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The Role of AI in Biochips for Early Disease detection 3

The Role of AI in Biochips for Early Disease Detection

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Sukumaran, C. Indhumathi, K. Balamurugan, P. Ambilwade, R.P. Sunthari, P. Mohana Natrayan, L. Government Law College Tamilnadu Thiruchirappalli620023 India Saveetha Engineering College India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Chennai India National Defence Academy Pune Department of Computer Science India K S R Institute for Engineering and Technology India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350342338

Improvements in illness diagnosis made possible by combining AI with biochip technology. It highlights the use of AI to improve the sensitivity and efficiency of biochips, allowing for faster and more precise illness diagnosis. Current healthcare uses of biochips are discussed, as well as artificial intelligence methods that may be used to the processing of biochip data. Through a variety of case studies, it shows how AI may be used for real-world tasks including early detection of cancer and other diseases through its capacity to analyze complicated data and perform predictive analysis. Data privacy, ethical considerations, and the need for extensive datasets to train AI are all discussed in the study as well as other obstacles to integrating AI with biochips. Furthermore, the legal implications of implementing new healthcare technology are covered. Future research options are suggested, with an emphasis on the importance of multidisciplinary cooperation and the possible effect of AI in biochip design. © 2023 IEEE.

关键词： Artificial intelligence (AI) Biochips Data Privacy Healthcare technology Predictive Analysis

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Self-Aware AI-Enabled Medical system 20

Self-Aware AI-Enabled Medical System

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20th international Bhurban conference on Applied Sciences and technology (IBCAST)

作者： Vadsaria, Aly Shah Raza, Ali Hammad, Muhammad Shahid, Muhammad Farrukh FAST NUCES Comp Sci Dept Karachi Pakistan

ISBN: (纸本)9798350308266;9798350308259

The healthcare plays a critical role in lives of people, enhancing the quality of live and enabling growth of world economy. The healthcare providers rely on health monitoring devices to provide timely and accurate data that could be used for early diagnosis and intervention. However external interference and internal faults in these wireless medical devices can cause anomalies in the readings they provide. Moreover, these devices aren't self-aware due to which they provide inaccurate readings being without detecting them. Additionally, users remain unaware of the inaccurate readings and do not realize the potential consequences of these readings such as they might delay early diagnosis of emergency medical condition. In order to address these problems, we designed a framework named as Self-Aware AI-Enabled Medical system that employs machine learning and deep learning algorithms to make these devices self-aware. The framework enables these devices to detect interference and abnormalities in the data and it also incorporates an Early Warning Score (EWS) system that infers if a user's vitals suggest any emergency medical condition and alerts them for immediate medical assistance. In our framework, we have three major features, 1st feature is the initial model for prediction for which we have used LSTM. The 2nd the EWS model for which we have used Random Forest Classifier, and the 3rd feature is abnormality detection model for which we have used Support Vector Classifier (SVC). The framework was able to achieve an impressive overall accuracy rate of 94%. By making health monitoring devices self-aware, our system enables users to receive timely notifications of potential medical conditions before severe symptoms arise, thus improving the effectiveness of electronic health monitoring devices in preventing and managing medical conditions. This advancement contributes to the well-being of individuals and society by enhancing healthcare outcomes and promoting proactive hea

关键词： Self Awareness Artificial intelligence Health Long Short-Term Memory (LSTM) Kalman Filter Early Warning Score (EWS) Support Vector Classifier (SVC)

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A Brief Survey on Feature Extraction Models for Brain Tumor detection 3

A Brief Survey on Feature Extraction Models for Brain Tumor ...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Janapati, Malathi Akhtar, Shaheda Acharya Nagarjuna University Dept. of Computer Science and Engineering Guntur India Guntur and Research Supervisor Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665462167

Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques. © 2023 IEEE.

关键词： Feature extraction

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Automating Big Data Analysis Using Artificial intelligence 3

Automating Big Data Analysis Using Artificial Intelligence

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bhimanpallewar, Ratnmala N. Jangid, Satish Kumar Yadav, Rakesh Kumar Arunkumar, D.T. Dehankar, Pooja Geetha, B. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India School of Engineering Ajeenkya D. Y. Patil University Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai128 India

ISBN: (纸本)9798350319125

Huge information evaluation, the use of synthetic intelligence, is a manner of using AI structures to automate the system of extracting meaningful insights from massive sets of statistics. A combination of AI-driven algorithms and hardware may be used to quickly perceive styles in unlabeled information that could otherwise take vast quantities of time to extract. This paper presents a method and framework for Automating Big Data Analysis using Artificial intelligence (AI). With the increasing volume and complexity of big data, traditional manual analysis methods are becoming inefficient and often result in inaccurate or incomplete insights. To address this challenge, we propose a systematic approach that leverages AI techniques such as machine learning and natural language processing to automate the process of data analysis. Our framework includes measures for data preprocessing, feature selection, model building, and result interpretation, and adapts to different types and sources of big data. We demonstrate the effectiveness of our approach through experiments on real-world datasets and compare the results with manual analysis methods. The proposed automated approach not only improves the speed and accuracy of big data analysis, but also reduces the human effort and expertise required. This has the potential to greatly enhance decision-making processes in various industries and domains that deal with large amounts of data. These talents can be used to permit higher choice-making and create extra efficient, automatic approaches to conducting huge-scale data analysis. © 2023 IEEE.

关键词： Permit Processing Quantities Similarly Visualization

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Morphology and Transformer-Based YOLOv5 for Workpiece Surface Crack detection 3

Morphology and Transformer-Based YOLOv5 for Workpiece Surfac...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Ren, Xinghua Hu, Shaolin Hou, Yandong Ke, Ye School of Artificial Intelligence Henan University Zhengzhou China School of Automation Guangdong University of Petrochemical Technology Maoming China

ISBN: (纸本)9781643684444

The surface crack of the workpiece is similar to the background and the background is complex. In the detection process, problems such as missed detection, false detection, and difficulty in detection are prone to occur. To solve the above problems, this research proposes a workpiece surface crack detection technique based on morphology and improved YOLOv5. To improve the ability of the model to extract global information, the erosion method in morphology is used to improve the crack feature of the data set. Next, the global and local information in the YOLOv5 backbone network is fused by MobileViTv3. And finally, SIoU is used as the loss function of the bounding box regression to improve the accuracy of the bounding box localization. After comparative experiment verification, the designed model achieves 73.1% and 74.6%accuracy on the original and corrosion datasets, respectively. Accuracy of the model improves by 13.6% and 15.1%, respectively. The results of the example experiment show that the method proposed in this paper has a good detection effect, realizes the accurate detection of cracks on the device surface. And it provides a novel idea for using deep learning to detect cracks in real scenes. © 2023 The Authors.

关键词： Morphology

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Design and Implementation of a LoRa-Based Water Quality Monitoring system 3

Design and Implementation of a LoRa-Based Water Quality Moni...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Malik, Praveen Kumar Malik, Pooja Kumar, Gotte Ranjith Sneha Abraham, Robin Singh, Rajesh School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India School of Computer Science & Artificial Intelligence Sr University Warangal India Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad India Uttaranchal University Uit Division of Research and Innvovation Dehradun India

ISBN: (纸本)9798350330724

Monitoring the physical, chemical, biological, and radiological properties of water is the process of figuring out whether it is suitable for a certain usage. Monitoring water quality strives to guarantee that water is fit for diverse applications, including drinking, recreation, and irrigation, as well as that it complies with environmental requirements. The conductivity, oxygen, TDS, turbidity, temperature, BOD, COD, pH and the presence of other pollutants like heavy metals, pesticides, and pathogens are among the parameters that are commonly examined during water quality monitoring. Monitoring water quality is crucial since it can have a negative impact on ecosystems, economy, and human health. Authorities can detect potential sources of contamination through monitoring and take the necessary action to safeguard the environment and the general people. Monitoring the quality of our water supply is essential to ensure its security. In this project, we suggest developing and deploying a LoRa-based system for monitoring water quality, which would allow for real-time monitoring of variables including pH, TDS, and temperature. The LoRa-based Water Monitoring Mote and the LoRa Gateway Mote are the two nodes that make up the system. © 2023 IEEE.

关键词： Water quality

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Using machine learning principles, the classification method for face spoof detection in artificial neural networks 3

Using machine learning principles, the classification method...

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

作者： Saravanan, P. Pandey, Aparna Joshi, Kapil Rondon, Ruchika Narasimharao, Jonnadula Imran, Afsha Akkalkot Srm Institute of Science and Technology Computing Technologies Tamilnadu Chennai India Dr. C. V. Raman University Computer Science and Engineering India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Petroleum Engineering Uttarakhand Dehradun248002 India Cmr Technical Campus Department of Computer Science & Engineering Telangana Hyderabad501401 India Savitribai Phule Pune University Department of Computer Engineering Karnataka Pune India

ISBN: (纸本)9798350399264

The usage of biometric technologies for person authentication and verification has grown significantly, and these systems are now widely employed. This biometrics is essential to personal, governmental, and international security. This paper's major strategy is to explain the value of biometric systems. These days, various biometric system types are in use. These biometric technologies include facial recognition, voice recognition, palm vein biometrics, and iris recognition. Spoofing is the fundamental danger to all of these biometric systems. When compared to other biometric systems, the facial recognition biometric system is more commonly employed. Face-spoofing techniques include mask attacks, photographic and video attacks, and We examine various techniques for spotting Face spoofing. Face has recently received more attention in a wide range of fields because to its security and convenience. Biometric system-based face detection frequently employed for authentication of applications where human faces are the recognition of daily life as well as retaining of sensitive information. However, face spoofing assaults continue to pose a threat to face recognition systems. Although several face spoofing detection methods have been proposed by researchers and have demonstrated excellent performance, our goal is to create a system for face spoofing using machine learning algorithms. © 2023 IEEE.

关键词： Face recognition

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A Novel Framework for detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd international conference on Computing and Information technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Research on detection and Recognition system of Oil and Gas Pipeline Weld Seam Based on Machine Vision technology 3

Research on Detection and Recognition System of Oil and Gas ...

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3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Zhang, Liyuan Zhou, Desheng Liu, Liming Liu, Xiaobin Yuan, Tao Zhao, Ming Ren, Kai Xi’an Shiyou University Shaanxi Xi'an China The 11th Oil Production Plant of PetroChina Changqing Oilfield Company Gansu Qingyang China

ISBN: (纸本)9781510667563

As the construction of oil and gas pipelines continues to advance, the safety of pipelines has garnered increasing attention. Pipeline welds represent a crucial factor in ensuring the safe transportation of pipelines over the long term. Therefore, to guarantee the quality and safety of pipeline welding, it is necessary to inspect the welds of oil and gas pipelines. Currently, manual detection is the most common method for inspection. However, as the number of pipeline detection points continues to increase, manual detection has revealed issues such as low detection efficiency and the susceptibility of detection results to subjective judgments. To address these shortcomings, this study introduces machine vision technology to the detection and recognition of oil and gas pipeline welds, which holds significant guidance implications for the detection and recognition of oil and gas pipeline welds. © 2023 SPIE.

关键词： Welds

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Third international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

Third International Conference on Artificial Intelligence, V...

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3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

ISBN: (纸本)9781510671485

The proceedings contain 92 papers. The topics discussed include: optimized scheduling of unmanned baggage transport vehicles in airports considering battery degradation;optimized scheduling of unmanned baggage transport vehicles in airports considering battery degradation;an efficient and privacy-preserving federal learning scheme;research on recommendation algorithm of private charging pile based on collaborative filtering;design of dormitory management system based on fingerprint recognition;adaptive genetic simulated annealing algorithm for project demonstration scheduling problem;research on the application of digital twin technology in smart highway real-time simulation;research on key technologies of smart security check in urban rail transit;an improved person re-identification method;and artificial intelligence-based intelligent elderly care system.

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