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data-Driven Models for Water Potability: A Machine Learning Approach 3

Data-Driven Models for Water Potability: A Machine Learning ...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Mishra, Nisha Sangeetha, S. V. Tresa Deepa, K. Venugopal, Vivek Amrita School of Engineering Bengaluru Campus Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman Amrita School of Engineering Bengaluru Campus Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350361186

Water is an essential component in our daily lives. Water is used for different purposes, such as drinking, industrial, and irrigation. Many problems can be seen nowadays because of containment water. Water is being polluted, and as a result, the environment is experiencing many negative effects, such as global warming and different types of diseases spreading in the environment. Different ML models are used to predict water quality because it's very important for the environment and surroundings. Water Potability can be predicted by machine learning using parameters such as PH, Conductivity, Turbidity, Solids, Hardness, silver, nitrates, cadmium, lead, etc. As there is a huge increase in the contamination of water by industries from urban to rural areas, it is affecting the environment and our way of life. So good accessibility and better quality of water are needed. The point of this work is to get goodquality of water by using machine learning techniques. In recent years, numerous contaminants have put water quality at risk. Forecasting and model development for assessing water quality have become essential to control water pollution. The level of quality is measured using variables like water quality indexing (WQI) and water quality categorization. © 2024 IEEE.

关键词： Support vector machines

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Alzheimer's Disease Prediction by Spatio-Temporal Feature Fusion for MRI data 3

Alzheimer's Disease Prediction by Spatio-Temporal Feature Fu...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Harsha Nandhini, S. Aravinth, J. Amrita School of Engineering Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350300710

Alzheimer's disease (AD) is one of the leading causes of memory loss where patients eventually die. Currently, there is no cure for patients with AD, but patients with Mild Cognitive Impairment (MCI) is the prodromal stage of AD may get diagnosed and have the chance to escape from AD. However, many people with AD remain incurable. Diagnosis of MCI is very important to alleviate complications like brain cell loss and brain shrinking. This can be achieved by developing a spatio-temporal feature fusion aiding early diagnosis of AD. This study proposes a model for spatio-temporal feature fusion using two techniques a) Hierarchical Supervised Local Canonical Correlation Analysis (HSL-CCA) and b) 2D-Convolution Neural Network (2D-CNN). This work uses Magnetic Resonance Imaging (MRI) data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database scans for the 5 stages of AD subjects, and spatial, temporal, and deep features are extracted using Gray-Level Co-occurrence Matrix (GLCM), curvelet transform and 2D-CNN. Principal Component Analysis (PCA) is applied to remove the redundant information, and Short-time Fourier transform (STFT) is used to transform the 1D information into 2D images. Finally, Feature fusion techniques such as: simple concatenation, canonical correlation analysis (CCA), HSL-CCA and 2D-CNN are incorporated to fuse extracted features. The post-processing stage which includes Random Forest (RF) based classification of fused features to classify multiclass of Alzheimer's. The outcome of different feature fusion techniques was compared with several baseline models, the proposed HSL-CCA and 2D CNN fusion approach achieves 84% and 87% in AD detection. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Accelerometer signals denoising without clean training data based on Noise2Noise approach 3

Accelerometer signals denoising without clean training data ...

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3rd international conference on Electronic Information Engineering and data processing, EIEDP 2024

作者： Zheng, Fengling Li, Wei Cui, Xiaohui Rajaboevich, G.S. School of Artificial Intelligence and Computer Science Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Jiangsu China School of Cyber Science and Engineering Wuhan University Wuhan China Tashkent University of Information Technologies named after Muhammad al –Khwarizmi Yunusobod district 108 Amir Temur str Tashkent100202 Uzbekistan

ISBN: (纸本)9781510680531

The accelerometer signal is capable of providing crucial information regarding object motion, posture, and vibration. Therefore, it is of great significance to investigate noise suppression techniques for accelerometer signals. However, in complex industrial environments and under various equipment conditions, the noise generated exhibits a diverse nature, leading to suboptimal noise reduction outcomes when employing conventional denoising algorithms. This paper proposes a neural network model (AgentUNet) for denoising accelerometer signals. The network consists of a combination of UNet and Agent Attention, enhancing its ability to learn contextual information and achieve superior denoising performance. Additionally, instead of using clean signals as labeled data, we train the neural network using pairs of noisy data, thereby alleviating the challenge of collecting clean signals in industrial environments. Finally, we conduct experiments on experiment dataset. The experimental results indicate that AgentUNet achieves superior denoising performance compared to other baseline models. Furthermore, the denoising effectiveness of the neural network trained without using clean signals as labeled data is similar to that of the network trained using supervised learning methods. © 2024 SPIE.

关键词： Accelerometers

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Performance comparison among VGG16, InceptionV3, and resnet on galaxy morphology classification 3

Performance comparison among VGG16, InceptionV3, and resnet ...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Qian, Yumeng Software School School of Informatics Xiamen Universityc Xiamen361102 China

This article introduces the structures of three classical convolutional neural networks: VGG16, InceptionV3, and ResNet50, and compares their performance on galaxy morphology classification. The different structures of these networks give them some distinct features, leading to different results. The dataset used was Galaxy10 DECals created by the Galaxy Zoo project. The models were compared in the same way, using one fully-connected layer only without modifying its original architecture, evaluated by accuracy, precision, recall rate, and f1 score. The experimental results show that InceptionV3 gave the best classification results from all aspects. The inception module performs well on the galaxy morphology classification problem. © Published under licence by IOP Publishing Ltd.

关键词： Galaxies

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Selection of wavelet generating function in voltage interruption detection 3

Selection of wavelet generating function in voltage interrup...

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2023 3rd international conference on Electrical Engineering and Computer Technology, ICEECT 2023

作者： Gong, Jing Zhong, Siyu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

When using wavelet for voltage interruption detection, improper selection can lead to detection failure due to the differences in wavelet characteristics and the diversity of the generating function. Based on the analysis of wavelet characteristics, the modulus maximum principle is used to compare the detail coefficients after wavelet decomposition, and a method that should be followed for selecting the generating function is proposed. Here are four important points to consider: First, choose orthogonal wavelets that can decrease redundancy. Second, symmetry does not have a major effect. Third, higher-order vanishing moments and longer support lengths result in better singularity detection. Finally, the order of vanishing moments is more important than the support length. We establish a disturbance signal model with interruption occurring at the time of voltage zero crossing and select six types of wavelets: db1, db3, db6, db8, coif2, and coif3 for four-scale wavelet decomposition. The experimental results demonstrate the accuracy of the proposed method in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Wavelet decomposition

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State Tracking and Evaluation Technology for Big data Objects Based on a New Digital Watermarking Algorithm 3

State Tracking and Evaluation Technology for Big Data Object...

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3rd international conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Deng, Jianfeng Cai, Nishui China Telecom Research Institute Shanghai China

ISBN: (纸本)9798350340402

Currently, enterprises generally face the following problems in managing big data: Multiple versions or replicas of the same data object cross-system interfaces, delayed data destruction with invalid data not being cleared In time, difficulty in tracking data with no support of data traceability technology, weak data security and insufficient risk analysis capabilities. To address these problems, this article proposes a new digital watermarking algorithm that is suitable for embedding digital watermarks in big data objects. Combined with flow data analysis technology, it can track and monitor the processes of generating, storing, transmitting, processing, and publishing big data objects, mark the version status of data objects, draw the distribution map of enterprise data status we innovatively apply this recognition technology to calculate key indicators such as the health value of big data object references and the number of invalid data object references, and analyze data objects using predefined weaknesses and risk rules to discover data security issues and provide danger warnings for enterprises. © 2023 IEEE.

关键词： Big data

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Artificial Intelligence in Maintaining Interaction Between Humans and Computer 3

Artificial Intelligence in Maintaining Interaction Between H...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mudholkar, Rachana Cardenas-Palomino, Frans Gehlot, Anita Vekariya, Vipul Raju, Adusupalle Muni Rajput, Kapil Dypiemr Pune Akurdi India Universidad Tecnológica Del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of Piet India India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

No slumber! No time off for illness! Not a holiday! Along with nonstop labor throughout the clock, How can humans compete with the phenomenon of artificial intelligence? Of AI. With the future, humanity is engulfed in an exhilarating, unstoppable future. Humans must choose whether to embrace AI's advancement or to oppose it. The unavoidable transformation of our current reality. The actual game-changing technology is Quantum computing is available in business settings. People must learn how to live, work, and study and engage with AI, or they'll end up becoming second-class citizens. AI's lifeblood is big data. Because the processing capability of quantum computing will allow future computers to handle huge quantities of large data. Receptionists, drivers, and other human jobs are being replaced by AI. Future accountants, physicians, and cooks. Soon, quantum supremacy will be attained, and AI will quickly catch up to and surpass the "Singularity."Can humans understand the chance to advance AI or refuse it? The potential for human development is BCI technology, which uses artificial intelligence. Regulations and safety measures issues such as "Who is accountable for AI privacy and legislation"be put into place? In addition to the ethical usage of AI, the question of "can AI be respected as an independent entity""And how about the freedoms and ethics of AI?"has to be addressed. The mankind is in a state of will mankind and AI get along? That is the question on the inevitable road to AI supremacy. Adversaries"? © 2023 IEEE.

关键词： data Analytics

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The 3rd international Workshop on Mining and Learning in the Legal Domain 23

The 3rd International Workshop on Mining and Learning in the...

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32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Makrehchi, Masoud Zhang, Dell Petrova, Alina Armour, John Thomson Reuters Labs Toronto ON Canada Thomson Reuters Labs London England Univ Oxford Oxford England

ISBN: (纸本)9798400701245

The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate a variety of tasks in the legal domain, such as legal search and research, legal document review and summary, legal contract drafting, and legal outcome prediction. Compared with other application domains, the legal domain is characterized by the huge scale of natural language text data, the high complexity of specialist knowledge, and the critical importance of ethical considerations. The MLLD workshop aims to bring together researchers and practitioners to share the latest research findings and innovative approaches in employing data mining, machine learning, information retrieval, and knowledge management techniques to transform the legal sector. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by recent rapid advances in Large Language Models and Generative AI. We encourage submissions that intersect computer science and law, from both academia and industry, embodying the interdisciplinary spirit of CIKM.

关键词： legal data mining legal information retrieval legal natural language processing legal knowledge management large language models

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Application Research on the Integration of Big data Information Science and Technology with New Media Art

Application Research on the Integration of Big Data Informat...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Zhang Di Liaoning Communication University Shenyang China

The Web information collection and publication system is studied by means of web information collection technology, web denoising technology, based on the fusion of Big data Information Technology and art, text de-duplication technology and text automatic classification technology. A minimum connection number algorithm based on address and weight is proposed and implemented in the application layer. This method is applied to the development and implementation of the new media information release management system, so that it has a better operating efficiency.

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Set Intersection Computing Based on Privacy Protection of Cloud Platform in Teaching data State database 3rd

Set Intersection Computing Based on Privacy Protection of Cl...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Jiang, Chun Qin, Fengcai Shi, Xiaoyu Nanning University Guangxi Nanning530000 China

ISBN: (纸本)9783031631382

Privacy protection set intersection computing is a specific application problem in secure multi-party computing. It not only has important theoretical significance, but also has strong application background. In the era of big data in teaching and education, research on this issue is consistent with the increasingly strong demand for privacy protection while enjoying various services in teaching data. However, in the process of teaching and education, a large number of teaching data are often distributed in different entities. The traditional data collection and sharing method leave the data unreservedly to one party, which cannot protect the privacy of teachers and students. Centralized data processing is also vulnerable to external enemies, resulting in serious security threats such as data leakage. The introduction of laws and regulations related to data security and privacy, higher requirements are put forward for data storage, processing and sharing. Under the premise of privacy protection, how to use privacy protection technology to effectively protect teaching data has grown up to be a hot topic. This paper examines how to use privacy protection set intersection algorithm in teaching data state database. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data privacy

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