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

共 252 条记录，以下是1-10 订阅

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

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

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

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

ISBN: (纸本)9798331518578

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. © 2025 IEEE.

关键词： Decision making

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Cyber Security Ensemble Learning Feature Extraction Phising Random Forest

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Transformer Based Classification of Closely Related Dravidian Languages in Code-Mixed Data

Transformer Based Classification of Closely Related Dravidia...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Saji, Anandhu Shamsudeen, Fousia M Centre for Artificial Intelligence Tkm College of Engineering Kerala Kollam India Tkm College of Engineering Dept. of Computer Application Kerala Kollam India

ISBN: (纸本)9798331507541

In recent years, code mixing, the blending of words and phrases from multiple languages within a single conversation has become increasingly common in multilingual contexts, particularly in India. This work investigates the effectiveness of transformer based models in identifying languages within code mixed data that includes English and three closely related Dravidian languages: Malayalam, Kannada, and Tamil, all written in English script. A comprehensive dataset was developed with five distinct labels: Tn-en (Tamil-English), Kn-en (Kannada-English), Ml-en (Malayalam-English), and En (English). Transformer models, including BERT, CamemBERT, mBERT, DistilBERT, and ELECTRA, were employed for classification, with CamemBERT achieving the highest validation accuracy of 97.62% due to its ability to handle morphologically rich languages effectively. The findings highlight the potential of transformer models in enhancing natural language processing (NLP) applications for code-mixed text, particularly in managing the complexities of closely related multilingual environments. © 2025 IEEE.

关键词： Natural language processing systems

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain 2

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Shubh Pathak, Disha Mohini Jain, Dhyanendra Srivastava, Somya Vats, Prashant Upadhyay, Govind Murari Department of Computer Science ABES Engineering College U.P. Ghaziabad India Department of Computer Science Engineering-AIML ABES Engineering College U.P. Ghaziabad India Dept. of CSE School of CSE Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Application Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331530389

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity. © 2025 IEEE.

关键词： Contrastive Learning

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Code-Mixed English-Indian Languages: Hate Speech Dataset Analysis

Code-Mixed English-Indian Languages: Hate Speech Dataset Ana...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Manoj Kumar Saini Rahul Pradhan Dilip Kumar Sharma Dept. of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In the era of Social Media Networks (SMN) and Online Forum (OF) such as Facebook, Instagram, Blogging Sites, Gaming Platforms etc., users tend to comment significantly in English and Indian Code Mixed (CM) languages. Code-mixing is a common manifestation in linguistically diverse social groups such as India. Many people do not know English, so thousands of people on these platforms write comments in code-mixed languages such as Hindi-English (Hinglish). Researchers have retrieved comments on the CM language from SMN and OF. Many people respond in the form of Hate Speech (HS) and Offensive Language (OL) to insult or hurt others, a person, or specific groups. SMN and OF encounter issues with pinpointing and eradicating HS and disagreeable information in diverse CM Indian languages. OL is straightforwardly or inadvertently used in hate speech posts to demean someone and that can cause psychological distress to persons. So, a specific and effective Code Mixed dataset and classification are required to detect HS and OL. Therefore, in this study, we carried out an in-dept. analysis on the retrieval methodology and classification of existing data sets for HS and OL. In this paper, we study and analyze 6 (SIX) datasets that were widely adopted in the literature.

关键词： Technological innovation Codes Social networking (online) Social groups Hate speech Semantics Psychology Syntactics Complexity theory Web sites

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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Exploring the Capabilities and Limitations of Generative AI applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 IEEE.

关键词： Generative adversarial networks

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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

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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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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