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An Approach for Effective Object Detection 1

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Bisht, Rohit Patel, Nileshkumar Ultimate Kronos Group Noida India Jaypee University of Engineering and Technology Guna India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

the ability to detect and recognize objects in images or videos is a vital aspect of computer vision, with applications in fields like surveillance, autonomous driving, robotics, and medical imaging. Object detection aims to locate and identify objects of interest within an image or video, and with the emergence of deep learning techniques, the accuracy and efficiency of this process have greatly improved. Modern approaches such as Faster R-CNN, YOLO, and SSD have demonstrated remarkable results in real-time object detection and recognition. However, despite these advancements, challenges such as occlusion, varying scales, cluttered backgrounds, and changes in viewpoint still pose significant obstacles. Further research and development of more sophisticated algorithms are necessary to address these challenges and accurately detect and recognize objects under diverse conditions. this paper seeks to improve the current object detection techniques’ performance by identifying their shortcomings and proposing solutions to enhance targeted performance metrics. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

the use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. the study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. this approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from Github to demonstrate the methodology. the sample is selected to reduce the complexity and to increase the performance. the dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. this improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP international Federation for Information Processing 2024

关键词： Snow

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Deep Watcher: A Surveillance System Using Deep Learning for the COVID-19 Pandemic 1

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Kulshreshtha, Rohil Jayapradha, J. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Several solutions have been proposed to combat the COVID-19 pandemic. In the absence or limited availability of medical resources, World Health Organization has recommended several safety measures. these measures were proposed to control the infection rate and keep current medical resources from depleting. Non-pharmaceutical intervention strategies such as wearing a mask and maintaining social distance are still being employed to combat the COVID-19 sickness. To contribute to this idea of human safety, our work aims to develop a model for detecting non-mask faces quickly and people who are not maintaining social distance in public. the proposed model uses computer vision and artificial intelligence to detect masks and distance between people. Also, a proposition has been made to increase localization performance during detection using the bounding box transformation. the combination of face mask detection and the social distance detection paradigm suggested in this paper is ideal for video surveillance equipment. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Music Recommendation System Based on Facial Expression using CNN 7th

Music Recommendation System Based on Facial Expression using...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Roshika, R. Girish, Anjana Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609 609 India

ISBN: (纸本)9783031699818

this music recommendation system focuses on the creation of an innovative music recommendation system designed to suggest songs tailored to users’ facial emotions and preferences derived from their online behavior. Extensive user details are scrutinized and summarized to comprehend individual musical tastes. Real-time facial emotion detection, coupled with social web content summarization, aids in the identification of singer names. the system recommends songs based on detected emotions and identified singer names, offering suggestions for popular songs and playlists. An insightful observation reveals users’ inclination toward happy songs during moments of joy, prompting a unique approach to address a recognized issue. Rather than exacerbating low moods with sad songs, the system recommends motivational or happy songs, showcasing its commitment to uplifting users. User studies affirm the precision and utility of the project, underscoring the overarching goal to provide a music recommendation system that not only caters to users’ musical preferences but also enhances their emotional well-being. the integration of continuous facial emotion detection and website summarization into the recommendation process adds a novel and dynamic facet to the system’s capabilities. © IFIP international Federation for Information Processing 2024.

关键词： Emotion Recognition

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Machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach 7th

Machine Learning Based Alzheimer’s Disease Detection: A Com...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Periya Nayaki, A. Vidyabharathi, A.K. Krishnaveni, S. thanabal, M.S. Department of Information Technology Velammal College of Engineering and Technology Madurai India Computer Science and Engineering PSNA College of Engineering and Technology Madurai India

ISBN: (纸本)9783031699818

Alzheimer’s disease typically leads in the death of neurons and the connections between them in memory-related parts of the brain, such as the entorhinal cortex and hippocampus. Following that, it affects the areas of the cerebral cortex responsible for language, cognition, and social interaction. Several other regions of the brain get harmed at some time. Patients with Alzheimer’s disease gradually lose their capacity to function normally and go about their daily lives. Currently, age is regarded as one of the most important risk factors;the majority of patients with dementia are 65 or older. Symptoms of the condition include increased disorientation, memory loss, including the inability to recall recent events or personal history, and learning difficulties. As of yet, there is no treatment for Alzheimer’s. A vast quantity of data will be acquired from the previously affected person, and the user input data will be compared to past data using machine learning techniques to determine whether they are still impacted. Finding the best categorization approach is difficult because it necessitates adapting to regional datasets. the methods LightGBM, AdaBoost, Random Forest Classifier, Support-Vector-Machine, Decision-Tree, and XGBoost provided accuracy rates of 78%, 86%, 81%, 73%, and 84%, respectively. In this work, the Voting Classifier algorithm, which may be more accurate than other methods, will be employed. © IFIP international Federation for Information Processing 2024.

关键词： Decision trees

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A Bibliometric Analysis of Research on Techniques for Network Communications Security 7

A Bibliometric Analysis of Research on Techniques for Networ...

引用

7th international conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Sikiru, Ismaeel Abiodun Dossou, Michel Institute of Mathematics and Physical Sciences Université d'Abomey-Calavi Africa Centre of Excellence in Mathematics and Computer Science Benin University of Ilorin Department of Information Technology Ilorin Nigeria Benin

ISBN: (纸本)9798350382266

Presently, security is very paramount in all endeavors that aim to guarantee seamless network communications, including our confidentiality, integrity, availability, and trust. thus, the objective of this bibliometric analysis is to examine and map the scientific advances in network communications security (NCS) in the recent years, while also projecting for future direction on the subject. To achieve this objective, web of science database was searched for related literature. While adopting preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach for inclusive and exclusive criteria, R package was executed for data analysis. Our findings, under the analysis of authors, countries, publications, and word analysis, show that Zhang Y (h-index=9;13.4%) and Wang X (h-index=9;12.4%) are the most contributing authors towards NCS. Anna University, India, is the most- contributing affiliated institution in this work, with 13.6%. Country- wise, USA is the most cited affiliated-countries, 6470 (32.1%). Of the sources, though Security and Communication networks journal has most studies on the subject, 91 (22.2%), IEEE Access owns highest h-index of 25 with citations of 1963 (35.7%). Also, keyword analysis reveals that in the recent years, 'security', 'machine learning', and '5G' are the most-relevant keywords used by authors. this study also reveals open interest, such as trust management, data models, and quantum mechanisms, for research community in network communications. © 2023 IEEE.

关键词： Network security

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source computer Vision Library).the system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Defending the Digital Frontier: URL-Based Phishing Detection Extension 7th

Defending the Digital Frontier: URL-Based Phishing Detection...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Vamsi, P. Muthaiah, U. Roshan Vardhan, C.H. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9783031699856

the internet’s rapid expansion has led to an increase in cyberattacks and phishing schemes that rely on uniform resource location (URL) links. In today’s digital landscape, it’s vital to identify these harmful links for safety. Traditional methods struggle to adapt to new website URLs, so machine learning is being used to detect malicious URLs. Previous studies have explored various machine learning techniques for URL detection, but a recurring issue is the need to preprocess website links before analysis. To address this problem, a new neural network method has been introduced. this innovative approach enables the creation of a model capable of identifying malicious URLs without the need for repetitive feature extraction steps. this groundbreaking technique has shown impressive results in detecting dangerous web addresses, operating swiftly and accurately with a detection rate of 99.61%. this represents a significant advancement in the field of cybersecurity, offering an effective solution to the ever-evolving threat landscape posed by malicious URLs on the internet. © IFIP international Federation for Information Processing 2024.

关键词： Phishing

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Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy 1

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Goswami, Shroddha Ashwini, K. Dash, Ratnakar National Institute of Technology Rourkela Rourkela India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Diabetic retinopathy (DR) is an eye ailment affecting retinal blood vessels. Many deep learning methods for detecting DR have been presented as manual diagnosis is time-consuming and inconvenient. InceptionV3 architecture was modified using a soft attention module in this proposed framework. the attention technique’s basic concept is to concentrate on specific relevant parts by assigning the weights accordingly. Contrast Limited Adaptive Histogram Equalization (CLAHE) is the pre-processing method applied initially to the fundus images to improve the contrast level. Along with this, augmentation has been done to increase the number of images, which are then trained and validated using a modified InceptionV3 model. the experimental results show that the suggested model better diagnoses all stages of DR than existing techniques and outperforms the existing model on the IDRiD and DDR datasets. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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A review of Communication and Data Networks Algorithms Shaping Modem Connectivity 7

A review of Communication and Data Networks Algorithms Shapi...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Srinath, M.K. Gatla, Anitha Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

the world of today is highly interconnected and undergoes rapid changes. Algorithms are essential for data networks and communication systems. this study looks at how the complexity of algorithms affects network design. It focusses on the GROA (Global Routing Optimisation Algorithm), LBEA (Load Balancing and Efficient Allocation), and QKD-NS systems in particular. We aim to educate individuals on the impact of various methods on security, route completion time, and resource constraints. Based on the results, GROA creates variable paths, QKD-NS boosts safety with quantum-based innovations, and LBEA efficiently divides up resources based on what the network requires. According to the study, putting these many technologies together could lead to strong partnerships that can handle security breaches, traffic jams, and changes in the network. this study focusses on how advances in technology have led to better computer design, which in turn has made the internet safer and easier to use. the paper elucidates the application of these novel mathematical discoveries to enhance the performance and durability of data networks. these new details help communication networks improve by ensuring that they are safe and can meet today's link standards. © 2024 IEEE.

关键词： Traffic congestion

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