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International Conference of information and Communication technology (ICoICT)

作者： Yogi Wiyandra Iskandar Fitri Yuhandri Computer System Departement Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Departement Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Ear infection, particularly in the middle ear, can lead to precipitation of auditory ossicles─a frequent condition causing marked hearing loss. This study aimed to locate ossicles by extracting features from images obtained by computed tomography (CT) scans. This would help doctors in the rapid and precise diagnosis of patients with infections in the auditory ossicles. The research methodology encompassed extracting features from radiological CT-Scan medical images, for which a pre-processing phase was warranted to enhance image fidelity. The process encompassed contrast stretching, cropping, resizing, filtering, and optimization of contrast to yield images of sharpened clarity specific to the topic of the study. After that, the paper involved object detection through the analysis of the Regions of Interest after marking particular portions in the images. It was followed by shape extraction to find the patterns that define the boundary of the infection. This helps in the differentiation between normal and eroded ossicular conditions. This work attains a rate of 92% in detection accuracy, offers an idea about the extension of auditory ossicles, and much more explicit images are achieved than the original ones. Such findings can help improve the diagnosis skills of the otolaryngology specialist and, therefore, aid better results in patient management. The future work can develop real-time processing capabilities to assist otolaryngologists during surgery or in clinical settings, providing immediate feedback and enhancing decision-making processes.

关键词： Accuracy Computed tomography Decision making Surgery Medical services Ear Feature extraction Bones Real-time systems Otorhinolaryngology

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An improvised CNN model for fake image detection

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International Journal of information technology (Singapore) 2023年第1期15卷 5-15页

作者： Hamid, Yasir Elyassami, Sanaa Gulzar, Yonis Balasaraswathi, Veeran Ranganathan Habuza, Tetiana Wani, Sharyar Information Security and Engineering Technology Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates Department of Management Information System College of Business Administration King Faisal University Riyadh Saudi Arabia Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Computer Science and SWE College of Info. Tech UAEU Al Ain United Arab Emirates Department of Computer Science International Islamic University Malaysia Kuala Lumpur Malaysia

The last decade has witnessed a multifold growth of image data courtesy of the emergence of social networking services like Facebook, Instagram, LinkedIn etc. The major menace faced by today’s world is the issue of doctored images, where-in the photographs are altered using a rich set of ways like splicing, copy-move, removal to change their meaning and hence demands serious mitigation mechanisms to be thought of. The problem when seen from the prism of Artificial intelligence is a binary classification one, where-in the characterization must be drawn between the original and the manipulated images. This research work proposes a computer vision model based on Convolution Neural Networks for fake image detection. A comparative analysis of 6 popular traditional machine learning models and 6 different CNN architectures to select the best possible model for further experimentation. The proposed model based on ResNet50 employed with powerful preprocessing techniques results in a perfect fake image detector having a total accuracy of 0.99 having an improvement of around 18% performance with other models. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Intelligence Classification Convolution neural networks Doctored images Splicing

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DFIL: an Efficient Web Service Prediction Method based on Deep Feature Interactive Learning 25

DFIL: an Efficient Web Service Prediction Method based on De...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhu, Ke Wang, Zhixin Jiang, Tao Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China He'nan University of Economics and Law School of Computer and Information Engineering Zhengzhou China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation. © 2022 IEEE.

关键词： Quality of service

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Ship flame segmentation by fusing DASPP and PE-SCAM attention 3

Ship flame segmentation by fusing DASPP and PE-SCAM attentio...

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3rd International Conference on Neural Networks, information and Communication Engineering, NNICE 2023

作者： Zheng, Xuecong Chen, Li Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Wuhan Institute of Marine Electric Propulsion Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China

ISBN: (纸本)9798350335972

To solve the problem that the flame area of small targets on ships can not be easily segmented and the flame boundary is obviously affected by other background such as smoke, this experiment improves DeepLabV3+ algorithm from the following three aspects. Firstly, Dense Atrous Spatial Pyramid Pooling (DASPP) was improved to enhance the extraction and utilization of flame boundary and small target flame features. Secondly, in order to avoid the influence of background such as smoke around the flame, the high-level flame features and shallow flame features obtained by DASPP were combined with the improved Attention Mechanism (PE-SCAM) to obtain more context information in spatial and channel dimensions, so as to enhance the processing effect of the model on the flame and its boundaries. Finally, the traditional cross loss function is optimized to reduce the influence caused by the imbalance of positive and negative samples between the flame and the background. Experimental results show that the MIou of the proposed algorithm on the ship flame data set reaches 88.43%, which can realize the accurate segmentation of small target flames and flame boundaries on the basis of ensuring high real-time performance. © 2023 IEEE.

关键词： Ships

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

A Framework for Deepfake Detection using Convolutional Neura...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Soundarya, B.C. Gururaj, H.L. Naveen Kumar, C.M. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Lung cancer

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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Improve the Efficiency and Security by Digitalizing the Sri Lankan Police Department 3

Improve the Efficiency and Security by Digitalizing the Sri ...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Yapa, K. Senaratne, A.N. Pathirathna, R.P.D.T.D. Priyamantha, Y.P.C.N. Dias, W.D.S. Katukithulgala, B. Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Police, as the main law enforcement organization in Sri Lanka, play a major role in preserving the public order, preventing criminal, terrorist activities, and enforcing the law. As a developing nation, Sri Lanka Police Department is moving forward with emerging technologies to effectively manage operations and rapidly respond to opportunities and challenges. Compared to developed countries, Police Department is still far more behind when working with modern technologies. Lack of coordination between departments, duplication of data, delay of services, unauthorized data modification, absence of data analysis and prediction systems, lack of transparency, and corruption were identified as the main issues that exist in the police department. In this paper, we propose 4 main improvements which help to overcome those issues and challenges. Improving the existing police clearance certificate system to issue digital certificates capable of verifying the authenticity, integrity of the document and providing efficient delivery and great user experience to the applicants. Implementing a Blockchain network to store confidential data securely by providing transparency and integrity. Developing a machine learning model to predict crimes geographically and predict fight crime action based on data gathered from a real-Time camera system (CCTV). © 2021 IEEE.

关键词： Forecasting

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TokenMatcher: Diverse Tokens Matching for Unsupervised Visible-Infrared Person Re-Identification 39

TokenMatcher: Diverse Tokens Matching for Unsupervised Visib...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Xiao Liu, Lekai Yang, Bin Ye, Mang Wang, Zheng Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science Wuhan University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)157735897X

Unsupervised visible-infrared person re-identification (US-VI-ReID) seeks to match infrared and visible images of the same individual without the use of annotations. Current methods typically derive cross-modal correspondences through a single global feature matching process for generating pseudo labels and learning modality-invariant features. However, this matching approach is hindered by both intra-modality and inter-modality discrepancies, which result in imprecise measurements. As a consequence, the clustering of individuals with single global feature is often incomplete and unreliable, leading to suboptimal performance in cross-modal clustering tasks. To address these challenges and to extract cross-modality discriminative identity information, we propose a TokenMatcher, which encompasses three key components: Diverse Tokens Matching (DTM), Diverse Tokens Neighbor Learning (DTNL), and the Homogeneous Fusion (HF) Module. DTM utilizes multiple class tokens within the visual transformer framework to capture diverse embedding representations, thereby facilitating the integration of fine-grained information essential for reliable cross-modality correspondences. DTNL enhances the intra-modality and inter-modality consistency among diverse tokens by refining neighborhood sets with insights from neighboring tokens and camera information, promoting robust neighborhood learning and fostering discriminative identity information. Additionally, the HF module consolidates clusters of the same identity while effectively separating those of different identities. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets demonstrate the efficacy of the proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Unsupervised learning

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computer Science, information technology and Engineering (ICCoSITE), International Conference on

作者： Larissa Navia Rani Yuhandri Yuhandri Information System Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia

In this study aimed to measure the level of similarity between two logos, both those that look different and those that look the same. This can be realized by forming a logo image database that is stored in a logo image database derived from various existing logo image data. This research uses 4 logo images as data testing and 210 image data for the database as data training. All of the logo images used come from the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham RI) West Sumatra Regional Office. The size images are color images with a pixel size of 320 x 320 pixels, the purpose of which is for the process of dimensional uniformity of the images to be studied. This research uses Content Base Image Retrieval (CBIR) method to search for images from a large image database than using Convolutional Neural Network (CNN) type Residual Network (ResNet-18) Architecture to get the similarity score accurately. The result of this implementation is the formation of an automatic distribution of training images and validation images with 147 training image data values (70%) and 63 validation images (30%) of the 210 existing images. The result of this research is producing the algorithm to implement the method and the tool software application to measure the similarity of logo images. The accuracy of this tool is 93.65% with a total of 84 iterations.

关键词： Training Image retrieval Software algorithms computer architecture Feature extraction Software Convolutional neural networks

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