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Salt Deposits Identification Using Enhanced U-net-based Mixed Attention Loss Function for Semantic Segmentation 2

Salt Deposits Identification Using Enhanced U-net-based Mixe...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Anitha, D. Al-Attabi, Kassem Priyanka, M.S. Soujanya, Thota Bhuvaneshwarri, I. Srm Institute Of Science And Technology School Of Computing Department Of Computational Intelligence Tamilnadu Kattankulathur India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of AI-ML Bengaluru India Sr University School Of Agricultural Sciences Telangana Warangal India Government College Of Engineering Department Of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350381665

Numerous areas of Earth's surface with huge accumulations of oil and gas have high salt deposits under surface. Determining such deposits assists several countries in enhancing the storage capacity of petroleum reserves and exploring new ones. However, determining such a deposit is challenging because expert seismic images need human interpretation of salt bodies, which causes biased and greatly variable translations. This research proposes the Enhanced U-net with Mixed Attention Loss Function (EU-net-based MALF) to automatically and accurately identify whether or not the seismic images have salt deposit regions. Initially, the images are gathered from Tomlinson Geophysical Services (TGS) dataset to evaluate the model performance. Then, the nearest neighbor interpolation method and min-max normalization are employed for image resizing and normalizing. At last, the EU-net-MALF is performed for semantic segmentation. The EU-net-MALF achieves a better accuracy of 98.26% compared to existing methods like fully convolutional DenseNet, U-Net and deep supervision, and Interactive Salt Segmentation based on Convolutional Neural Network and GraphCut (SaltISCG). © 2024 IEEE.

关键词： Image enhancement

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Automatic Highlight Generation of Soccer Videos 14

Automatic Highlight Generation of Soccer Videos

引用

14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Park, Jiwoo Jwa, Younkyung Kwak, Jiin Lim, Jihun Kim, Sehee Pohang University of Science and Technology Ai Graduate School Pohang Korea Republic of Gwangju Institute of Science and Technology Ai Graduate School Gwangju Korea Republic of Ulsan National Institute of Science and Technology Ai Graduate School Ulsan Korea Republic of Korea University Department of Mathematics Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Due to soccer's immense popularity, numerous soccer matches are played each year and people watch or record these matches. However, considering that the duration of a game of soccer is at least 90 minutes and that important scenes such as goals and passes take up a small portion of the total time, we inevitably look for highlights rather than the entire game. Therefore, we propose a model that automatically generates a highlight video of a full soccer match in this paper. For this purpose, we searched diverse action-spotting models to choose a backbone model for our highlight classifier. Through various experiments, NetVLAD++ is selected as a backbone model and we created a fairly natural highlight generator by combining NetVLAD++ and our highlight classifier. Our results are meaningful because they can be applied in various fields and have huge room for improvement. © 2023 IEEE.

关键词： Automatic Video Summarization Highlight Generation Soccer Video Highlight

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Anomaly Detection in Social Networks using Improved Local Outlier Factor

Anomaly Detection in Social Networks using Improved Local Ou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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Performance Analogy of Autoencoders for Image Denoising

Performance Analogy of Autoencoders for Image Denoising

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Shanmukha Priya Sreenidhi Appalabatla Murali Krishna T. Dept. of Computer Science and Engineering (AI & ML) Malla Reddy University Hyderabad India Dept. of Computer Science and Engineering Ashoka Women's Engineering College Kurnool India

Image Denoising is one of the current day challenges in the field of image processing. Noise in the image is the existence of artifacts that do not start from the original picture content. Autoencoders have made considerable progress in image denoising because of their ability to reconstruct image inputs. Autoencoders for denoising images usually accepts a degraded image as input and is trained to predict the reconstructed image as its output. An effort has been made to analyse the functioning of selected autoencoders for image denoising, such as Basic Autoencoder, Denoising Autoencoder and Convolutional Denoising Autoencoder using the Modified National Institute of Standards and Technology database (MNIST) dataset. The performance of these models is evaluated and compared using Structural Similarity Index Measure, Peak Signal to Noise Ratio evaluation metrics. The strengths and weaknesses of each autoencoder are discussed and the factors that influence their performance are explored. Qualitative and quantitative analysis of the dataset provides an insight of effectiveness and efficiency of each autoencoder in denoising images. Observations show that Convolutional Denoising Autoencoder out-performs other autoencoders in terms of the evaluation metrics and better reconstructed image. This study can act as a base for researchers and practitioners to identify suitable autoencoders for denoising images in real-life applications.

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Multi Label Classification Algorithm Based on Conditional Local Correlations among Labels and Ensemble Learning 25

Multi Label Classification Algorithm Based on Conditional Lo...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Nasir, Ola Hadi, Wael Samara, Ghassan Al Daoud, Essam Aljaidi, Mohammad Khouj, Mohammed Talat Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Multi-Label Classification (mlC) is an advanced form of traditional classification, where each data instance is simultaneously associated with multiple labels. The importance of mlC has increased significantly in recent years, due to its wide range of application areas. Modern algorithms, when faced with the challenge of multiple labels, continue to show suboptimal performance, conversely, they show significantly superior performance when processing single-label datasets. This paper proposes a new algorithm to deal with the problem of transforming datasets from multiple labels to single labels by exploiting conditional and local correlations between labels after applying the Apriori predictive algorithm to extract relationships. Six ensemble learning models: Voting Hard, Voting Soft, Voting Weighted, Stacking LR, Stacking RF, and Stacking SVM, along with four classification models: SMO, DecisionStump, Random Forest, and Random Committee, were applied to three datasets. The algorithm showed effective and good performance by comparing the results of the proposed Multi Label Classification algorithm based on Conditional Local Correlations among Labels (mlC-CLCL) with several popular algorithms for dealing with multiple labels. © 2024 IEEE.

关键词： Contrastive Learning

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Multi Resolution based Graph Convolutional Networks for Detecting Communities in Social Networks

Multi Resolution based Graph Convolutional Networks for Dete...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Harsha, P. Obaid, Saif Mazumder, Debarshi Nagendar, Y. Almoussawi, Zainab Abed Malla Reddy Engineering College for Women Cse Department Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Sr University School of Computer Science and Engineering Warangal India Ahl Al Bayt University College of Islamic Science Karbala Iraq

ISBN: (纸本)9798350360660

Detecting the fundamental community structure of a social network is crucial in social network analysis. All though, there are many recent approaches in detecting communities, analysing large scale social networks at all resolutions is a challenging task. To solve this problem, the proposed research focuses on developing an efficient Multi Resolution-Graph Convolutional Network (MR-GCN) to identify the communities in social networks. The proposed method with the graph mining technique improves the output graph quality in identifying the communities. The conventional approaches depend on the labelled data, which is limited data in the real time applications. Initially, the nodes of raw graph are converted into embeddings and are further processed by multiple layers of a GCN to incorporate both the local and global structural features. The MR feature fusion is used to combine node embeddings from different resolutions, which includes all necessary information. The final phase includes Adam optimizer to train the model, and the overall experimental results obtained are accuracy of 97.85%, precision of 96.57%, and f1-score of 95.77%. The proposed method results are compared with the existing methods such as Graph Neural Network encoding scheme for a Multi-Objective Evolutionary Algorithm (GNN-MOEA) and Markov Random Fields GCN (MRFasGCN). © 2024 IEEE.

关键词： Convolutional neural networks

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Importance of Lightweight Algorithm for Embedded Security in Machine-to-Machine Communication towards Internet of Things 22

Importance of Lightweight Algorithm for Embedded Security in...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Mahendra Balkrishna Salunke Parikshit N. Mahalle Gitanjali Rahul Shinde Department of Computer Engineering Smt. Kashibai Navale College of Engineering India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology India Department of Computer Science and Engineering AI ML Vishwakarma Institute of Information Technology India

ISBN: (纸本)9781450399937

The Internet of Things (IoT) has been regarded as an important technological advancement that has changed how people think about and performs their activities. It is a framework that aims to create an efficient and effective method of managing various assets. The IoT also offers a paradigm shift that will enable the treatment of both physical and virtual assets uniformly. The increasing number of devices connected to the Internet will allow users to collect and exchange large amounts of data, making better decisions and enhancing their lives. One of the most important factors that the Internet is constantly working on is the development of new connectivity methods. One of these involves the Internet of Things, a framework for exchanging information among various sensors and devices. The rapid emergence and evolution of machine-to-machine communications will bring new opportunities for various applications. Unfortunately, many challenges still need to be resolved to make this technology work properly. One of the most important factors that need to be considered is the availability of secure infrastructure. One of the most important factors that need to be considered when implementing the security features of the Internet of Things is the availability of lightweight security algorithms. These are designed to help prevent unauthorized access and manipulation of the data sent and received by various devices. To function properly, the algorithms should not require a lot of resources, such as processing power. One of the advantages of implementing lightweight security algorithms is that they can help reduce the overall overhead of the security operations of the IoT. This is especially important since large-scale deployments are expected to involve thousands or millions of devices. This paper aims to comprehensively overview the various advantages of implementing these security algorithms.

关键词： IOT

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Text Analysis Tool

Text Analysis Tool

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2023 International Conference on Artificial Intelligence and Applications, ICaiA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Noronha, Perpetua F. Bhan, Madhu Niranjanamurthy, M. Chandana, D. Mount Carmel College Autonomous Department of Computer Science Bangalore India M.S.Ramaiah Institute of Technology Department of Mca Bangalore India B.M.S. Institute of Technology Department of Ai and Ml Bangalore India Utegration Company Department of Software Development Bangalore India

ISBN: (纸本)9781665456272

The automated analysis of electronic text is referred to as 'text processing'. The amount of online textual data is increasing, and automatic text processing techniques have the potential to be tremendously beneficial because they can gather more meaningful information faster. Features like text summarization, language translation, emotion classifier and headline generation of news articles are some of the popular ways of text processing. The fundamental goal of text summarization is to extract the most important information from a text and deliver it in a concise and legible form. The practice of transforming written text from one language into another such that it may be easily understood is known as language translation. Emotion classifier analyses the emotions and categorizes the text into various emotions. Headline generation is the process of obtaining the headlines from various news articles. This paper is about developing a text processing tool based on the concepts of Machine Learning and Natural Language Processing. Implementation of this tool allows for the automation of text processing. This tool aims to improve productivity and efficiency of processed data by faster generation of precise and meaningful data to all the providers across all platforms. © 2023 IEEE.

关键词： Text processing

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EQUIVARIANT GRAPH MECHANICS NETWORKS WITH CONSTRaiNTS 10

EQUIVARIANT GRAPH MECHANICS NETWORKS WITH CONSTRAINTS

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10th International Conference on Learning Representations, ICLR 2022

作者： Huang, Wenbing Han, Jiaqi Rong, Yu Xu, Tingyang Sun, Fuchun Huang, Junzhou Tsinghua University China Department of Computer Science and Technology Tsinghua University China Tencent AI Lab Department of Computer Science and Engineering University of Texas Arlington United States

Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained. Current methods, particularly the ones based on equivariant Graph Neural Networks (GNNs), have targeted on the first two challenges but remain immature for constrained systems. In this paper, we propose Graph Mechanics Network (GMN) which is combinatorially efficient, equivariant and constraint-aware. The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object. In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics. Moreover, to allow equivariant message passing in GMN, we have developed a general form of orthogonality-equivariant functions, given that the dynamics of constrained systems are more complicated than the unconstrained counterparts. Theoretically, the proposed equivariant formulation is proved to be universally expressive under certain conditions. Extensive experiments support the advantages of GMN compared to the state-of-the-art GNNs in terms of prediction accuracy, constraint satisfaction and data efficiency on the simulated systems consisting of particles, sticks and hinges, as well as two real-world datasets for molecular dynamics prediction and human motion capture. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Molecular dynamics

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative ai model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of ai in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of ai-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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