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Reassessing Deep Learning in Time Series: Predicting Time with Foresight

Reassessing Deep Learning in Time Series: Predicting Time wi...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Vinutha, C.B. Vivek, V. Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Machine learning relies heavily on time series forecasting, which has a wide range of uses, including although not limited to forecasting air quality, traffic patterns, and power use. Recent developments in deep training and matrix factorization models, however, have shown competitive performance and are emerging as alternatives. However, a fundamental disadvantage of these contemporary models over more established ones is their complexity. The Gradient Boosted Regression Tree, a well-known machine learning baseline, and popular deep learning models are compared in this work. We convert the time series forecast challenge into a window-based prediction issue, which is akin to (DNN) models. In addition, we design the GBRT model's input and output structure. In order to construct a single input instance to simulate a multi-output GBRT model, we concatenate the desired values with additional characteristics for each training window before flattening them. Using nine datasets, we undertake a thorough investigation and evaluate eight cutting-edge deep learning models recently presented at prestigious conferences. The findings show that a simple GBRT model performs much better than the state-of-the-art DNN models evaluated in this research thanks to the window-based input transformation. © 2023 IEEE.

关键词： Deep Learning DNN and Temporal Regularized Matrix Factorization Feature engineering Gradient Boosted Regression Trees (GBRT) Time Series Forecasting

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A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans 6th

A Deep Convolution Network-Based Pneumonia Identification fr...

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6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Upreti, Kamal Mishra, Prateet Maheshwari, Manish Vats, Prashant Dhankar, Aisha Batra, Reenu Yadav, Saneh Lata Chawla, Tanvi Bhardwaj, Jayant Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi New Delhi India Department of Computer Science and Information Technology Symbiosis University of Applied Sciences MP Indore India Department of Computer Science and Applications Makhanlal Chaturvedi National University of Journalism and Communication MCNUJC Madhya Pradesh Bhopal India Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India Department of Electronics and Communication Bhagwan Parshuram Institute of Technology GGSIPU New Delhi India

ISBN: (纸本)9789811976599

Pneumonia is an infectious ailment which affects the respiratory system and is caused by microbes that infect the human by affecting their lung air sacs and fill them with fluid. Using chest X-ray is the most popular approach for detecting pneumonia, and the results must be calculated and examined by medical experts. The inconvenient way of detecting pneumonia results in a loss of human life owing to incorrect approach and treatment. With the advancement of computer and information technology, developing an autonomous system that can help in the diagnosis and prevention of pneumonia is now viable, especially if the patient is in a place where the medical facilities and expert doctors are limited. In order to solve this challenge, this project uses deep learning technologies. The neural network was customized to accomplish the difficult task of detecting diseases such as pneumonia in order to aid medical experts in their diagnosis and treatment options. CNN applies a filter to a photo in order to extract data from a chest X-ray. The above-mentioned goal was achieved using Python programming language, as well as deep learning and neural network methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep CNN Recognition of pneumonia Rectified linear function Thoracic X-ray component

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kale, Kiran Raja Praveen, K.N. Savita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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Fostering Smart Agriculture: Using Vision-Based AI for Livestock Managing

Fostering Smart Agriculture: Using Vision-Based AI for Lives...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Shukla, Gaurav Gowri, K. Sudhamsu, Gadug Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Jain University Faculty of Engineering and Technology Department of Computer Science Engineering India

ISBN: (纸本)9798350344387

The swine industry constitutes a significant segment within livestock farming. However, conventional methods employed for monitoring pig health and welfare are laborious and time-intensive. Relying on these traditional monitoring techniques becomes increasingly taxing for both pigs and farmers, particularly in the face of the expanding scale of hog farming. This stress, in turn, impacts pig welfare and overall economic productivity. Integrating artificial intelligence (AI) has emerged as a fundamental component in the realm of intelligent pig farming. Advanced pig farming systems leverage AI integration to continuously monitor biometric data encompassing pig vocalizations and behavior. This data, acquired through sensors such as cameras and RF ID devices, is transformed into pivotal indicators of pig health and welfare. Analyzing these essential variables enables the early identification of health issues affecting pigs, facilitating prompt interventions and treatments that significantly enhance production efficiency and financial outcomes in pig farming. This paper presents a comprehensive overview, highlighting the challenges and opportunities within precision pig husbandry. The fusion of AI technologies with traditional farming practices presents novel prospects and hurdles, shaping the landscape of modern pig farming. Through this evaluation, the paper aims to underscore the transformative potential and the evolving landscape of precision-based pig farming. © 2023 IEEE.

关键词： Artificial intelligence (AI) Livestock management Pig farming Pig health Precision farming and Behavioral analysis Sensors Smart agriculture Vision-based AI Welfare monitoring

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The Role of Modern Machine Intelligence in Decoding Activity Patterns from Visual Data of Various Organisms

The Role of Modern Machine Intelligence in Decoding Activity...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Gowrishankar, J. Kumar, Ritesh Srivastava, Dharmesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

The integration of modern machine intelligence presents a transformative paradigm in the study of organisms' behaviors through visual data analysis. Leveraging deep learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this approach automates the extraction of intricate patterns from images and videos, enabling comprehensive insights into diverse organisms. Object detection algorithms further facilitate precise tracking. By surpassing traditional methods, it enhances accuracy and scalability. Ethical considerations guide responsible research. This fusion of technology and ecology fosters a deeper understanding of natural ecosystems, paving the way for sustainable coexistence with the environment and the preservation of biodiversity. © 2023 IEEE.

关键词： Data handling

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AI-Powered Developments in Oncological Diagnostic Imaging: Revolutionary Approaches to Cancer Treatment

AI-Powered Developments in Oncological Diagnostic Imaging: R...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Singh, Shweta Ashwini, B. Vivek, V. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

This project aims to demonstrate new uses of deep learning techniques in the detection of cancer. A huge body of examples that show the desired behaviour may be used to train a system using deep learning, which is a family of algorithms. Introduction of deep learning methods to tumour imaging may help pathologists identify and categorise cancer early on so that patients can get the right therapies to improve their chances of surviving or recovering. Utilising statistics and other analytical techniques and scienceDirect have data, it appears that since the late 1990s, a sharp increase in research on deep learning technologies in medical imaging has been motivated by the exceptionally high mortality rates of some cancer types in order to address the concomitant issues of a more precise recognition and the identification of cancer types to use effective therapies for cancer. This study also identifies the institutions, nations, and intellectual subject areas with the greatest levels of intellectual output in multiple disciplines as the origins of the trajectory of deep learning technology in cancer detection. According to Amara's law, this novel technique may result in a change in the way that cancer diagnoses and treats its many forms. When they can submit digital photos to laboratories in other developed nations to have malignancy kinds diagnosed, this fresh innovation may also aid developing regions, narrowing the present healthcare divide as much as feasible. © 2023 IEEE.

关键词： Cancer Detection Cancer Types Deep Learning Early Diagnosis Machine Learning Medical Imaging Metastatic Cancer Pathologists and Clinical Oncology Therapies Tumour Imaging

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Powering Development Wirelessly: Improvements in Far-Field Instrument Power

Powering Development Wirelessly: Improvements in Far-Field I...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar Sinha, Deepak Singh, Swati Sivaperumal Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In this study, far-field mobile electrical power options for inexpensively mobile sensors are thoroughly investigated. Emphasis is placed on situations in which sensor placements are uncertain and battery replacement is not practicable. Low-power nondirectional mobile battery backup becomes a possibility for sensors with occasional data transmission demands, estimated radiofrequency (RF) power consumption ranging from 20 to 200 W/cm2, and a necessity for small sensor dimensions. The sensor system uses an antenna to collect coming GHz electromagnetic waves, then transforms this energy into electricity for a storage device, which could be a thin-film battery, using a rectifier circuit. This method makes it possible for the full platform-which includes gauges, an inexpensively Bluetooth transmitter, including a low-power CPU for management-to function continuously. To maximize performance, particularly at reduced incident energy levels, RF power receipt and power control circuits must be designed in concert. Both hybrid and modular power management circuitry options are described and evaluated in this research. © 2023 IEEE.

关键词： Bluetooth wireless Energy harvesting and Efficiency Optimization Far-field power Low-power sensors Mobile sensors Power management Rectenna design System integration Wireless energy transfer

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MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced Quality of Service 10

MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced...

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10th International Conference on Wireless Networks and Mobile communications, WINCOM 2023

作者： Sadia, Haleema Iqbal, Hafsa Ahmed, Ridha Alaa Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi Pakistan National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350329674

Non-orthogonal multiple access (NOMA) scheme has proved a potential candidate to meet the design challenges of orthogonal multiple access (OMA) schemes. High spectrum efficiency and higher data rates are further achieved by integrating NOMA with multiple input multiple output (MIMO) technology. In this article, we have explored orthogonal space-time block coding (OSTBC) to improve the quality of service (QoS) for the proposed MIMO-NOMA framework. We specifically employed the Alamouti STBC scheme, which provides full diversity for the proposed network. At the transmitter side, OSTBC and power domain multiplexing with superposition coding are utilized, while at the reception side, a low complexity maximum likelihood (ML) detector is used, followed by successive interference cancellation (SIC). Based on bit error rate (BER) evaluations, Monte Carlo simulations are performed to analyze the performance of OSTBC MIMO-NOMA system under various fading channels. The proposed framework is further investigated with a higher modulation scheme and different channel code rates. The investigated Alamouti scheme provides a maximum diversity gain of 2M (M shows the number of receiving antennas) without any need for channel state information at the transmitter end and a less complex ML detector at the receiver end. This research lays the groundwork for designing adaptive modulation and coding methods for future MIMO-NOMA based B5G wireless communication systems. © 2023 IEEE.

关键词： Quality of service

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Effective Co-Operation in Edge-Cloud Computation Networking

Effective Co-Operation in Edge-Cloud Computation Networking

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Mahesh, T.R. Savita Bhagwat, Renuka Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

To improve the performance of mobile devices, both the edge of the network and the cloud provide computing services. While the cloud can manage large processing needs, the edge can minimize latency by offering local computing services. Their cooperation is essential for edge-cloud computational business models since it increases the usage of computing resources and assures Quality of Service (QoS). To facilitate resource sharing between the network's edge and the cloud, this study proposes an effective architecture for mobility edge-cloud computing networks. It uses wholesale and buyback techniques. Study conducted design resource management issues for edge servers to manage wholesale and buyback agreements and for cloud services to establish wholesale rates and distribute local computing resources in order to maximize this sharing process. From the perspectives of both optimizing societal welfare and maximizing profitability in both the perimeter and the cloud, study conducted approach these issues. Study conducted illustrate the concavity of the social welfare for i) and suggest an ideal cloud resource management approach to maximize it. Study conducted first develop the conical shape of wholesale computing power with respect to the wholesale rate for option ii), given the difficulty of directly demonstrating the conical shape of the key issue. Study conducted then create an optimal pricing while computing administration approach for maximizing the resulting profits. According to numerical analyses, maximizing overall profit is consistent with promoting societal welfare, whereas maximizing individual earnings is achieved through maximizing pricing using cloud computing resource management. © 2023 IEEE.

关键词： Cloud computing Edge-cloud computation Mobile Edge Computing (MEC) Modelling and Performance analysis Profit transfers Profitability Quality of Service (QoS) Queueing mechanism Resource management Resource sharing social welfare

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