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A Development of Designing a Sensor N/W for Retrieving the Data from WSN via SBT 4

A Development of Designing a Sensor N/W for Retrieving the D...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Thangaraj, S. John Justin Kumar, Neelam Sanjeev Saranya, A. Bhat, C. Rohith Kareem, Doaa Saadi Shareef, Hayder Saveetha School of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai Vadapalani26 India Amity University Amity School of Engineering and Technology Mumbai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Search engines are essential for getting information from web sites in the internet age. They use a variety of ranking algorithms, depending on content-based information extraction techniques or statistical searching strategies, to organise the retrieved results. It is still difficult, nevertheless, to comprehend the underlying information on each web page without opening each one individually. This challenge motivates the concept and implementation of an object-attribute-value (O-A-V) information extraction system as a web model. To help with future searches, this model functions as a user dictionary by helping to filter search terms in queries. To evaluate the first model, natural language processing (NLP) and semantic web (SW) queries in the form of English phrases are employed. Similar to this, content-based picture information extraction and retrieval against user queries is used by image search engines like Google Images. Using picture descriptions, a domain ontology is constructed in order to close the semantic gap between user expectations and image retrieval outcomes. The second suggested model looks at subject-predicate-object (S-P-O) interactions by analysing natural language user queries using an NLP parser algorithm. This extraction builds upon the O-A-V web model, which is ontology-based. The article presents a SPARQL auto-query generation module to auto-generate SPARQL questions by utilising S-P-O extraction and taking into account the intricacy of creating SPARQL queries from the standpoint of a user. The auto-generated SPARQL query is used to obtain photos after these queries are placed on the ontologies. Systems for retrieving documents and images were developed and evaluated against industry standards in order to provide answers to these problems. The outcomes show improvements over baseline systems. © 2024 IEEE.

关键词： Ontology

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Analysis of Covid-19 Using Chest X-Ray Images: An AI Based Prospective 3

Analysis of Covid-19 Using Chest X-Ray Images: An AI Based P...

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Kohli, Himani Bansal, Abhay Amity University Amity School of Engineering and Technology Computer Science and Engineering Noida India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The unprecedented surge of Disease occurred in 2019, December named under covid-19, also known as corona-virus, was responsible for outbreaks happening in 2019-2020. It was first diagnosed in Wuhan, Hubei Province in the country China. Coronavirus is phenotypically and genotypically diverse in its behavior. Further, confirmed worldwide cases on April 6, 2020, consisting of 12,11,214 cases reported to the World Health Organization (WHO). The reverse-Transcript-Ase polymer-Ase chain reaction (RT-PCR) is the common solution utilized in the recognition of coronavirus, which has been found to be time-consuming and troublesome. On February 14, 2020, the laboratory-confirmed cases were 49,053 and deaths including 1,381 were documented. As of August 12, 2020, the virus has afflicted the lives of 20,674,903 people. It shows that deadly diseases are surging, plunging, and scattering illiberally in the current world. In accordance with the World Health Organization (WHO), over and above, 30 peculiar diseases like SARS coronavirus (SARS-COV), MERS coronavirus (MERS-COV), and H1N1 influenza, etc., have been diagnosed in previous twenty years to affect the health fitness situation of billions of people. The prime objective of this article is to make a review paper with artificial intelligence as a primary objective. It takes all the research to draw the comparative analyses of the distinct characteristics of data, mainly x-ray images of the chest, providing technologies and approaches to detect and prognosis coronavirus with learning algorithms. With the aim of finding the best-suited model, we compared the research on the basis of essential factors such as size of the batch , rate of learning, summed epochs till which it runs, and nature of optimizer. © 2022 IEEE.

关键词： Coronavirus

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Stroke Prediction using Machine Learning Methods 12

Stroke Prediction using Machine Learning Methods

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Gupta, Saumya Raheja, Supriya Amity University Amity School Of Engineering And Technology Computer Science Engineering Noida India

ISBN: (纸本)9781665437011

Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5.5 million. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. A stroke is generally a consequence of a poor style of living and hence, preventable in up to 80% of the cases. Therefore, the prediction of stroke becomes necessary and should be used to prevent permanent damage by stroke. The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, AdaBoost Classifier, XGBoost Classifier, and Random Forest Classifier. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that the AdaBoost, XGBoost and Random Forest Classifier made the least value of incorrect predictions and had the greatest accuracy scores 95%, 96% and 97% respectively. Hence, they were the best suited model for stroke prediction and can feasibly be used by physicians to predict stroke in real world. © 2022 IEEE.

关键词： Forecasting

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Time Series Analysis of Cryptocurrency: Factors and Its Prospective 5th

Time Series Analysis of Cryptocurrency: Factors and Its Pros...

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

作者： Sejwal, Sahil Aggarwal, Kartik Nayak, Soumya Ranjan Awotunde, Joseph Bamidele Amity School of Engineering and Technology Amity University Noida India Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (纸本)9789811920035

In the recent time as well as the last decade cryptocurrency has been one of the most discussed topic among the researchers all around the world. Different economies across the globe have seen a lot of growth of cryptocurrency over the time and bitcoin especially has seen a growth of 1100% that is why time series analysis of cryptocurrency is of immense significance. Time series analysis can be referred to as the process of taking into consideration a sequence of different points which are observed over a specific time interval. A large number of people start investing into cryptocurrencies without having any knowledge or analyzing the cryptocurrency market because of the hype it has these days and suffer huge losses so designing a model which can predict accurately as to how different cryptocurrencies would behave on basis of previous record can be very helpful and it can help some people in making profit rather than suffering loss. This paper presents a comparative overview of different algorithms like RNN, Linear Regression, GARCH, and ARIMA which can be used for time series analysis and concludes as to which algorithm is best suitable for time series analysis by considering different parameters like RMSE, MAE, etc., Besides this, it also analyzes the different factors which affect the prices of cryptocurrency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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Smart Cricket Ball: Solutions to Design and Deployment Challenges 6th

Smart Cricket Ball: Solutions to Design and Deployment Chall...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Balbudhe, Pravin Sharma, Rika Solanki, Sachin Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Raipur Chhattisgarh Raipur India Directorate of Technical Education Government Polytechnic Campus Maharashtra Sadar Nagpur India

ISBN: (纸本)9789819940707

Cricket is the most played and accepted game in the Asian region of the world. In any game decision-making affecting the result plays a major role, which has to be accurate in every aspect to conclude the fair game. This matter is a live cricket match or even in training sessions. To date, decision-making was dependent on visual observation by the expert umpire. These manual observations of results may be inaccurate at some point and based on the experience and expertise of the umpire. With the rapid advancement and technological involvement in gaming industries, electronic gadgets are helping the player to enhance the performance and accuracy of results. This paper deals with the challenges and the possible solutions involved in designing and building a smart cricket ball, which will be capable to calculate and share the ball’s activity log. This log will involve the direction, speed, type, trajectory, spin, deviation angle, and impact triggers. It is difficult to get all information from a single type of sensor or module, this work suggests creating the fusion of multiple sensors and software algorithms for accurate measurements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Decision making

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Towards Fortifying Digital Image Security: 'Blockchain Integration with CryptoMark Approaches' 7

Towards Fortifying Digital Image Security: 'Blockchain Integ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sharma, Vivek Kumar Arora, Renuka Yadav, Chandra Shekhar Mehrotra, Deepti Amity University Amity School of Engineering and Technology Greater Noida Campus Uttar Pradesh Gautam Buddha Nagar India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida Campus Uttar Pradesh Gautam Buddha Nagar India Noida Institute of Engineering and Technology School of Computer Applications Greater Noida India Dr Apj Abdul Kalam Technical University Uttar Pradesh Gautam Buddha Nagar India Amity University Amity School of Engineering and Technology Department of Information Technology Noida Campus Uttar Pradesh Gautam Buddha Nagar India

ISBN: (纸本)9798350350067

In the rapidly evolving digital landscape, Guaranteeing the safety and confidentiality of digital assets, particularly images, has become imperative. Blockchain has arisen as a leading solution for ensuring decentralized and unchangeable data storage, backed by strong cryptographic security measures. This paper presents a systematic review that investigates the impact of blockchain on enhancing security and privacy for encrypted and watermarked digital images. By synthesizing existing literature, our objective is to comprehensively analyze the efficacy of blockchain integration in safeguarding the integrity and confidentiality of digital image assets. Our review covers various aspects including encryption techniques, watermarking methods, and blockchain implementations. Additionally, we assess the research goals, motivations, and future directions in leveraging blockchain for image security and privacy enhancement. This review provides critical insights for scholars, professionals, and lawmakers into blockchain technology's ability to greatly improve digital image preservation across a wide range of applications, including the health sector and intellectual ownership administration. © 2024 IEEE.

关键词： Blockchain

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Towards Sustainable Power: A Quantitative Analysis of Environmental Factors and Energy Demand in Different Indian States 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Baranitharan, Balakrishnan Chandran, Karthik Murugesan, Rajalakshmi Roy, Satyabrata Dutta, Pushan Kumar Bhushan, Bharat Bhattacharya, Pronaya Department of Agricultural Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Mechatronics Jyothi Engineering College Kerala Thrissur679531 India Department of Biomedical Engineering Sethu Institute of Technology Tamil Nadu Madurai626115 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Kolkata700135 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida201310 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Kolkata700135 India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

As a result of non-linear load fluctuations, increased utilization of electronic devices, and the growing emphasis on sustainable and renewable energy sources, the power system reliability sector has garnered significant attention. The efficiency of electricity transmission from the source to residential, manufacturing, and commercial consumers is gauged by sustainable power quality. Energy plays a crucial role in accomplishing various tasks, and different types of sustainable energies are available and employed based on specific requirements. Renewable resources such as water, wind, and solar power are harnessed to generate sustainable energy, with considerations for factors such as rainfall and temperature, as they directly impact energy production. Rainfall contributes to the availability of water-based energy sources, while solar power generation relies on sunlight and temperature. Consequently, studying rainfall, temperature, and wind patterns becomes essential in assessing energy availability and requirements. Statistical measures, including the correlation coefficient, are employed to explore the relationships between these parameters. This paper aims to provide a comprehensive research focus on sustainable power within distribution networks, encompassing both elements of energy availability and requirements. Its intended audience comprises sustainable researchers, designers, and engineers working in this sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clean energy

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Advancements in Handwritten English Character Recognition: A Comprehensive Analysis Using Pattern Recognition and Deep Learning on Scanned Documents 2nd

Advancements in Handwritten English Character Recognition: A...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Moolchandani, Jhankar Kumar, Rahul Singh, Kulvinder Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior India Sri Ganganagar Department of CSE Tantia University Rajasthan Sri Ganganagar India

ISBN: (纸本)9789819768097

The Handwritten English Character Recognition (HECR) system is a vital component that bridges the gap between digital and pen-and-paper processes. Several technologies, including deep learning and pattern recognition, are used to change handwritten English letters in this study. Enhance the accuracy of the identification procedure. The UNIPEN dataset uses state-of-the-art technologies such as U-Net, Convolutional Neural Networks (CNNs), YOLO, and RCNN to identify handwritten characters found in scanned manuscripts. In addition to CNN feature extraction, YOLO and RCNN have many applications in data classification, data collecting, and structured organization. Adjusting the model's hyperparameters is all it takes to make it work better. The results demonstrate the model's efficacy;on testing data, it achieved 99.59% accuracy, and on training data, it achieved 98.44%. The paper highlights the requirement of using numerous deep learning algorithms to improve handwritten character identification, which is a dual aim. Integrating U-Net, CNN, YOLO, and RCNN methodically is driving future technique development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Role of Artificial Intelligence Enabled Internet of Things (IoT) in the Automobile Industry: Opportunities and Challenges for Society 5th

Role of Artificial Intelligence Enabled Internet of Things (...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Tyagi, Amit Kumar Mishra, Anand Kumar Kukreja, Swetta Department of Fashion Technology National Institute of Fashion Technology New Delhi India Computer Science and Engineering NIIT University Rajasthan Neemrana India Amity School of Engineering and Technology Amity University Maharashtra Maharashtra Mumbai India

ISBN: (纸本)9789819725496

The integration of Artificial Intelligence (AI) enabled Internet of Things (IoT) technologies in the automobile industry has witnessed rapid advancements, revolutionizing the way vehicles are designed, manufactured, operated, and maintained. This paper aims to discuss the current state of research on the role of AI and IoT in the automobile sector, identifying research gaps and addressing challenges that impact society. The automotive industry is undergoing a transformative shift, with AI and IoT playing important roles in enhancing vehicle performance, safety, and overall user experience. AI applications, such as machine learning algorithms, are employed for predictive maintenance, autonomous driving, and intelligent traffic management. Concurrently, IoT facilitates seamless connectivity among vehicles, infrastructure, and other smart devices, leading to the development of smart cities and intelligent transportation systems. However, several research gaps persist in understanding the full potential and implications of AI and IoT in the automobile industry. Firstly, privacy of communication systems and the integration of AI and IoT raises questions about the standardization of communication protocols and interoperability, necessitating further research on establishing industry-wide standards. Further several challenges also emerge in the form of cybersecurity threats, as connected vehicles become susceptible to malicious attacks. Hence, this work reviews the existing literature, highlighting gaps in current research and proposing a roadmap for future investigations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vehicular ad hoc networks

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IMNRFixer: A hybrid approach to alleviate class-imbalance problem for predicting the fixability of Non-Reproducible bugs

IMNRFixer: A hybrid approach to alleviate class-imbalance pr...

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作者： Goyal, Anjali Sardana, Neetu Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non-Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class-imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IMNRFixer, a novel and hybrid NRF prediction tool. IMNRFixer uses three different techniques to combat class-imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IMNRFixer models on four large and open-source projects of Bugzilla repository. Our results show that IMNRFixer outperforms conventional machine learning techniques. IMNRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively. © 2020 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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