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4th International Conference on computing and Communications Technologies, ICCCT 2021

作者： Srivatsan, M. Sathyanarayanan, Abhishek Meenakshi, K. Vadivu, G. Hsiu-Chun-Hsu SRM Institute of Science and Technology CSE Department Chennai India School of Computing SRM Institute of Science and Technology Department of Networking Communications Chennai India School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India National Chung Cheng University Department of Information Management Taiwan

ISBN: (纸本)9781665414470

The Covid 19 Pandemic has had an impact on many aspects of our daily lives such as Restricting contact through touch, wearing masks, practicing social distancing, staying indoors which has led to change in our behaviors and prioritized the importance of safety hygiene. We travel to different places such as Schools, Colleges, Restaurants, offices, and Hospitals. How do we adapt to these changes and refrain from getting the virus? Luckily, we have the technology to aid us. We are all used to biometric systems for marking our Presence/ Attendance in places like colleges, Offices, and Schools with fingerprint sensors, fingerprint sensors use our Fingerprint to mark our presence however Covid 19 has restricted the use of touch causing problems in marking attendance. One way to resolve the problem is using Artificial Intelligence by using a Recognizer to identify people with their face and iris features. We implement the Face Recognition and the Iris Recognition using two models which run concurrently, one to Recognize the Face by extracting the features of the face and passing the 128-d points to the Neural Network (Mobile net and Resnet Architecture). which gives the identity of the person whose image was matched with the trained database and the other by extracting iris features to recognize people. For extracting iris features we use the Gabor filter to extract features from the eyes which are then matched in the database for recognition using 3 distance-based matching algorithms city block distance, Euclidean distance, and cosine distance which gives an accuracy of 88.19%, 84.95%, and 85.42% respectively. The face Recognizer model yields an Accuracy of 98%, while Iris Recognizer yields an accuracy of 88%. When these models run concurrently it yields an accuracy of 92.4%. © 2021 IEEE.

关键词： Face recognition

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Diabetic Retinopathy Detection

Diabetic Retinopathy Detection

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data Engineering and Communication systems (ICDECS), International Conference on

作者： S Sharanya Arnav Vinod Rai Sai Vedanth Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need.

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DIGITAL PHRENOLOGY: ALGORITHMS AND ETHICS FROM A systems PERSPECTIVE 67

DIGITAL PHRENOLOGY: ALGORITHMS AND ETHICS FROM A SYSTEMS PER...

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67th Annual Meeting of the International Society for the systems sciences, ISSS 2023

作者： Keykaan, Dimitrius Snyman, Dirk Kruger, Hennie Verhoef, Anné Unit for Data Science and Computing North-West University South Africa School of Philosophy North-West University South Africa Department of Information Systems University of Cape Town South Africa

ISBN: (纸本)9781713893455

Decisions made by machine learning algorithms have societal impacts, both good and bad. This study acknowledges the great good that machine learning algorithms contribute to society while also pointing out that there are negative impacts such as bias, discrimination and misrepresentation of data, etc. Algorithms often use either irrelevant or incomplete data to make life-changing determinations about certain groups of society. The algorithmic use of data that does not represent the true or actual ability of individuals or groups of people leads to digital phrenology. When algorithms are designed and tested outside the real world, it may not be obvious that digital phrenology may be caused. The ethical consequences of this problem can be mitigated if algorithms are not viewed as separate from the world it is eventually deployed into. Therefore, a more holistic systems view of algorithms, including their ethical consequences, should be taken. The purpose of this paper is to show that digital phrenology exists and that, once deployed, algorithms become part of a societal system where ethical values cannot be ignored. Various efforts and approaches to mitigate the ethical concerns related to unethical algorithmic behaviour have either done well in raising awareness or produced promising results. However, very little attention has been given to the explicit use of ethical theories as a possible approach to alleviating the problem. Therefore, two major ethical theories are proposed to assess the ethicality of hypothetical machine learning scenarios. A critical analysis of related literature and case studies is central in this study's inquiry method. Results show the existence of digital phrenology. It also reveals how viewing algorithms as a single component of a larger societal system can give a better perspective on the possible ethical consequences that arise from their use. This research contends that the use of ethical theories can contribute to addressing digital ph

关键词： Learning algorithms

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Set-valued prediction in multi-class classification 31

Set-valued prediction in multi-class classification

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019

作者： Mortier, Thomas Wydmuch, Marek Dembczýnski, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznán University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. © 2019 for this paper by its authors.

关键词： Forecasting

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Pivot probability induction for statistical machine translation with topic similarity

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Journal of Computational Information systems 2013年第20期9卷 8351-8359页

作者： Huang, Yanzhou Shi, Xiaodong Su, Jinsong Chen, Yidong Huang, Guimin Fujian Key Lab. of the Brain-Like Intelligent Systems Xiamen University Xiamen 361005 China Department of Cognitive Science School of Information Science and Technology Xiamen University Xiamen 361005 China School of Software Xiamen University Xiamen 361005 China Research Center on Data Science and Social Computing Guilin University of Electronic Technology Guilin 541004 China

Previous works employ the pivot language approach to conduct statistical machine translation when encountering with limited amount of bilingual corpus. Conventional solutions based upon phrase-table combination overlook the semantic discrepancy between the source-pivot corpus and pivot-target corpus and consequently lead to probability estimation inaccuracy for the induced translation rules. In this paper, the latent topic structure of the document-level training data is learned automatically and each phrase translation rule is assigned to a topic distribution. Furthermore, the phrase probability induction is carried out on the basis of the topic similarity, allowing the translation system to consider the semantic relatedness among different rules. Using BLEU as a metric of translation accuracy, we find out our system is capable of achieving a absolute improvement in in-domain test compared with the baseline system. © 2013 Binary Information Press.

关键词： Semantics

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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Traffic Sign Detection Using YOLOv8

Traffic Sign Detection Using YOLOv8

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Rahul Kumar Aniket Gupta Rajeswari D Department of Data Science and Business Systems School of computing College of Engineering and Technology

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy.

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

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Vehicular Communications 2025年 54卷

作者： J Akshya M Sundarrajan S. Amutha Rajesh Kumar Dhanaraj Adil O. Khadidos Alaa O. Khadidos Shitharth Selvarajan Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia 21589 Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds UK Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning.

关键词： Computational Geometry UAV Trajectory Convex Optimisation Voronoi Diagrams Delaunay Triangulations Spatial Coverage

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Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure 10

Who Responded to Whom: The Joint Effects of Latent Topics an...

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10th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2024

作者： Ji, Lu Chen, Lei Li, Jing Wei, Zhongyu Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China School of Data Science Fudan University China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Intelligent and Complex Systems Fudan University China

ISBN: (纸本)9798891761551

Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts. © 2024 Association for Computational Linguistics

关键词： Computational linguistics

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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