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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Poonkodi, M. Vadivu, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Big Data Analytics School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665435246

computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a 'seeing' machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities' initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes *** lead to the requirement of high power computing devices for recognition, which is considerably *** this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively. © 2021 IEEE.

关键词： computer vision

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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Automation Through Machine Learning: Reinvigorating Cyber Security 15

Automation Through Machine Learning: Reinvigorating Cyber Se...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in Tamil and Malayalam 3

Findings of the Shared Task on Multimodal Abusive Language D...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Premjith, B. Jyothish Lal, G. Sowmya, V. Chakravarthi, Bharathi Raja Natarajan, Rajeswari Nandhini, K. Murugappan, Abirami Bharathi, B. Kaushik, M. Prasanth, S.N. Raj, R. Aswin Vijai Simmon, S. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland SASTRA Deemed to be University SRC Campus Tamilnadu Kumbakonam India School of Mathematics and Computer Sciences Central University of Tamil Nadu India Department of Information Science and Technology Anna University India SSN College of Engineering Tamilnadu India

ISBN: (纸本)9789544520854

This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings.

关键词： Sentiment analysis

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Modeling Path Importance for E_ective Alzheimer's Disease Drug Repurposing

Modeling Path Importance for E_ective Alzheimer's Disease Dr...

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29th Pacific Symposium on Biocomputing, PSB 2024

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, Chienwei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9789811286414

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a networks rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023 The Authors.

关键词： Embeddings

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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

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computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Demo: CaseFinder: Automated Visual and Quantitative Analysis Tool for Police Narrative

Demo: CaseFinder: Automated Visual and Quantitative Analysis...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Martin Brown Dominic Thomas Md Abdullah Al Hafiz Khan Abm Adnan Azmee Monica Nandan Yang Pei Department of Data Science and Analytics Kennesaw State University Marietta USA Information Systems Kennesaw State University Marietta USA Computer Science Kennesaw State University Marietta USA Computer Science Social Work and Human Services Kennesaw State University Marietta USA

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

This demonstration introduces the CaseFinder tool, a GUI designed to automate visualizations and quantitatively analyze 911 police reports. The tool allows for efficient data exploration for non-technical users integrating behavioral health-related keywords. By amalgamating diverse keyword category inputs, CaseFinder provides a holistic approach to analyzing reports by uncovering indications of behavioral health problems while including perspectives from multiple professional domains. Ultimately, CaseFinder aims to improve the understanding of the magnitude of the problem of behavioral health issues involved in 911 crises to mitigate reoccurrence.

关键词： Visualization Law enforcement Statistical analysis data visualization Graphical user interfaces

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A security framework for 5G-enabled wireless sensor networks based on blockchain technology

A security framework for 5G-enabled wireless sensor networks...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Durgarao, M.S.P. Valluru, Dinesh Saraswathi, S. Malathi, P. Thilagam, K. Loganathan, S. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India Mlritm Engineering College Department of Information Technology Hyderabad India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Dept of Cse - Aiml Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamilnadu Chennai India Velammal Engineering College Department of Ece Tamil Nadu Chennai India Paavai Engineering College Department of Ece Tamilnadu Namakkal India

ISBN: (纸本)9798350359688

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results. © 2024 IEEE.

关键词： Smart contract

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Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

arXiv

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arXiv 2024年

作者： Yao, Quanming Zhang, Yongqi Wang, Yaqing Yin, Nan Kwok, James Yang, Qiang Department of Electronic Engineering the Tsinghua University China Department of Data Science and Analytics Thrust the Hong Kong University of Science and Technology Guangzhou China Beijing Institute of Mathematical Sciences and Applications China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. © 2024, CC BY.

关键词： Contrastive Learning

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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