support vector machine (SVM) classifier for intruder detection. The proposed method is compared to several state-of-the-art methods, and experimental results show that the proposed method outperforms them in terms of accuracy and detection rate. Furthermore, the proposed method is shown to be robust to lighting changes and occlusions, making it suitable for real-world applications and the approach is then evaluated on a dataset of 360° images captured by cameras and achieves high accuracy in detecting abnormal events such as intrusion and theft. The results demonstrate the effectiveness of the proposed approach in enhancing the security of cameras and can be useful in various applications such as surveillance systems, smart homes, and malls.

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Leveraging UAV Technology for Enhanced Disaster Management: Innovations and applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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Evaluation of Machine-Learned Price Prediction Models for Block-Chain Technology 1

Evaluation of Machine-Learned Price Prediction Models for Bl...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Sharma, Ashutosh Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Dept. of IT Ghaziabad India IMSUC-Ghaziabad Dept. of Computer Application Ghaziabad India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

ISBN: (纸本)9798350345957

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed application framework results demonstrate that the application functioned as expected. © 2023 IEEE.

关键词： Blockchain

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Image-Based Attendance System using Facial Recognition 11

Image-Based Attendance System using Facial Recognition

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Vineet Kumar Singh, Trilok Dixit, Abhishek Singh, Raushan Kumar Singh, Pradeep Kumar Singh, Juginder Pal Tmu University Dept. of Computer Science and Engg Moradabad India Invertis University Dept. of Computer Science and Engg. Bareilly India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India GLA University Dept. of Computer Engg. and Application Mathura India

ISBN: (纸本)9781665487320

In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network). © 2022 IEEE.

关键词： Database systems

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Ganguly Kailash Pati Mandal Debasis Guha Dept. of Computer Application Dr. B.C. Roy Engineering College Durgapur India Dept. of Computer Science and Engineering National Institute of Technology Durgapur India

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively.

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