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A proposed deep learning model for multichannel ECG noise reduction

Discover Artificial Intelligence 2025年第1期5卷 1-16页

作者： Maurya, Jay Prakash Manoria, Manish Joshi, Sunil Department of Computer Science Engineering Samrat Ashok Technological Institute Madhya Pradesh Vidisha India Director & Professor Department of Computer Science Engineering Rungta College of Engineering & Technology Bhilai Chhattisgarh Durg India

Heart disease is a critical concern of healthcare for everyone in today’s era. An effective and noninvasive indication of heart disease is an electrocardiogram (ECG). Understanding regular ECG signal patterns and comparisons with irregular, patterns may help to identify the serious nature of heart diseases such as arrhythmia. Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. Errors in ECG interpretation might result from noise contamination of ECG signals. ECG pretreatment from noise is therefore needed for precise analysis. This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. Cascaded layered approach was framed in encoder and decoder sections of model where denoising and reconstruction process worked and compared on standard performance parameters with recent denoising deep autoencoders. This proposed work FCN-DAE with Jacobin regularization uses the noise stress test database (NSTDB) for the noise signal of ECG data sourced from the PhysioNet repository. The proposed model achieves 4.763 * 10–2 mv2 for signal space diversity (SSD), 0.288 mv for the median absolute deviation and 1.859 for the root mean square error (RMSE) in the conducted experiment. The experimental findings demonstrate that complex noise from the ECG signal may be removed up to 97.02%. © The Author(s) 2025.

关键词： Deep learning

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Enhancing User Experience and Mitigating BIAS in Electronic Device Recommendations on e-Commerce Platforms: Leveraging NLP and GPT Technologies 4

Enhancing User Experience and Mitigating BIAS in Electronic ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Gunda, Pavan Komati, Thirupathi Rao Gitam School of Technology GITAM Deemed to be University Department of Computer Science & Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798331540364

Electronic devicerecommendations on e-commerce sites enhance the user experience by assisting users in finding products that suit their requirements, interests, and preferences. By providing them with pertinent options, it helps users who are looking for specific gadgets save time and effort. Electronic devices commonly saw shorter product lifecycles due to the regular introduction of updated and new models. Users may feel under pressure to update to newer versions in order to access the newest features or upgrades, which could cause obsolescence problems. Previously NLP techniques are utilized for recommending electronic gadgets. Biased recommendations may result from NLP models inheriting biases found in the training set of data. As a result, there may be unfair or discriminatory consequences, such as more product recommendations for particular demographic groups or the reinforcement of preexistingpreconceptions. GPT may be used to construct chatbots or AIs that engage with customers using natural language. These assistance systems driven by AI are able to help customers make purchases, answer their questions, and make personalized product suggestions. Companies can improve the customer experience by responding quickly and giving useful information. One can use GPT to simplify customer service tasks like answering common questions and fixing problems right away. This can help companies help customers around the clock, speed up reaction times, and give human support workers less work to do. Support systems driven by GPT can also learn from exchanges with customers over time, making them better at what they do. High-quality material may be produced by GPT for use in blog entries, social media postings, marketingand copy, including product descriptions. One can use this material to get people interested in and buying from your e-commerce sites, show off product attributes and advantages, and attract new customers. We can also make GPT-generated content search engine

关键词： Sales

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Women Infertility: A Systematic Review of Effects and Causes

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SN computer science 2023年第2期4卷 103页

作者： Sahana Devi, K.J. Yendapalli, Vamsidhar Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka Bengaluru India

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Causes Detection of PCOS Effects ML techniques PCOS Women infertility

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Taxonomy for Classification of Cloud Service (Paas) Interoperability 1

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World Conference on Information Systems for Business Management, ISBM 2022

作者： Adhoni, Zameer Ahmed Dayanand Lal, N. Department of Computer Science and Engineering Gitam School of Technology Gitam University Bangalore India

ISBN: (数字)9789811974472

ISBN: (纸本)9789811974465

The decreasing cost of hardware has opened the doors to the development of various concepts like big data and huge storage spaces. The development of the cloud is one major development in this string of developments. The economic and other important contract features like the locking time gave rise to the usage of multi-clouds by various companies for the same or similar applications. The extended form of this scenario leads to operation on data on different clouds giving rise to interoperability. The Infrastructure as a service, Platform as a service, and Software as a service in the cloud force us to define taxonomy based on various views. Each view addresses its own customized problem. There is a need to generalize the taxonomy of interoperability. This paper presents a taxonomy for the classification of the cloud for Platform as a Service. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interoperability

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Deep Learning–Based Surrounding Descriptor for the Visually Challenged 1

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Vamsi, T. Mohana Naga Shankar, K. Ravi Karthik, G. Puruvi, M. Chanti, B. Department of Computer Science and Engineering Gitam University Andhra Pradesh Visakhapatnam India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

The proposed work presented in this chapter, "Deep learning–based surroundings descriptor for the visually challenged," aims to make a system software that helps visually impaired or blind individuals in perceiving and understanding their surroundings. Visually challenged persons are not able to enjoy the surroundings and nature as we normally do, and this project aims at improving their quality of life. The system will capture images to detect environmental features and then provide real-world auditory feedback to the user. The goal of this project is to create a reliable, user-friendly, and assistive technology and an affordable device software that improves the visually challenged person’s life and makes them independent, allowing them to move around more safely and confidently. This project uses several deep learning and NLP technologies such as CNN and LSTM. The image inputs are taken and the features are extracted using the VGG16 model, which is an advanced version of CNN that is great at object identification and localization. In addition, we used LSTM for training the model with the extracted features and the corresponding captions. Finally, when a user gives an image input to the trained model, it predicts the caption, and then it converts the text-to-speech for the user. The Surroundings Descriptor technology aims to solve the difficulties that visually impaired people face when navigating their surroundings and to improve their experience of life. The project includes technology design and development, system testing and evaluation, and improving the model based on user feedback. Finally, the Surroundings Descriptor has the potential to significantly improve visually impaired individuals’ mobility and independence, allowing them to participate better in public life and live more fulfilling lives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Assistive technology

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Eliminations of sulphur and fluoride from contaminated water by the applications of various types of biosorbents materials

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Water-Energy Nexus 2025年 8卷 73-92页

作者： Dey, Subhashish Veerendra, G.T.N. Phani Manoj, A.V. Anjaneya Babu, P.S.S. Jagadeesh, Gogineni Venkata Swarup Kumar, J.N.V.R. Civil Engineering Department Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Mechanical Engineering Department Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Computer Science Engineering Department Gitam University Andhra Pradesh Vishakapatnam India

Industrial and human waste releases sulphur and fluoride metals into the water sources, polluting them and endangering humans and the environment. There are various methods available for removals of the pollutants from the water. The membrane filtering and Chemical precipitation are more expensive, useless at low metal concentrations, and produce vast amounts of sludge and toxic by products that must be disposed of when treating big volumes of water. Alternative the wastewater treatment using the biosorption technology is environmentally friendly method. These methods are cheaper, more accessible, and reusable than traditional ones. Biomass can be treated physically and chemically treated before usage. The other parameters included the contact time, agitation speed, adsorbent dosage, pH, and temperature also affects the biosorption performances. After the removal of sulphur and fluorides from water, the bio-sorbent can be regenerated and reused to save money. Wastewater pollution removal using adsorption method is generally accepted. In the present scenario, the magnetic biosorbents and nanoparticles biosorbents are used for wastewater treatment have been developed. The magnetic biosorbents are attractive because to their various active sites, large specific surface area, easy separation, and lower cost. Chemical activations like acid, alkali, and salt improve biosorbents adsorption. In this observe, the various biosorbents have been developed for their better performance on defluoridation and desulphuridation. Due to different scientific barriers in the biosorption procedures those obstruct its commercialization;there has been a gradually rising consideration in this area of study. Of late more consideration is being paid in the formation of cost-effective adsorbents using various agricultural wastes, plant biomass, bacteria, algae and fungi. This review article highlights the applications of different biosorbents, sorption isotherm, and kinetics. In addition, the

关键词： Wastewater treatment

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Deep Learning Approaches for Detecting and Predicting Knee Osteoarthritis Severity 2

Deep Learning Approaches for Detecting and Predicting Knee O...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Karegoudra, Shilpa Veeresha, R.K. Yendapalli, Vamsidhar Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India GITAM University GITAM School of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350354461

Knee osteoarthritis stands as one of the most prevalent musculoskeletal ailments globally. Its initial signs include joint stiffness, discomfort, and limitations in functionality, marking it as a significant joint disorder. Older adults are more likely to have arthritis, and it normally gets worse with age. If there are no early, effective therapies used, it can lead to impairment in older people. Therapy must be started as soon as possible;otherwise, a full knee replacement may be necessary. The very subjective nature of the standard procedures creates a barrier to early illness progression detection. This study involves in-depth exploration and examination of the application of deep learning techniques for knee osteoarthritis detection and severity prediction. In this work EfficientNet-B6 was chosen for its superior performance in balancing accuracy, speed, and resource efficiency. Unlike models such as MobileNet or ResNet, X-ray images are used to train the model to predict the severity of knee osteoarthritis. The model classifies the data with an accuracy of 87%. A website for OA prediction is developed, the user needs to drag and drop an X-ray image in the dialog box given. The predicted output along with the severity will be shown within a few seconds. This website is very easy to use. © 2024 IEEE.

关键词： Knee prostheses

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A systematic review on non-contact methods to estimate the surface roughness 2

A systematic review on non-contact methods to estimate the s...

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2nd International Conference on Materials science and Mathematics for Advanced Technology, MSMAT 2022

作者： Karegoudra, Shilpa Yendapalli, Vamsidhar Department of Computer Science and Engineering Udupi574110 India Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka India

Information and Communication Technology (ICT) is used in recent industries to advance into a decision-making environment in manufacturing. The objectives of this are efficiency, flexibility, innovation, quality, and cost reduction. Controlling and monitoring the machining operation is an area of interest for most researchers. Drilling is the process of making a hole by using different cutting tools and continuously removing material to create the desired feature. It often rubs against the produced surface during chip evacuation. As a result, the produced roughness is distinct from that of other machining operations like milling or turning. Surface roughness may therefore differ from the one that is predicted theoretically. In this paper, we are studying the various non-contact methods to estimate the surface roughness of the machined surfaces. © 2023 Elsevier Ltd. All rights reserved.

关键词： Cost reduction

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Automated Detection and Classification of Cotton Leaf Diseases: A computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Ganguly, Swastick Jathin, S. Mohak, M. Sanjay, M. Somashekhar, B.M. Shobharani, N. Amrita School Of Computing Department Of Computer Science Mysuru Campus India Gitam School Of Technology Gitam University Dept Of Computer Science And Engineering Bengaluru Campus India

ISBN: (纸本)9798350371567

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability. © 2024 IEEE.

关键词： Cotton

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An approach of a quantum-inspired document ranking algorithm by using feature selection methodology

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4041-4053页

作者： Bhagawati, Rupam Subramanian, Thiruselvan Department of Computer Science and Engineering Presidency University Bangalore India Department of Computer Science and Engineering Gitam (Deemed to be University) Bangalore India

The main goal of an information retrieval system (IR) is ranking. Several methodologies were adopted with the integration of computing and advanced applied systems. However, traditional techniques have low stability, and machine learning techniques suffer from feature selection (FS) problems for exponentially growing data. This study speculates that combining quadratic unconstrained binary optimization (QUBO) with quantum annealing (QA) enhances the FS during the ranking process. QA is applied to reduce noisy and redundant data from the various state-of-the-art datasets in the field of IR. This study used the LETOR dataset. LETOR is divided into two versions: LETOR 3.0 (30,000 scientific documents) and LETOR 4.0 (more than 25 million documents). QA reveals the requisite quantum behavior in the findings using a processing unit known as the quantum processing unit (QPU). QPU addresses FS issues written as QUBO optimization problems successfully. The FS problem was QUBO formulated utilizing three quadratic models. A comparison was also made between the linear and QPU solvers employed in the ranking procedure. Quantum-based solutions outperform traditional methods in addressing ranking issues. Compared to the standard algorithms (LTR and LamdaMART), our suggested technique based on QA yields a normalized discounted cumulative gain of 0.39 and 0.80, respectively. QA can address real-world issues by offering practical solutions. QUBO and the QA process have improved feature selection for the ranking process. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Feature selection Information retrieval Quantum annealer Quantum computing Ranking

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