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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： S. Durai T. Sujithra Athmakuri Sai Charan Sai Kiran Pulyala Battula Vishnuwardhan Satyam Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Computer Science and Engineering Manipal Institute of Technology Manipal

Defect Detection became one of the hardest problems in this world nowadays. As of now humans detected the defects with the help of their naked eye. This process is time consuming and this can be done by only experts and these decisions may be differ from expert to expert. To avoid this problem we are training the collected data set. This will consume very less time and provides the best solution. The detection of defects is essential in quality control in manufacturing to get high quality products in less time. Humans can identify large defects but in case of micro defects humans can't found those so, this implementation will helpful in finding large as well as micro and milli defects as well. In this project we are trying to detect the defects such as dents and ripples present on the surface of the car which are macro in size at the time of manufacturing the cars and in the case of accidents happened. We'll use a high-quality camera to simultaneously capture photographs of the vehicle's five sides. We will use Convolution Neural Network [CNN] for training (which highlights the location of the defects). Once trained, that model we can use it for different end user applications.

关键词： Training Deep learning Convolution Neural networks User interfaces Prediction algorithms Real-time systems

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0

Machine Learning Classifiers for Predicting the Risk of Card...

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Communication, Control and Intelligent Systems (CCIS)

作者： Palak Kumari Ashish Kumar Kakali Chatterjee Master of Computer Applications Patna Women’s College Patna India Dept. of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body’s, brain’s, or heart’s blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives.

关键词： Support vector machines Accuracy Scalability Machine learning Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Blood flow

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A defected ground structure based ultra-compact wider bandwidth terahertz multiple-input multiple-output antenna for emerging communication systems

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Heliyon 2024年第17期10卷 e36842页

作者： Kumar, Praveen Sivakumar, V. Rao, Vidya George, Criss Tom Awadhiya, Bhaskar Huchegowda, Yogeshwary Bommenahalli Nanjappa, Yashwanth Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Electronics and Communication Engineering Shri Madhwa Vadiraja Institute of Technology and Management Visvesvaraya Technological University Bantakal 574115 India

This work presents a quad-port Multiple Input Multiple Output (MIMO) wideband antenna that operates in the terahertz (THz) frequency range is designed and analyzed. To improve MIMO performance, a defective ground structure (DGS) is used. A rectangular metallic patch was altered by adding parasitic elements to the radiator with the lowered ground plane on a polyamide substrate in order to achieve the wideband THz operating frequency. The THz antenna was turned into a MIMO antenna by replicating horizontally and vertically with a spacing of 0.05 λ and 0.002 λ, respectively (λ calculated at 1.7 THz). The designed THz MIMO antenna, comprising 40 μm × 46 μm × 2 μm, operates across the 1.7–10.4 THz frequency region. The THz MIMO antenna provides isolation of more than 20 dB in the frequency range of 2.8–10.4 THz and more than 10 dB for the frequency of 1.7–2.7 THz. Isolation augmentation is accomplished by establishing different local current channels using the antenna's DGS. The MIMO diversity properties of the proposed THz MIMO antenna are analyzed and found to be ECC<0.004, DG ~ 10 dB, TARC < −10 dB, MEG < −3 dB, and CCL<0.23 bps/Hz over the antenna's operating frequency. © 2024 The Author(s)

关键词： DGS MIMO THz antenna Wideband

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Securing Internet of Medical Things: Exploring Vulnerabilities and Attack Vectors

Securing Internet of Medical Things: Exploring Vulnerabiliti...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Umesh Umesh Sunil Kumar Kamlesh Dutta Aparna Kumari Dept. of Computer Science & Engg Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of AI & Data Science Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engg National Institute of Technology Hamirpur H.P India Dept. of Computer Science & Engg Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The emergence of the Internet of Medical Things (IoMT) is driven by the integration of Internet of Things (IoT) technologies in the healthcare sector. IoMT represents the integration of healthcare devices, systems, and applications into a connected network that leverages the power of IoT. IoMT facilitates the seamless collection, sharing, and analysis of patient data to improve diagnosis, treatment, and overall healthcare quality. However, significant security concerns have arisen with the adoption of IoMT technologies in the healthcare sector. These vulnerabilities pose significant threats to the privacy of patient data as well as the integrity of the healthcare delivery system. In this paper, we first conduct a study on various vulnerabilities associated with IoMT environments. This article then presents various security attack vectors that compromise the integrity, security, and privacy of IoMT-driven healthcare systems. Finally, the paper concludes by highlighting future research directions in the domain of security in IoMT-powered healthcare systems.

关键词： Privacy data privacy Internet of Medical Things Grid computing Vectors Security Reliability

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Web-enabled Vision-based Person Counting System for Smart Space Management 18

Web-enabled Vision-based Person Counting System for Smart Sp...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chohan, Jasgurpreet Singh Naser, Zamen Latef Sharif, Hayder Fezaa, Laith H. A. Dixit, Krishna Kant Bisht, Yashwant Singh University Center for Research and Development Chandigarh University Punjab Mohali India National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Technical College Iraq Gla University Dept. of Computer Engineering and Applications Mathura India Uttaranchal Institute of Technology Department of Mechanical Engineering Dehradun India

ISBN: (纸本)9789380544519

Efficient space management has emerged as a crucial issue in the era of smart spaces and IoT-enabled surroundings. In this study, an innovative method is proposed to solve the difficulties of real-time occupancy monitoring and resource optimization in various public and private venues. The system makes use of an ESP32-CAM module with a built-in camera to capture real-time images of the monitored region. The recorded images are analyzed using computer vision techniques and algorithms to track and detect people entering and leaving the area. The basis of the system is the person counting algorithm, which delivers precise and immediate occupancy statistics. The system allows users to log in from any location and monitor space occupancy in real time. The study was conducted based on the performance and accuracy of the system under various circumstances, indicating its appropriateness for a range of applications, from offices and retail outlets to public spaces and smart homes. The importance of this research is found in its contribution to the subject of smart space management, which is still in its infancy but where data-driven choices are essential for maximizing resource usage, guaranteeing social distance compliance, and improving user experiences. This web-enabled vision-based person counting system can help advance smart space management in the context of the Internet of Things (IoT) and smart cities as a creative and useful solution. In the end, the study provides a thorough description outlining its goals, approach, salient characteristics, and prospective effects on smart space management. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blynk cloud app computer vision ESP32-CAM module smart space management Web-Enabled

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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

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Journal of Stroke Medicine 2022年第2期5卷 126 - 132页

作者： Girish Menon Hareesha KS Karthik Gajula Sampath Kumar Ajay Hegde Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics & Communications Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

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ChatGPT-guided Semantics for Zero-shot Learning

ChatGPT-guided Semantics for Zero-shot Learning

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2023 International Conference on Digital Image Computing: Techniques and applications, DICTA 2023

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin North South University Dept. of Electrical and Computer Engineering Bangladesh University of Adelaide Australian Institute for Machine Learning Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia University of Technology Sydney School of Computer Science and Dsi Australia

ISBN: (纸本)9798350382204

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method. © 2023 IEEE.

关键词： Zero-shot learning

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches

An Efficient Model for Early Detection of Maize Leaf Disease...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Shanta Kallur Suman Yaligar Puneeth N. Thotad Dept. of Computer Science and Engineering K.L.E.Institute of Technology Hubballi India Affiliated to Visvesvaraya Technological University Belagavi Dept. of Master of Computer Applications K.L.E.Institute of Technology Hubballi India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition.

关键词： Deep learning Training Productivity Adaptation models Technological innovation Accuracy Image recognition Transfer learning Convolutional neural networks Diseases

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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