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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Zhou, Yongjie Wang, Dahan School of Computer and Information Engineering Xiamen University of Technology Xiamen Chinese Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Vehicle re-identification (re-ID) is an essential component of intelligent video surveillance, which attempts to solve the problem of retrieving specific vehicle instances. The technical challenge is mainly the requirement that the algorithm be robust under different viewing angles, resolutions, occlusions, and lighting conditions, yet different conditions can arise where the appearance of the exact vehicle varies dramatically. In contrast, the appearance of two different vehicles is exceptionally similar. To overcome this enormous challenge: we adopt a novel multi-scales attention network architecture, which will feature representation for feature maps at different scales to enhance the recognition of intricate parts, and introduce an inter-coordinate attention module to build interdependencies between component positions, making it possible to build a more robust vehicle model feature representation, and finally this paper also proposes a camera-based vehicle re localization method that enables the model to calculate the vehicle occurrence probability using the potential information among road network cameras as a post-processing step to re-correct the confidence level of the re-identification ranking. Experimental results show that the method outperforms the reference method in all indexes even without post-processing. At the same time, the camera confidence correction can be used in overlay with the reordering clustering correction method, which can improve the results by four percentage points and MAP up to 81.4%. © 2022 ACM.

关键词： Vehicles

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From Conversations to Knowledge: Enriching Movie Datasets with Knowledge Graph

From Conversations to Knowledge: Enriching Movie Datasets wi...

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international conference on computing and Networking Technology (ICCNT)

作者： Darshna Parmar Tanish Nagrani Rushabh Babre Pramit Mazumdar Dept. of CSE IIIT Vadodara Gujarat India Dept. of IT IIIT Vadodara Gujarat India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Knowledge graphs are invaluable assets for facilitating the dissemination of structured and semantically enriched data to both humans and machines while ensuring its accuracy and reliability. However, it has been observed that a direct approach of generating knowledge graph from existing benchmark data sources such as ReDial, DBPedia, etc. fails to highlight many crucial relationships between entities. In this work we proposed a strategy to enrich a knowledge graph by resolving multiple entity relationships generated from the ReDial movie dataset. First the missing entities and their corresponding relationships were identified. Subsequently, the publicly available large TMDb dataset is leveraged to align additional information such as genre, director, actor, writer, and year as nodes. Bidirectional links are then introduced to semantically enrich the knowledge graph. Validating a knowledge graph is considered as another important step. In this direction, we extract triplets in (movie, relationship, genre) format from the knowledge graph. The triplets are validated using IMDb which is a vast repository of movie details, and the Gemini Generative AI model. We compute accuracy of the extracted triplets and the results indicate improvement in the knowledge graph generation strategy and the IMDb as an efficient validator for our approach.

关键词： Accuracy soft sensors Semantics Knowledge graphs Oral communication Named entity recognition Motion pictures Resource description framework Reliability Recommender systems

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IRIS recognition and Classification Using Multiprocessing for pattern Matching and Encoding

IRIS Recognition and Classification Using Multiprocessing fo...

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2021 international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2021

作者： Borkar, Kavita Salankar, Suresh GH Raisoni College of Engineering Signal Processing and Communication System Centre of Excellence Department of Electronics Telecommunication Engineering Nagpur India G H Raisoni College of Engineering Department of Electronics Telecommunication Engineering Nagpur India

ISBN: (纸本)9781665425032

A biometric IRIS recognition system gives unique ID of an individual dependent on a one of a kind element or trademark moved by the individual. Biometric arrangements, for example, recognizable proof frameworks utilizing unique finger impression, iris, face, and palm print, hand calculation, signature, and so on. This paper proposes an individual distinguishing proof utilizing iris acknowledgment framework with the assistance of six significant advances for example picture procurement, confinement, Isolation, standardization, highlight extraction and coordinating and furthermore these six stages comprises a quantities of minor strides to finish each progression. The limits of the iris, as papillary and limbic limit, are identified by utilizing Canny Edge Detector and Circular Hough Transformation. We can utilize concealing engineer to disengage the iris picture structure the given eye picture, this secluded iris picture is changed from Cartesian to polar co-ordinate. Presently at long last concentrate the remarkable highlights (include vector) of the iris subsequent to upgrading the iris picture and afterward perform coordinating with measure on iris code utilizing Hamming Distance for acknowledgment and reject measures. © 2021 IEEE.

关键词： Biometrics

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Automatic Attendance Monitoring System (AAMS) 2nd

Automatic Attendance Monitoring System (AAMS)

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2nd conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Pandey, Abhinav Santosh Verma, Kumar Mayank Verma, Tanya Kumar, Abhay School of Computing Science Department Galgotias University Greater Noida India

ISBN: (纸本)9789811902956

As We Know Marking Attendance manually is something that takes more time and also leads to distraction of class whilst taking attendance. This form of taking attendance didn’t guarantee the safety of the whole data. So, we automated this system by creating an Automated Attendance Monitoring Application that will manage all the tasks related to attendance marking. This application is based on facial recognition technology which is nothing but identification of a particular person by peculiar features of their faces and it functions as a clear identifier of anyone (Matilda and Shahin in 2019 IEEE international conference on system, computation, automation and networking (ICSCAN), pp. 1–4, 2019[1]). As computer vision (CV) is growing day by day as it is technology that takes images/videos as its input and performs classification of images, detection and recognition of given input images (Deeba et al. in Int. J. Adv. Comput. Sci. Appl. 2019[2]). Its functionality is simply we had to move our camera into room and it will detect the faces and later train the images by our pre-trained model i.e. Haar Cascade Files and then recognizes every face and marks the attendance by matching with stored images data in firebase and stores attendance in csv file and this will save time and hence will avoid fake proxies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision (CV) Face recognition Firebase Haar cascade file Local binary pattern histogram

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Development of pattern-Based Test Case Catalog to Increase Reuse and Variety in software Testing

Development of Pattern-Based Test Case Catalog to Increase R...

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Alwan Kemal Dana Sulistiyo Kusumo Rosa Reska Riskiana Software Engineering Department School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

In software development, the quality control process is a critical element that aims to oversee and verify product quality. Poor quality control processes are frequently a key factor in software project failures. software testing is key to identifying defects, but designing an effective test suite requires a huge effort that can affect cost, effectiveness, maintenance time, and development schedules. The Directorate of the Center for Excellence in Information Technology (PUTI) as one of the IT support units within Telkom University, is currently facing the same challenges in managing the BaSO application, which is an application for making official letters online. This application requires further testing to ensure that each function runs properly in various browser and device environments. In this case, the reuse and pattern approach was proposed as a solution, and based on this approach, a test case catalog was created. To assess the design system's impact and effectiveness, an experimental study was conducted for the PUTI QA team to test the BaSO application. The experimental results show that the use of a test case catalog during the test case development process can increase the reusability of test cases, with an average precision value of about 50%. In addition, the obtained test case variation value also increased by 35.5%. The findings highlight the significant impact of using a test case catalog, thus contributing to a more effective software testing process.

关键词： software testing Schedules Process control Quality control Feature extraction software Product design Quality assessment pattern recognition software development management

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A Social Engineering Attacks Survey and Prevention Techniques

A Social Engineering Attacks Survey and Prevention Technique...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Riskhan, Basheer Sheikh, Md Amin Ullah Hossain, Md Shakil Hussain, Khalid School of Computing and Informatics Albukhary International University Jalan Tune Abdul Razak Kedah Alor Setar Malaysia

ISBN: (纸本)9789819780921

This study delves into the complex world of social engineering assaults, including phishing, grooming, pretexting, and other forms. The literature analysis highlights the need for a comprehensive cybersecurity strategy due to the growing threat of social engineering in the digital age. The technique examines every kind of assault, exposing strategies and suggesting countermeasures. Technological solutions, customized policies, and user education are examples of strategies. To reduce changing dangers, the conversation emphasizes the necessity of continual awareness, training, and flexible security solutions. This poll offers vital information that people and organizations may use to strengthen defenses against social engineering as our digital world develops, guaranteeing ongoing personal and organizational security within the limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sociology

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Distinct but correct: generating diversified and entity-revised medical response

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Science China(Information Sciences) 2024年第3期67卷 94-113页

作者： Bin LI Bin SUN Shutao LI Encheng CHEN Hongru LIU Yixuan WENG Yongping BAI Meiling HU College of Electrical and Information Engineering Hunan University School of Mathematics Sun Yat-sen University JD Technology National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences Xiangya Hospital of Central South University Teaching and Research Section of Clinical Nursing Xiangya Hospital of Central South University

Medical dialogue generation(MDG) is applied for building medical dialogue systems for intelligent consultation. Such systems can communicate with patients in real time, thereby improving the efficiency of clinical diagnosis. However, predicting correct entities and correctly generating distinct responses remain a great challenge. Inspired by actual doctors' responses to patients, we consider MDG a two-stage task: entity prediction and dialogue generation. For entity prediction, we design an ent-mac post pre-training strategy by leveraging external medical entity knowledge to enhance the pre-trained model. For dialogue generation, we propose an entity-aware fusion MDG method in which predicted entities are integrated into the dialogue generation model through different encoding fusion mechanisms, using information from different sources. Because the diverse beam search algorithm can produce responses with entities that deviate from the predicted entities, an entity-revised diverse beam search is proposed to correct the entities entailed in the generated responses and make the generated responses more distinct. The experimental results on the China conference on Knowledge Graph and Semantic computing 2021(A/B tests) and the international conference on Learning Representations 2021(online test) datasets show that the proposed method outperforms several state-of-the-art methods, which demonstrates its practicability and effectiveness.

关键词： medical entity prediction ent-mac post pre-training strategy entity-aware fusion medical dialogue generation encoding fusion mechanism entity-revised diverse beam search

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Investigation of Machine Learning Algorithms for pattern recognition in Image Processing

Investigation of Machine Learning Algorithms for Pattern Rec...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： Chennaiah Kate C. Kalpana Arvind Sharma Ajay Singh Yadav Ashok Kumar S. Sandeep Kumar Department of Information Technology St. Peter's Engineering College Hyderabad Telangana India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Electronics and Communication Engineering Government Women Engineering College Ajmer Rajasthan India Department of Mathematics SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

In order to recognize patterns in images, this study tests the performance of many “machine learning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods (“deep learning, support vector machines, decision trees, and random forests”) and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its “accuracy, precision, recall, and F1 score”, among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms.

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Eyecheck Diabscan: A Productive Approach for Few-Shot Excellence in Retinopathy Diagnosis

Eyecheck Diabscan: A Productive Approach for Few-Shot Excell...

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Advanced computing & Communication Technologies (ICACCTech), international conference on

作者： Nuthanakanti Bhaskar E. Krishnaveni Sinde Jayavanth Rao Kumbala Pradeep Reddy G Vinoda Reddy Avala Raji Reddy Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering (DS) CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Hyderabad India Department of Mechanical Engineering CMR Technical Campus Hyderabad India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The design and implementation of a few-shot learning-based diabetic retinopathy diagnostic system is the focus of this research project. According to the NEI (National Eye Institute), The United States report 2021, diabetic retinal degeneration is one of the most common causes of vision loss in people aged 20 to 74. Early recognition and treatment are therefore deemed essential. The National Eye Institute is a resource-based informational center about diseases and eye health. The primary objective of this endeavor is to improve the few-shot learning technique's capacity for early-stage diabetic eye disease detection. This suggested model may operate using a restricted amount of data, i.e., less data processing by enhancing data optimizing, and localize the necessary discriminative regions. Additionally, it enables the model to recognize and follow unique, novel patterns among the inputs. This approach is going to operate along with an assessment of proposals pertaining to patients' geographic and financial obstacles. Diabetic Retinopathy Identification is applied in the medical and healthcare sectors. This enables us to manage progression risk, forecast therapy results, and lessen adverse consequences and acute implications.

关键词： Diabetic retinopathy Accuracy Shape Shape measurement Retina pattern recognition Ophthalmology Proposals Few shot learning Recommender systems

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Exploring the Potential of Deep Learning in Diagnosing Eye Diseases

Exploring the Potential of Deep Learning in Diagnosing Eye D...

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international conference on computing and Networking Technology (ICCNT)

作者： R Roopashree Rajkumari Ghosh Murli Manohar Gaur Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep gaining knowledge is a powerful artificial intelligence approach that has been applied to numerous clinical areas, which include computer imagination and prescient, herbal language processing, medical picture analysis, and now, eye ailment diagnosis. Even as still in its infancy, deep studying has begun to show capability for assisting docs and medical practitioners in diagnosing eye diseases quickly and appropriately. With the aid of the use of deep knowledge of models to extract meaningful functions from patient-specific eye imaging, medical doctors are capable of making decisions with multiplied self-belief and accuracy. This text will speak about the contemporary state of research in the region of deep getting to know and its ability to improve the diagnosis of eye sicknesses. Deep getting-to-know architectures, especially convolutional neural networks (CNNs), have been used to effectively stumble on and classify diverse types of eye sicknesses in retinal pix. A CNN is a synthetic neural network that simplifies the technique of photo recognition and classification by extracting features inclusive of edges and patterns. These capabilities are used to make predictions approximately the affected person’s eye fitness. Every other utility of deep mastering is inside the analysis of diabetic retinopathy (DR). DR is a situation caused by excessive blood sugar stages and is the main motive of blindness among people with diabetes.

关键词： Deep learning Neural networks Sensitivity and specificity Eye diseases Feature extraction Retina pattern recognition Convolutional neural networks Prognostics and health management Medical diagnostic imaging

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