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Data Mining Methods for Internet of Things: A Survey 6

Data Mining Methods for Internet of Things: A Survey

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Saxena, Aditi Taluja, Resham Sararswat, Manish Shalini, N. Krishna, Om Kumar, Pushpendra GLA University Dept of Electronics and Communication Engineering Mathura India Graphic Era Deemed to Be University Department of Mechanical Engineering Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Dept of Computer Science and Engineering Hyderabad India Lloyd Law College Greater Noida India Lloyd Institute of Management and Technology Greater Noida India

ISBN: (纸本)9798350304480

It is fascinating to think about all the many ways in which information, intellect, and knowledge may be used in human existence. Because of the lightning-fast pace at which technology is advancing, it is now essential to amass a large quantity of data in order to forecast and investigate potential developments in the field of artificial intelligence. In order to identify new information inside databases, it is necessary to apply strategies and procedures taken from a variety of information systems specializations. The practice of data mining entails not only the finding of new information but also the extraction of information that may be put to beneficial use. In recent years, considerable advances have been made in decision-making abilities in a variety of contexts, including expert systems, artificial agent networks, and machine intelligence. These businesses include companies in the fields of commerce, education, architecture, and building construction, among others. The approach that was taken in this piece was derived from an analysis of the data mining strategies and developments that have seen the greatest amount of adoption across a variety of business sectors over the course of the last five years. These patterns and modes of operation were investigated. Throughout the course of this enquiry, the processes and patterns described above were scrutinized. The use of text mining as a tool for database analysis, the enhancement of worker productivity in the industrial sector, and the application of data mining for instructional practices are only a few instances of what has been learnt in the area of education. One other illustration of this would be the use of text mining as a technique for image recognition. It was also found in the fields of trade and industry. © 2023 IEEE.

关键词： Data Mining Data Mining Processes Data Mining Techniques Database Database Management Systems

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Web Accessibility Challenges for Disabled and Generation of Alt Text for Images in Websites using Artificial Intelligence

Web Accessibility Challenges for Disabled and Generation of ...

引用

International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Tejal Tiwary Ranjendra Prasad Mahapatra Dept. of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad India

There are millions of visually impaired persons worldwide, and their numbers are continually increasing due to the ageing of the baby-boomer generation. The Internet may be extremely beneficial to blind individuals because it allows them to achieve things that they would otherwise be unable to complete without the assistance of others (such as reading mail or managing bank accounts). Education services and online shopping is also increasing day by day in the world of internet. So it is important that education websites, online shopping websites should be accessible to all regardless of their disabilities. This study describes challenges in accessibility of Indian Universities website especially for Visually Impaired. It also discusses its solutions by generating alternate text for images in websites through Artificial Intelligence techniques

关键词： Education Production Aging Internet Electronic commerce Artificial intelligence Task analysis

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Healthcare: A Machine Learning Approach for Disease Detection using Voice Signals

Healthcare: A Machine Learning Approach for Disease Detectio...

引用

Innovation in technology (APCIT), Asia Pacific Conference on

作者： Purohit Shrinivasacharya H. K. Vedamurthy Dept. of Information Science & Engg. Siddaganga Institute of Technology Tumakuru Karnakata India Dept. of Computer Science & Engg. Siddaganga Institute of Technology Tumakuru Karnakata India Visvesvaraya Technological University

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Nowadays, various treatments are available for different health issues. However, it’s impossible for people to know about all diseases and their medical treatments or medicines. Therefore, the problem arises here: there isn’t anyone or any place where we could visit and learn everything about different diseases. It would have been great if we could simply input the symptoms into a machine or device, and it would detect the disease by recognizing our symptoms and suggest the cure. This work allows users to identify a certain type of dysfunction in their bodies using some Machine Learning algorithms. It aims to develop an application that collects a person’s day-to-day voice samples. The voice samples collected from the normal person and the vocal patterns of individuals suffering from diseases are compared using different machine learning algorithms with features extracted from the Mell Frequency Cepstral Coefficient. The voice analytics interprets the audio signal, and analyzes the sentiments of the speaker. This work detect a disease whose datasets are available on the Internet to test the model. The designed model is tested and tabulated for the different machine learning algorithms and comparison with the other models are also tabulated. The designed system is giving an accuracy of 95.39%. The finally designed tool can be used in real-time application that can be used in remote areas and in case of emergencies, as access to doctors in these areas is not sufficient, which often leads to deaths in some situations. Further it can be enhance our application with updates so that it can detect and predict more of them in the near future.

关键词： Training Technological innovation Accuracy Machine learning algorithms Feature extraction Bayes methods Mel frequency cepstral coefficient

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Performance Comparison of Object Detection Neural Network Models Based on Accuracy and Latency 2

Performance Comparison of Object Detection Neural Network Mo...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gomathy, B. Sengottaiyan, N. Aarthi, K. Thirumoorthy, P. Tamizharasu, K. Kalyanasundaram, P. Dr.NGP Institute of Technology Dept of Computer Science and Business Systems Tamilnadu Coimbatore India Alliance College of Engineering and Design Alliance University Faculty of Science and Technology Karnataka Anekal562106 India Kongu Engineering College Department of ECE Erode Tamilnadu Perundurai638 060 India Nandha Engineering College Department of CSE Erode Perundurai638 052 India KSR Institute for Engineering and Technology Tiruchengode Department of CSE 637215 India

ISBN: (纸本)9798350327533

Aim: The purpose of this study is to compare the object detection performance of You Only Look Once V4 (YOLOv4) and Single Shot Multibox Detector (SSD) algorithms with respect to metrics like accuracy and latency. Materials and method: Twenty sample photos in all, from different classifications and labels, were gathered. These samples were divided into training dataset (60 %) and test dataset (40 %). To measure the performance, values for accuracy and latency were computed for YOLOv4 and SSD with G power 0.8. Result: The accuracy in prediction of the object in the image was higher in the YOLOv4 algorithm (97 %) compared to the SSD algorithm (84 %). After running a t-test on an independent sample of the two groups under consideration. It is observed that YOLOv4 reported greater preference than the SSD algorithm having p value 0.166 (p>0.05). It was proven that the YOLOv4 reported greater preference than SSD in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks Machine Learning Novel Custom Dataset Object Detection Single Shot Multibox Detector You Only Look Once V4

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Convolutional-Neural Network Bases Speaker Emotion Detection Based on Human-computer Interaction

Convolutional-Neural Network Bases Speaker Emotion Detection...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Nithya B Saranya R Mary Joseph R. Josphineleela Arunkumar Thirunagalingam Arun Kumar Kodali Dept of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur Department of IT St. Joseph's Institute of Technology OMR Chennai Department of Computer Science and Engineering Panimalar Engineering College Data and Operations Mckesson Corporation Mphasis Corporation Data Architect

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic connections is made possible by emotional knowledge. Conventional techniques for detecting emotions depend on manually created characteristics, which are frequently laborious and less resilient to speech variability. By using CNNs' automatic feature extraction capabilities, the suggested system analyses speech signal spectrograms and extracts crucial emotional indicators. The method obtains notable gains in emotion classification accuracy by retraining the system on a varied collection of emotional speech samples. Important emotional states like happiness, anger, sadness, and neutrality are all successfully distinguished using the CNN model. This study demonstrates how deep learning can help develop emotion-sensitive HCI systems and promote more organic and contextually aware human-machine interactions. Applications in customer service, healthcare, and virtual assistants can benefit from the technique's adaptability and versatility.

关键词： Human computer interaction Emotion recognition Virtual assistants Medical services Feature extraction User experience Convolutional neural networks Robots System analysis and design Spectrogram

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An IoT enabled Malware Identification Mechanism over Digital Documents using Predictive Learning Scheme 2

An IoT enabled Malware Identification Mechanism over Digital...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Radha, K. Sivagamidevi, G. Juliet, N. Niranjana, S. Nimmalaharathi Dhanalakshmi, G. Dr.Mahalingam College of Engineering and Technology Department of Computer Science and Technology Pollachi India Dhanalakshmi College of Engineering Dept of Ai & Ds Chennai India S.A. Engineering College Department of Master of Computer Applications Chennai India Jeppiaar Institute of Technology Department of ECE Chennai India SreeVidyanikethan Engineering College School of Engineering Department of Eie Andhra Pradesh Tirupati India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350315905

The ability to identify malicious software is crucial to ensuring the safety of computer networks. Nevertheless, signature-based technologies now in use are inadequate for identifying zero-day assaults and polymorphic infections. That's why we need detection methods that use machine learning. The proliferation and sophistication of malware threats have elevated the problem of automated malware identification to the forefront of network security discussions. Manually analyzing all malware in a programme using conventional malware detection techniques is laborious and resource-intensive. The proliferation of the internet has led to a meteoric rise in the number of people using IoT devices. Malware assaults are growing more common as the storage capacity of IoT devices grows;as a result, detecting malware in IoT devices has become a pressing concern. For identifying malware in IoT devices, we present an ensemble categorization approach based on deep learning. For the identification of malware, we employ ANN and LSTM outputs. Our suggested technique achieves an average accuracy of 99.49% on standard datasets, which is higher than the accuracy of state-of-the-art methods. © 2023 IEEE.

关键词： Internet of things

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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

引用

International Conference for Convergence of technology (I2CT)

作者： Areesha Sami Anum Naseer Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Aqsa Khalid Rimsha Awan Farhan Ashraf Zohaib Ahmed Khan Arslan Javaid Department of Computer Engineering Information Technology University Lahore Punjab Pakistan Dept. of Computer Science Bahria University Department of Computer Science Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally.

关键词： Support vector machines Machine learning algorithms Medical services Predictive models Prediction algorithms Vectors Diabetes

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Resource Allocation for OTFS-Based ISAC Systems

Resource Allocation for OTFS-Based ISAC Systems

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Meiying Zhang Hengtao He Shenghui Song Jun Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Integrated sensing and communications (ISAC) has been considered as a key technology for future wireless networks. Orthogonal time frequency space (OTFS) modulation is considered as an effective technology to achieve ISAC in high-mobility scenarios due to its ability to combat high delay and Doppler shifts. However, although the performance analysis of OTFS-based ISAC system has been investigated, resource allocation and joint optimization for communication and sensing are still in its fancy. In this paper, we consider resource allocation in OTFS-based ISAC systems. We first introduce the system model and formulate the resource allocation problems. Then, several related works are discussed in detail. Next, an OTFS-based multiple-user multiple-input multiple-output (MIMO) ISAC system model is investigated, where the base station is considered as an ISAC transmitter and sends the message to different vehicles while simultaneously estimating their relative range and velocity via output feedback, respectively. We then formulate the sensing and communication trade-off optimization problem considering the delay Doppler resource block (DDRB) allocation and provide some sample results. Finally, we show several future directions for resource allocation in OTFS-based ISAC systems.

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Advanced Road Lane Line Detection

Advanced Road Lane Line Detection

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ms. Visnu Dharsini Karthik K M. Gopichandd Jorige Venkatesh Sabarish S Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350333855

Lane detection systems are essential for driverless cars and sophisticated safety measures (ADASs). These detectors warn drivers of potential hazards in order to avert collisions. Type recognition and ego lanes give context. Lighting and driving conditions may hinder lane perception. We assess ego lane borders via CNN-based regression and YOLOv6. After spatial modification, lanes or road borders are validated by a network. Lane Net plus is advised for use as lane markers and side segmentation. It constructs a multitasking network, divides it by semiology, generates encapsulated space networks, and clusters lane lines to segment instances. This warrants investigation. Combining the spatial and semantic network loss functions for segmentation provides the total network loss function. In the training dataset, 12,000 pixel-tagged dashcam images and 90,000 iterations are included. 12-hour model trained on the server. dual GPUs Models are 95% correct.

关键词： Training Image segmentation Roads Semantics Transforms Real-time systems Convolutional neural networks

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Efficient Diagnosis of Coronary Artery Disease: A Hybrid Convolutional-XGBoost Approach

Efficient Diagnosis of Coronary Artery Disease: A Hybrid Con...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Arkashree P. Mishra Suvasini Panigrahi Dept. of Computer Science & Engineering Veer Surendra Sai University of Technology Burla India

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine Learning (ML) as well as Deep Learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods.

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