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Pedestrian Detection and Movement Direction recognition with Convolutional Neural Network 9th

Pedestrian Detection and Movement Direction Recognition with...

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9th international conference on pattern recognition and machine intelligence (PReMI)

作者： Baheti, Bhakti Innani, Shubham Gajre, Suhas Talbar, Sanjay SGGSIET Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (纸本)9783031126994;9783031127007

Pedestrian detection and movement intention recognition is one of the most challenging task and active research area in Advanced Driver Assistance Systems (ADAS). It is important to detect the pedestrian and estimate future direction for safe integration of self driving cars on the existing roads. This paper proposes a system that would detect the pedestrians on the road or surroundings, and automatically recognize the direction in which they are moving viz. front, left and right by an end-to-end Convolutional Neural Network (CNN) based approach. We propose to tackle this problem as a two step approach. Firstly, pedestrians are detected by the advanced YOLOv3 architecture. The detection results are processed to recognize the movement direction. As real time performance is desirable for practical applications, we propose to use small CNN architectures like SqueezeNet, MobileNetV1, MobileNetV2 and ShuffleNet in the system. The proposed approach outperforms existing approaches in literature and provides a remarkable contribution to ensure safety in intelligent vehicles.

关键词： Convolutional Neural Network Object Detection YOLOv3 SqueezeNet MobileNet ShuffleNet

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A Comprehensive Analysis of Gender Identification from Iris Images using Artificial intelligence Technique 3

A Comprehensive Analysis of Gender Identification from Iris ...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Gentem, Varaprasad Kumar, A.P. Siva Government Degree College Dr B.R. Ambedkar Konaseema India Jntu College of Engineering Department of Computer Science and Engineering Ananthapuramu India

ISBN: (纸本)9798331508456

Gender identification using iris images is the process of identifying an individual's gender by analyzing the patterns in their eyes. However, iris images are sometimes blurry, leading to misclassification. To overcome the problem, various existing studies have explored gender identification from iris images using Artificial intelligence (AI) techniques. This paper analyses gender identification using iris image AI techniques such as machine Learning (ML) and Deep Learning (DL) are utilized to categorize gender like male and female using iris images. The current researches employ ML techniques such as Support Vector machine (SVM), Random Forest (RF), Naive Bayes (NB), and Principal Component Analysis (PCA). Additionally, DL technique such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), and pretrained CNN models like AlexNet, VGG16, VGG19, ResNet10, ResNet50, DenseNet20, and InceptionV3 have also been utilized for gender identification using iris images. To evaluate the performance of these existing models, the evaluation metrics of accuracy, precision, recall, and F1-score have been taken into consideration. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Cybersecurity with an Investigation into Network Intrusion Detection System Using machine Learning 3

Enhancing Cybersecurity with an Investigation into Network I...

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3rd IEEE international conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shohanur Tausif Islam, Wahid Ahmed Khan, Md. Rifat Daffodil International University Department of CSE Dhaka Bangladesh American International University Bangladesh Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798331534400

The growing dependence on networked systems underscores the importance of robust security measures to fend off potential intrusions. This research undertakes the task of assessing different machine-learning models concerning network intrusion detection. Following an in-depth examination and comparison, models such as Decision Tree, Logistic Regression, XGBoost, and Random Forest exhibit promising aptitude in precisely identifying potential intrusions. Notably from our proposed model, Random Forest emerged as the top performer among these models, attaining the highest accuracy scores (99%). Its proficiency lies in delivering precise predictions and effectively capturing the intricacies of network intrusion detection. However, the Support Vector machine (SVM) displayed limitations in its performance throughout this research. These discoveries illuminate the positive and negative aspects of these patterns, providing valuable knowledge about their efficiency in detecting intrusions. The study underscores the necessity for further fine-tuning of successful models and the exploration of advanced techniques to fortify network security. This research, as a whole, signifies an initial and crucial stride in the direction of formulating network intrusion detection systems that are more effective and reliable within the realm of network security. © 2024 IEEE.

关键词： Network intrusion

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Enhancing Sales Forecasting Accuracy through Integrated Enterprise Resource Planning and Customer Relationship Management using Artificial intelligence 3

Enhancing Sales Forecasting Accuracy through Integrated Ente...

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Gupta, Arun Agarwal, Pratiksha Confiance Tech Solutions Los AngelesCA United States

ISBN: (纸本)9798350372120

Sales forecasting accuracy is crucial for businesses to allocate resources, manage inventory, and streamline operations efficiently. However, traditional forecasting methods often struggle to adapt to the dynamic nature of consumer behaviour and market trends. This paper explores the potential of enhancing sales forecasting accuracy through the integration of Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems combined with Artificial intelligence (AI) techniques. Businesses can gain deeper insights into customer behaviour, preferences, and market dynamics by harnessing AI algorithms, such as machine learning and predictive analytics, coupled with data from ERP and CRM systems. This integrated approach improves sales forecasts' accuracy and facilitates proactive decision-making, personalized marketing strategies, and enhanced customer engagement. This paper delves into the theoretical foundations and practical implications of implementing such an integrated system through a comprehensive review of relevant studies and case studies. Additionally, it provides actionable recommendations for businesses looking to adopt this approach to optimize their sales forecasting processes and gain a competitive edge in today's dynamic market environment. © 2024 IEEE.

关键词： Decision making

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What We Know About HTTP/3 and Its Implementation: A Literature Review 3

What We Know About HTTP/3 and Its Implementation: A Literatu...

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3rd international conference on Computing and machine intelligence (ICMI)

作者： Koch, Jacob Falowo, Olufunsho Elrod, Nathan Univ Cincinnati Sch Informat Technol Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

HTTP is the backbone of modern web communication. With the rapid growth of web architecture and web communication, it has led to the need for a development of new versions of the HTTP protocol to address performance, security, and communication issues that currently exist. HTTP/3 is a new protocol proposed in 2022 and recently accepted. This paper aims to examine its integration with the QUIC protocol, a UDP-based alternative to traditional TCP-based HTTP versions. HTTP/3 addresses several limitations of previous versions, such as head-of-line blocking, by using QUIC and UDP for traffic communication. This helps HTTP/3 to provide a more responsive and fault-tolerant connection. Despite its benefits, HTTP/3 faces several challenges, including application support for UDP traffic and a potential susceptibility to Distributed Denial of Service (DDoS) attacks. Often in literature however, HTTP/3 and QUIC are conflated as the same thing due to their novel nature. Through a systematic literature review, this study seeks to answer three key research questions. First, how does HTTP/3 integrate the QUIC protocol, and what are the key differences between HTTP/3 and traditional TCP-based HTTP versions? Second, which specific features and enhancements in HTTP/3 address the performance and security issues found in previous HTTP versions? The final question is, "How has HTTP/3 been deployed to solve challenges in cloud, IoT, and web environments and what opportunities does it afford? By addressing these questions, this paper will provide a clearer understanding of HTTP/3's architecture, its relationship with QUIC, and its implications for web, IoT, and cloud system architecture.

关键词： HTTP/3 QUIC HTTP Protocol Transmission Protocols Literature Review

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A Systematic Review and Taxonomy of Ransomware Detection Based on Artificial intelligence Algorithms 3

A Systematic Review and Taxonomy of Ransomware Detection Bas...

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3rd international conference on Embedded Systems and Artificial intelligence, ESAI 2024

作者： Chaieb, Omar Kannouf, Nabil Benabdellah, Mohammed Mohammed Premier University ACSA Laboratory Faculty of Science Oujda Morocco Abdelmalek Essaâdi University LSA Laboratory SOVAI Team National School of Applied Sciences Al Hoceima Morocco

ISBN: (纸本)9798331522056

The escalating prevalence of ransomware attacks poses a significant risk to digital infrastructures, data integrity, and essential services worldwide. Traditional signature-based detection methods often struggle to keep pace with the evolving landscape of ransomware variants and stealthy attack techniques. Artificial intelligence (AI) offers a promising solution, leveraging sophisticated features such as bytecodes, opcodes, API calls, and behavioral analysis to enhance ransomware detection capabilities. This review delves into the latest advancements in ransomware prevention and detection techniques, categorizing ransomware types and examining the intricate lifecycle of ransomware attacks. A comprehensive taxonomy of ransomware analysis techniques, machine/deep learning methods, and feature selection techniques is presented, considering the diverse operating systems targeted by these malicious threats. By conducting a thorough analysis of recent research articles in this field, we identify the key challenges faced by academics and the research community in mitigating the ransomware threat. Moreover, we explore potential future research directions to address these challenges and develop more effective countermeasures. © 2024 IEEE.

关键词： Taxonomies

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A Multimodal Content Moderation System using Adversarial machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

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Analysis of machine Learning for Recognizing Hand Gestures 3

Analysis of Machine Learning for Recognizing Hand Gestures

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kumar Sharma, Anand Kumar, A Suresh Nagpal, Amandeep Singh, Rajesh Reddy, DasariVeera Chadha, Anupama Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Graphic Era Deemed to Be University Department of Cse India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India ManavRachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350399264

The medical field and other research areas fields heavily rely on artificial intelligence (AI) and machine learning (ML). Hand gesture recognition (HGR), which is a straightforward approach for interacting with machines, has drawn the attention of numerous researchers in the context of AI. Utilizing ML technique, HGR is carried out. Both the image technique and the design approach are parts of it. In the image technique, the hand picture is rebuilt utilizing the attributes of the images. However, when using the model-based approach, the image is recreated using many models, including volumetric, geometric, and others. The key difficulty in gesture control continues to be the ML individual's complexity and preparation time. Its accuracy is used to assess the HGR accuracy. Its precision is used to assess the HGR overall system performance. The many techniques and algorithms utilized in gesture control are covered in detail inside this work. © 2023 IEEE.

关键词： machine learning

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Study on HGR by Using machine Learning 3

Study on HGR by Using Machine Learning

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sharma, Anand Kumar Suresh Kumar, A. Nagpal, Amandeep Singh, Rajesh Reddy, Dasari Veera Chadha, Anupama Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Graphic Era Deemed to Be University Department of CSE India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350399264

关键词： machine learning

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Artificial intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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