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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Katariya, Jitendra Kumar Yadav, Dhananjay Gayathri, C. Kolekar, Vikas K. Daivagna, Umesh M. Pushparajesh, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research. © 2023 IEEE.

关键词： Network architecture

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IoT In Increasing Human to Machine Interactions in the Automobile Sector 1

IoT In Increasing Human to Machine Interactions in the Autom...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Mindarta, Erwin Komara Kavitha, D. Poongundran, Mahizharuvi Priyadarshini, R. Sethu Ram, M. Moldabayeva, Magira Universitas Negeri Automotive Engineering Education Program Malang Indonesia School of Computer Science and Engineering Vellore Institute of Technology India Sri Meenakshi Government Arts College for Women Pg & Research Department of Computer Science Tamil nadu India School of Computer Science and Engineering College Vit Sr.Grade Ii Department Chennai India School of Engineering & Technology Department of Computer Science & Engineering Tirupati India International Education Corporation Department of Linguistics Almaty Kazakhstan

ISBN: (纸本)9781665497107

IoT technologies can facilitate machine-to-machine as well as human-to-machine interactions. Use of an automotive human-machine interface can help in exchanging information between vehicles and passengers or drivers. Organizations in automobile sector are using these technologies for providing their customers with seamless customer-experience, which has helped not only in enhancing profitability of the organization;however also in enhancing customer base. An advanced speech-recognition technology is used in the cars for automation of different functions that ensures extraordinary experience for a driver. This requires adoption of an advanced industrial user-interface that connects the human with the machine actively. Present study has analyzed the ways IoT has played a significant role in making human-to-machine interaction possible in automobile industry. In literature-review, different concepts and theories support the impact of IoT in improving human-machine interactions in automobile industry. This chapter has further analyzed the challenges related to the use of this technology. In methodology section of current study, the ways data has been collected in the study has been analyzed. In findings-section, objectives and variables of the study have been considered for designing relevant themes for providing proper insight into the effectiveness of IoT technologies in improving human -machine interactions in automobile industry. The findings section reveals that IoT usage in this industry can enhance customer-experience by improving human-machine interactions especially when using IoT-enabled cars. It has provided proper suggestions regarding the best ways of utilizing IoT in this sector while ensuring reduction of possible risks. © 2022 IEEE.

关键词： Automotive industry

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy 11

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Dewanthi, Desyra Sukma Kristopo, Hari Qomariyah, Nunung Nurul Axel, Muhammad Business Management & Marketing Program Binus Business School International Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Technology Department Jakarta11480 Indonesia School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18-40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business. © 2024 IEEE.

关键词： Differential privacy

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Alzheimer’s Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer’s Disease Prediction from MRI image using Convolu...

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Intelligent Computing and Sustainable Innovations in technology (IC-SIT), International Conference on

作者： Sanghamitra Panda Rekha Sahu Satya Ranjan Dash Department of Computer Science and Engineering Centurion University of Technology and management Bhubaneswar Odisha India Department of Computer Science and Engineering Silicon University Bhubaneswar Odisha India School of Computer Application KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Alzheimer’s disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer’s disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer’s disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model.

关键词： Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling computer architecture Predictive models Convolutional neural networks Alzheimer's disease Testing

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Efficiency Beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency Beyond Limits: LSTM and k-NN Synergy for Forecast...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Amit K. Mogal Vaibhav P. Sonaje CH N Santhosh Kumar V. Chithra Devi Prasanthi Gottumukkala Satheesh Kumar D Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik Maharashtra India Department of Computer Science & Applications School of Computer Science and Engineering Sandip University Nashik Maharashtra India Department of CSE Anurag Engineering College Kodada Telangana India PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R 2 ) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings.

关键词： Data centers Machine learning Predictive models Nearest neighbor methods Data models Resource management Energy management

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A Review on Breast Cancer Classification using Neural Networks

A Review on Breast Cancer Classification using Neural Networ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Aashita Rajput Muskan Sachin Yadav Megha Chhabra Arun Prakash Agarwal Department of Computer Science & Application School of Engineering &Technology Sharda University Greater Noida India

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them.

关键词： Analytical models Computational modeling Biological system modeling Neural networks Breast biopsy Breast cancer Research initiatives

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Blockchain based Security Solution for Internet of Drones 2

Blockchain based Security Solution for Internet of Drones

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Maurya, Sudhanshu Niveditha, V.R. Anandapriya, B. Verma, Rohan Mirza, Kashish Tiwari, Mohit School of Computing Graphic Era Hill University Bhimtal Campus Nainital India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Patrician College of Arts and Science Department of Computer Application Chennai India Hemvati Nandan Bahuguna Garhwal University Department of Computer Science and Engineering Uttarakhand Srinagar India Graphic Era Hill University Bhimtal Campus Department of Computer Science and Engineering Nainital India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474160

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information. © 2022 IEEE.

关键词： Drones

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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applications of the Decentralized Finance (DeFi) on the Ethereum

Applications of the Decentralized Finance (DeFi) on the Ethe...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2022

作者： Teng, Huang Tian, Wayneyuan Wang, Haocheng Yang, Zhiyuan Suffolk University Department of Computer Science and Math BostonMA02115 United States Carleton University School of Computer Science OttawaK1S5J5 Canada Macao University of Science and Technology Computer Technology and Application Harbin150010 China Xi'an Jiaotong-Liverpool University Department of Computing Suzhou215000 China

ISBN: (纸本)9781665409025

With the popularity of blockchain technology, more and more financial fields are ready to apply or have applied the blockchain technology to achieve technological upgrade. The Ether is the most popular platform which we can establish the smart contract to apply the blockchain technology. Decentralized financial DeFi is an important part of realizing the blockchain technology upgrade in the financial application. Based on blockchain technology, this paper introduces the basic working principle of blockchain and Ethernet. Then explains how it provides the technical foundation for decentralized finance as a technical innovation. It then proposes the advantages that DeFi apply in the financial sector and how it can bring benefits to individuals or enterprises based on the features of blockchain such as decentralization, no personal tampering and automatic information uploading. © 2022 IEEE.

关键词： Blockchain

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