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Face Recognition via Thermal Imaging: A Comparative Study of Traditional and CNN-Based Approaches 24

Face Recognition via Thermal Imaging: A Comparative Study of...

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8th International Conference on Machine Learning and Soft Computing, ICMLSC 2024

作者： Lin, Shinfeng Chen, Luming Chen, Wensheng Chang, Boyu Dept. of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9798400716546

In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the traditional face recognition, we employ Random Forest (RF) and Support Vector Machine Classifier (SVM) techniques. Conversely, CNN-based face recognition leverages Convolutional Neural Networks (CNN) to identify individuals. Our research involves a comprehensive evaluation conducted across different databases, including the PUCV Drunk Thermal Face (PUCV-DTF) and the UCH Thermal Temporal Face (UCH-TTF) datasets. To emulate real-life scenarios, we introduce elements such as glasses, face mask, and noise into the original thermal images during experimentation. The recognition rates of traditional and CNN-based methods for small size and middle size databases achieve 90% and 100%, respectively, under a challenging condition, wearing glasses and mask. Experimental results demonstrate the feasibility and effectiveness of our proposed method, showcasing its robustness in tackling various challenges. © 2024 Owner/Author.

关键词： Infrared imaging

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A Software Framework Based on JUnit for Automated Software Testing in computer science Courses 54

A Software Framework Based on JUnit for Automated Software T...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： Software testing

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Exploring the Forward-Forward Algorithm to Train Neural Networks for Camera Trap Images on the Edge 10

Exploring the Forward-Forward Algorithm to Train Neural Netw...

引用

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Alhammadi, Hamad Mkadmi, Meriam Mitra, Rohan Zualkernan, Imran American University of Sharjah Computer Science and Engineering Dept. Sharjah United Arab Emirates

ISBN: (纸本)9798350373011

Camera trap systems and motion-activated field cameras are edge devices commonly used for animal monitoring in the wild. Using such technologies has revolutionized ecological research and wildlife monitoring, enabling non-invasive study of animal movement, behavior, and population dynamics. However, the manual analysis and classification of the vast number of camera trap images can be time-consuming and challenging for researchers. The image data from these cameras is also naturally class unbalanced because some species are rarer than others, and typically more than half the images are 'ghost' images triggered by anomalous events like the wind. Neural networks using the backpropagation (BP) algorithm have been used to classify camera trap images. Since the deployment context of each camera trap is unique, there is a need to custom-train the classification model for each camera on the edge. Unlike BP, the newly proposed Forward-Forward (FF) algorithm reduces the memory needs for training on the edge. This paper explores how well the FF algorithms could distinguish between 'ghost' and 'animal' images for camera trap images when trained on the edge. FF algorithm on different image sizes and varying the number of neurons in each layer is investigated. The primary conclusion is that both BP and FF have similar performance and inference time on this task. In addition, FF is slower to train but requires 60% less memory than BP while training. © 2024 IEEE.

关键词： Population dynamics

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From Ranch to Worldwide:Observing Soil Welbeing Patterns

From Ranch to Worldwide:Observing Soil Welbeing Patterns

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

作者： Divakar, K.M. Ankith Gowrishankar Esturi, R. Anil, P.S. Namith, D. Chethan, A. S J C Institute of Technology Dept. of Computer Science Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Declining soil wellbeing represents a huge danger to worldwide food security and rural maintainability. Precisely checking soil wellbeing patterns and anticipating future land appropriateness at nearby and worldwide scales are essential for illuminating feasible administration practices and strategy intercessions. This paper proposes a clever structure that use the force of large information investigation to address this test. We expect to overcome any issues between ranch level checking and worldwide experiences, engaging partners at each level to come to informed conclusions about land use and management. Our structure depends on the mix of assorted information sources. High-goal remote detecting information, including satellite symbolism and aeronautical photography, will give basic experiences into soil properties, crop conditions, and land the board rehearses. We will additionally improve the examination by consolidating promptly accessible open-access datasets on environment, soil overviews, and financial data. This exhaustive information combination approach permits us to catch the diverse idea of soil wellbeing and land reasonableness dynamics. Extracting significant experiences from these tremendous datasets requires clever examination devices. We will use a setup of AI procedures, including regulated and solo learning calculations, to reveal stowed away examples and connections inside the information. Directed learning will empower us to foster prescient models for soil wellbeing appraisal and land appropriateness planning, while solo learning will assist with recognizing remarkable attributes and likely peculiarities in various farming *** system will create constant soil wellbeing maps, featuring areas of corruption, supplement lacks, and potential improvement open doors. This enables ranchers to come to informed conclusions about manure application, water the board, and harvest choice, advancing their activities for expanded yield and long ha

关键词： Fertilizers

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Demystifying Existing Sentiment Analysis Approaches of Hindi and English Languages using Machine Learning 1

Demystifying Existing Sentiment Analysis Approaches of Hindi...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Om, Hari Kumar Sharma, Avinash Sharda University Sharda School of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Gr Noida India

ISBN: (纸本)9798350356816

The number of operational information acquired, such as product reviews in several dialects, has expanded as the digital era has progressed. Many businesses, such as online service providers, benefit greatly from information analysis. Sentiment analysis (SA) is the technique of deciphering & categorizing the opinions/viewpoints/sentiments following the content (assessment) of user via text analysis tool (SA). Sentiment Analysis is a Natural Language Processing (NLP) program that is exercised in order to determine opinions of online consumers' comments, evaluations, thoughts, viewpoints and so on. Nowadays, social networking websites like Facebook & Twitter are recurrently utilised to submit consumer assessments regarding a variety of topics like politics, news, movies, fashion, food, etc. Viewpoints, Opinions & Reviews are important in determining the degree of gratification of consumers with a given unit. To determine the polarity, which might be neutral, negative, or positive. This study discusses a Sentiment Analysis technique on different social media platform reviews in Hindi. This paper presents a comparative study of sentiment analysis on Hindi and English texts using machine learning techniques. Datasets are accumulated of Hindi & English texts from various online sources, including social media platforms, news websites, and movie review sites. Various machine learning algorithms, including Naive Bayes, Support Vector Machines, Random Forest, and Logistic Regression, to perform sentiment analysis on the datasets. © 2024 IEEE.

关键词： Decision trees

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An Immersive Sound Effects System Based on Dynamic Spatial Perception and Architectural Response 9

An Immersive Sound Effects System Based on Dynamic Spatial P...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Lai, Wen-Hsing Ma, Chia-Hao Wang, Siou-Lin National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350318241

This paper achieves the conversion of monaural audio into spatially immersive stereo audio through the use of binaural speech synthesis and Pyroomacoustics. It integrates dynamic spatial perception with the acoustic response of architectural spaces to recreate immersive audio experiences in desired environments like concert halls. The system encompasses diverse options for motion trajectory generation and architectural space modeling within its interface, allowing users to customize and merge various environmental spatial effects and surround modes, delivering an unparalleled auditory encounter. © 2024 IEEE.

关键词： Speech synthesis

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Rubbish Revolution: A Smart Solution for Effective Plastic Waste Management and Collaborative User Engagement in Responsible Disposal 3

Rubbish Revolution: A Smart Solution for Effective Plastic W...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Sunil, K.S. Avinash, K.B. Diljith, P.A. Hafis Muhammed, T.K. Nirmal, Midhun Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381931

The management of plastic waste is a pressing global issue that demands effective solutions to mitigate it's negative impacts on the environment, health and economies. This work proposes the development of a web based application that enables users to manage their plastic waste by ensuring a method for it's proper disposal. The idea is suitable for a WiFi enabled campus where the dustbins are equipped with the required hardware/software for barcode and QR code scanning. The work is coined the name 'Rubbish Revolution', abbreviated as 'RR'. RR has three roles, an admin, the shopkeepers and the users. For each user, a unique user ID along with a unique QR code is generated when they register via RR. RR allows the store to scan the barcode/QR code on the plastic products and assigns that particular product information to the user who has purchased the product. This information enable users to assign responsibility for the plastic waste generated. The user would be rewarded in the form of credit points for the proper disposal of these plastic wastes. If the owner of the plastic waste improperly disposed it and someone else disposes it properly, the owner gets negative points while the disposer earns points. The users can check their credit points and redeem them when the points has reached certain limit. The points are given a value that can be redeemed at the store. The admin can edit the points of the user if necessary. RR seeks to provide a practical and innovative solution to the plastic waste problem by leveraging technology and user engagement. © 2024 IEEE.

关键词： Bar codes

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Almost Order Preserving Matching 16

Almost Order Preserving Matching

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Liao, Wen-Wen Yang, Chang-Biau National Sun Yat-sen University Dept. Computer Science and Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350377903

In this paper, we define a new problem, called the almost order-preserving matching (aOPM) problem, which is a combined variant of order-preserving matching (OPM) and the longest almost increasing subsequence (LaIS). Given a numeric pattern P, a numeric text string T, and a tolerance constant c) the aOPM problem seeks to find the substring of T that each element has the almost same rank as the corresponding element in P. In an almost increasing sequence, small drops within c are permitted while still maintaining a generally increasing trend. Our algorithm for solving the aOPM problem consists of three stages: fingerprint, almost rank, and bipartite matching. The time complexity of the proposed aOPM algorithm is O(n+k1m m+R+k2m3), where n= T, m= P, R is the number of separation rank combinations, k1 and k2 denote the number of candidate substrings identified in the fingerprint and almost rank stages, respectively. © 2024 IEEE.

关键词： almost increasing subsequence bipartite matching fingerprint order-preserving matching string matching

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A Multi-Routing Paths' Exploration Algorithm for Multi-paired Communicating Vehicles' Data Transmission Offloading using the Multi-access Edge Computing Architecture 4

A Multi-Routing Paths' Exploration Algorithm for Multi-paire...

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4th European Conference on Communication Systems, ECCS 2024

作者： Huang, Chung-Ming Tsai, Cheng-Han National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350377927

This work proposed a novel method for multiple communicating paired vehicles' data transmission offloading using the Multi-access Edge Computing (MEC) technique. When two paired communicating vehicles are communicating with each other through cellular network's Base Stations (BSs), the data transmission can be offloaded to the Cellular Vehicle-to-Vehicle (C-V2V) path that exists between these two vehicles using 5G NR's sidelink communication;these two paired communicating vehicles can switch back to the Cellular Vehicle-to-Infrastructure (C-V2I) communication to communicate with each other when the corresponding C-V2V path is broken. Using the proposed method, the MEC architecture serves as a centralized computing mechanism to determine the availability of k-hop C-V2V paths for paired communicating vehicles' data transmission offloading. Each vehicle periodically reports its context, such as position, speed, driving direction, and C-V2V sidelinks' Channel Busy Ratios (CBRs), to the MEC server to derive feasible k-hop C-V2V data transmission offloading paths. The simulation result demonstrates that the proposed method has (i) better throughput compared to the method of relying solely on C-V2I communication through BSs and (ii) better throughput, higher offloading fraction and lower average data loss rate comparing with the other k-hop-limited CV2V data transmission offloading method. © 2024 IEEE.

关键词： Edge computing

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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