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Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

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SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

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An Enhanced and Interactive Training Model for Underground Coal Mines Using Virtual Reality 2

An Enhanced and Interactive Training Model for Underground C...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Jain, Akshali Jain, Mehul Patel, Mayank Rathore, N.S. Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350346640

To solve the problems that occur during the training of coal mining, this study has developed a VR-based user interactive solution with which immersive virtual training will be provided to the miners before going into real fieldwork, and workers can get the in-handed experience of safety and precautions in coal mines. VR solutions for underground coal mining training can help the workers to skill up their workforce, especially for the young miners and better life-saving training. Maximum of the present VR-based education structures are lacking in a learning enjoy. But these structures adopt training and mastering functions via player interatcion with the assist of 3D coal mine VR schooling models. The system can be efficiently implemented in a laboratory environment. The proposed model provides four types of training - Coal digging and Loading, Fire rescue operation, Disaster scenario, and mechanical operations. In all these operations, after wearing the VR headset instructions will be provided to the users step by step on what they must do accordingly. With the help of this solution, miners can be trained to save their lives and handle the disastrous situation in coal mines. This will be a complete training for new miners. © 2023 IEEE.

关键词： Miners

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A multi-modal approach for identifying schizophrenia using cross-modal attention 46

A multi-modal approach for identifying schizophrenia using c...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Premananth, Gowtham Siriwarden, Yashish M. Resnik, Philip Espy-Wilson, Carol University of Maryland Department of Electrical and Computer Engineering College ParkMD United States University of Maryland Institute for Advanced Computer Studies College ParkMD United States

ISBN: (纸本)9798350371499

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs from the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multi-modal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN), with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score. © 2024 IEEE.

关键词： Image coding

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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Impact of COVID-19 on People 1

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Nandal, Neha Tanwar, Rohit Saketh, Meduri Pilania, Urmila Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Technology Manav Rachna University Faridabad India

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

COVID-19 affected each and every sector of human life. The food sector, security, employment, agriculture, etc. converged a lot during the pandemic. The coronavirus (COVID-19) pandemic has had a significant impact on the world. The virus was first identified in Wuhan, China, in December 2019, and has since spread to nearly every country. The World Health Organization declared it a pandemic on March 11, 2020. As of January 2021, the virus has infected over 89 million people and resulted in over 1.9 million deaths globally. The pandemic has had wide-ranging effects on society, including health impacts, economic consequences, and changes to daily life and social interactions. Many countries have implemented measures such as lockdowns, travel restrictions, and mask mandates in an effort to slow the spread of the virus. The impact on the healthcare system was significant due to the overwhelming number of hospitalizations and intensive care needs in some countries. The economy took a hit as well as many businesses closed and job loss became widespread. This review provides insight into the impact of the coronavirus on people and how different measures have been taken up to have a control over this pandemic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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CNN-LSTM: Development of Offline Signature Authentication 1

CNN-LSTM: Development of Offline Signature Authentication

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Ganapathy, Revathy Lingeshwaran, D. Suthanthiradevi, P. Harish Kumar, M.V. Vels Insitute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350359756

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system. © 2023 IEEE.

关键词： Long short-term memory

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Web Content Based Features for Malicious Web Page Detection Using Machine Learning 4

Web Content Based Features for Malicious Web Page Detection ...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Malicious web pages are well-known threats in cyberspace. Malicious webpages and their URLs appear to be legitimate webpages in cyberspace and are used to steal personal information from internet users (victims) or to install malicious software on the victim's machine in order to gain further access and make the victims more vulnerable in cyberspace. Identifying such web pages in cyberspace is a difficult task for internet users. Researchers use different techniques with different sets of features to detect malicious web pages in cyberspace. Most of them extract vital features from URL text for malicious webpage classification due to their simplicity and risk-free nature. Some of them extract the limited number of features from the webpage content for better classification along with other features. Our proposed system considers features that are extracted only from the webpage content and use a machine learning model for classification. The accuracy of the existing and proposed systems is demonstrated experimentally. The result shows that the proposed system is more efficient. © 2022 IEEE.

关键词： Websites

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Malicious Domain Detection using NLP Methods - A Review 11

Malicious Domain Detection using NLP Methods - A Review

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

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9781665487320

Cyber security has emerged as a serious problem in today's business world. Attackers use different techniques to deceive the internet user to perform illegal activities such as stealing sensitive information, injecting malware, using the victim's system for further attack, etc. The majority of attacks are disseminated via social media posts, emails, and SMS messages. The message or webpage has embedded malicious links that will lead the user to the attacker's nest. Consequently, the issue of identifying malicious domain names has sparked significant concern. Researchers employ a variety of methods to find such malicious pages, including black or white lists, rules, machine or deep learning, visual similarity, and language processing (NLP). the attacker's employment of various tricks to confuse internet users, detecting or blocking an attack through a malicious domain is a difficult task. NLP with Machine Learning and NLP with Deep Learning methodologies receive the most interest from researchers due to their processing power. This paper provides an overview of several NLP text processing techniques as well as recent domain-related research. © 2022 IEEE.

关键词： Malware

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