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Distributed computing and Optimization Techniques (ICDCOT), International Conference on

作者： L Umasankar Anitha D S Trisheela Hassan M. Al-Jawahry Subba Reddy Borra Electrical and Electronics Engineering S.A.Engineering College Poonamallee Tiruvallur India Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Dept of CSE Nitte Meenakshi Institute of Technology Bengaluru India The Islamic university Najaf Iraq Computer Science And Engineering Malla Reddy Engineering College for Women (Ugc-Autonomous) Maisammaguda Hyderabad India

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design.

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Mixture of Knowledge Minigraph Agents for Literature Review Generation 39

Mixture of Knowledge Minigraph Agents for Literature Review ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

ISBN: (纸本)157735897X

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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An Improved Driver Drowsiness Detection using Haar Cascade Classifier

An Improved Driver Drowsiness Detection using Haar Cascade C...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： S. S. Saranya Ravi Mytresh Mylavarapu Manideep Dept. Department of Computing Technologies College of Engineering & Technology Organization SRM Institute of Science and Technology Chennai India Dept. of Computer Science & Engineering Core Organization: SRM Institute of Science and Technology Chennai India

Driver drowsiness detection is a system that uses various sensors and algorithms to detect when a driver is becoming fatigued or drowsy. When a driver is drowsy, they are not operating in full alert mode rather, they are only somewhat tired. The majority of traffic accidents throughout the world are caused by fatigued and sleepy drivers. This can be done through monitoring eye movements, facial expressions, and head position, as well as analyzing driving patterns such as sudden lane changes or prolonged periods of inactivity on the steering wheel. The system can then alert the driver to take a break or pull over to rest, potentially preventing accidents caused by drowsy driving. It can also be used in commercial vehicles for safety of drivers and other people on the road. Accidents in the present day are increasingly being caused by this consisting of ocular fatigue. These characteristics show that the driver's condition is not right. For the purpose of detecting sleepiness, the ratio of distances between the horizontal and vertical eye landmarks is calculated using the EAR (Eye Aspect Ratio). The proposed method calculates the landmark since the landmarks are identified precisely enough to do so. This study determines the eye aspect ratio (EAR), a single scalar quantity that characterizes the opening of the eyes in each frame, and extracts it. Finally, an SVM classifier developed by Haar Cascade recognizes eye blinks as a pattern of EAR values. However, both driver fatigue and distraction may result in slower response times, reduced driving efficiency, and a higher risk of being involved in an accident. The output is delivered to the detection system, and the alert will be activated, if the driver's degree of fatigue and the estimated amount of sleepiness are determined. The true number of accidents brought on by driver fatigue is difficult to ascertain because it is frequently under reported. The driver typically doesn't notice the small change from being tired to

关键词： Support vector machines Sleep Wheels Ear Fatigue Sensor systems Sensors

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On Learning with LAD

arXiv

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arXiv 2023年

作者： Jothishwaran, C.A. Srivastava, Biplav Singla, Jitin Gangopadhyay, Sugata Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science and Engineering AI Institute College of Engineering and Computing University of South Carolina ColumbiaSC29208 United States Department of Biosciences and Bioengineering Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

The logical analysis of data, LAD, is a technique that yields two-class classifiers based on Boolean functions having disjunctive normal form (DNF) representation. Although LAD algorithms employ optimization techniques, the resulting binary classifiers or binary rules do not lead to overfitting. We propose a theoretical justification for the absence of overfitting by estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models where hypothesis sets consist of DNFs with small number of cubic monomials. We illustrate and confirm our observations empirically. © 2023, CC BY.

关键词： Boolean functions

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Design of a Hybrid WDMA-Optical-CDMA over Multi-Mode Fiber Transmission System based on LG Modes for Short Haul-Local Area Network 1

Design of a Hybrid WDMA-Optical-CDMA over Multi-Mode Fiber T...

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1st International Recent Trends in Technology, engineering and computing Conference, IRTTEC 2020

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Fareed, Alaa Al-Dawoodi, Aras Mohsin, Aram Hewa School of Human Development and Techno Communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Computer Technical Engineering Alkitab University Iraq Criminal Evidence Department Al-Qalam University College Iraq

Various optical communication technologies use Optical-Code Division Multiple Access (Optical-CDMA) as a channel access technique, which allows transmission of different users simultaneously over the optical communication channel. Therefore, Optical-CDMA permits free undue interference transmission of several users. Whereas Wavelength-division multiple access (WDMA) is a technique that can be implemented on how the optical channel can be divided into subchannels. This paper proposes a Hybrid WDMA-Optical-CDMA over multi-mode fiber (MMF) transmission system for short haul-local area network. The WDMA system is carried out by using four Laguerre-Gaussian (LG) modes that operate over four main wavelengths (1551 nm, 1550 nm, 1549 nm and 1548 nm). Additionally, the Optical-CDMA system is carried out by using one dimension Zero Cross-Correlation (ZCC) code to overcome multiple access interference (MAI) between the four ZCC codes. The performance of our system is evaluated based on eye diagrams, Q-Factor and bit-error rates (BER) measurements. © Published under licence by IOP Publishing Ltd.

关键词： Multiple access interference

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PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

PupilRec: Leveraging Pupil Morphology for Recommending on Sm...

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作者： Shen, Xiangyu Jiang, Hongbo Liu, Daibo Yang, Kehua Deng, Feiyang Lui, John C. S. Liu, Jiangchuan Dustdar, Schahram Luo, Jun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China The City University of Hong Kong School of Graduate Studies Hong Kong The Chinese University of Hong Kong Computer Science and Engineering Department Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Vienna University of Technology Distributed Systems Group Vienna1040 Austria Nanyang Technological University School of Computer Engineering 639798 Singapore

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users' inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users' inner feelings, we consider using the change of pupil size when browsing to model users' preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users' pupil size change information through the front camera of smartphones;it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user-product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users' preference and can recommend products users interested in. © 2014 IEEE.

关键词： Smartphones

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AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method

arXiv

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arXiv 2023年

作者： Ayedi, Mohamaed Foued Salem, Hiba Ben Hammami, Soulaimen Said, Ahmed Ben Jabbar, Rateb CHabbouh, Achraf The University of Carthage Higher School of Communications of Tunis Tunisia Stile.ai Department of Computer Science and Engineering Qatar University Doha Qatar KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

Existing fashion recommendation systems encounter difficulties in using visual data for accurate and personalized recommendations. This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations. When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements. These captions guide retrieval from a global fashion product catalog to offer similar alternatives that fit the visual characteristics of the original image. On a dataset of over 100,000 categorized fashion photos, the pipeline was trained and evaluated. The F1-score for the object detection model was 0.97, exhibiting exact fashion object recognition capabilities optimized for recommendation. This visually-aware system represents a key advancement in customer engagement through personalized fashion recommendations. © 2023, CC BY.

关键词： Recommender systems

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： P.S. Ramesh I. Sudha J. Jagannathan Rajkumar Pandiarajan A. Ponmalar Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India R.M.K. Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

With a steady increase in the adoption of cloud storage systems, the client’s data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today’s sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats.

关键词： Resistance Cloud computing Technological innovation Multi-factor authentication Quantum computing System performance Memory management Software performance Encryption Protection

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Research on the Application of computer Vision Based on Deep Learning in Autonomous Driving Technology

arXiv

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arXiv 2024年

作者： Zhang, Jingyu Cao, Jin Chang, Jinghao Li, Xinjin Liu, Houze Li, Zhenglin Division of the Physical Sciences The University of Chicago ChicagoIL United States Dallas TX United States The Kyoto College of Graduate Studies for Informatics Kyoto Japan Department of Computer Science Columbia University New YorkNY United States Department of Computer Science New York University New YorkNY United States Department of Computer Science and Engineering Texas A&M University College StationTX United States

This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multitask joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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