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Envisioning the potential of Natural Language Processing (NLP) in Health Care Management
Natural Language Processing (NLP) continues to play a strategic role in disease detection, intensive care, drug discovery and control of mushrooming infections during the current pandemic. It energizes chat programs to reduce outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, and Deep learning language models. It supports more languages around the world. NLP powered AI such as Health map and Copweb platforms track patient requests and perform incident detections. This study looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier electronic health records (EHRs) maintenance in the health care industry. This research work explores the strategic approach and potential of NLP which maximizes the value of the EHR and healthcare data, making data a critical and trusted component in improving health outcomes 2021 IEEE. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE. -
Epileptic Seizure Prediction from EEG Signals Using DenseNet
Epilepsy is a disorder in which the normal electrical pattern in the brain is disrupted causing seizures or loss of consciousness. Seizure is harmful during various events like swimming or driving. The electroencephalogram (EEG) is the measurement of electrical activity received from the nerve cells of the cerebral cortex. Forthcoming seizures can be predicted from scalp EEG signal to improve the quality of life. The study proposes a method of automatic epileptic seizure prediction from raw EEG signal. The raw EEG signal is converted into EEG signal image for automatic extraction of features and classification of inter-ictal and pre-ictal state using Dense Convolutional Network (DenseNet). This classification process is carried out in a manner similar to the process followed by a medical practitioner without resorting to hand-crafted features. The public CHB-MIT EEG database is used for training, validation, and testing. An EEG signal for 1 second duration is taken as one sample. The accuracy for the classification of inter-ictal and pre-ictal state is achieved up to 94% by using 5-Fold cross validation. However, the accuracy is not up to the mark for the presence of common artifacts caused by eye-blinking and muscle activities during EEG recordings. Hence, a 30 seconds pool based technique is used for decision on correct state identification. The proposed pool based technique provides an average specificity of 95.87% and a false prediction rate of 0.0413/hour. It also provide average sensitivities of 100%, 97%, and 90% for the time slots 0 - 5 minutes, 5 - 10 minutes, and 10 - 15 minutes before the seizure event. 2019 IEEE. -
Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format. Published under licence by IOP Publishing Ltd. -
ESSA Scheduling Algorithm for Optimizing Budget-Constrained Workflows
Workflows are a systematic approach for defining various scientific applications of distributed systems. They break down complicated, data-intensive processes into minor activities that can be executed serially or in parallel according to the type of application. Cloud systems need to allocate resources and schedule workflows efficiently. Despite many studies on job scheduling and resource provisioning, an efficient solution isn't found. Therefore, techniques are required to enhance resource utilization for optimal cloud computing platforms. Hence, user and provider quality of service (QoS) goals, like shortening workflows and ensuring budget limits with low energy utilization, must be considered. Enhanced Salp Swarm Optimization (ESSA) is designed to optimize makespan and QoS metrics in cloud systems. A Virtual Machine (VM's) compute capacity is related to Central Processing Unit (CPU) and memory. Size and memory demand is considered for tasks in the workflow, and task execution time is evaluated using both CPU and memory. The collated experimental outcomes convey that the newly presented technique boosts the workflows' energy utilization (up to 89%) and pushes the normalized makespan results to 3.2ms. 2022 IEEE. -
Estimation of Vehicle Distance Based on Feature Points Using Monocular Vision
In this digital era safety and security have the highest precedence, the advanced driver assistance system is the latest trend and where many challenges are open for researchers. Vehicle to vehicle distance estimation is one of the most important challenges to provide the security and safety alerts for the driver. In order to achieve this, image of the front vehicle is captured using the single camera under monocular vision to estimate the vehicle distance. Then three key steps are designed to estimate the vehicle distance: extracting and locating the key features of the vehicle, characteristic triangle is drawn between those features to calculate pixel area and develop the measuring formula to calculate the distance. For efficient feature extraction and localizing of the feature position, conventional AdaBoost algorithm is utilized to find the strong features for scalable samples. Distance measurement formulation is used to derive the correlation between the pixel area and distance by considering the different parameters from the prototype of pinhole camera, camera standardization and plotting of area. Formula is developed to estimate the optimum moving distance between vehicles to vehicle. After the experimental analysis, the accuracy rate is improved and time complexity satisfies the precision. 2019 IEEE. -
Ethical and Societal Implications of Artificial Intelligence in Space Mining
The advent of Artificial Intelligence (AI) in space mining marks a pivotal shift in the exploration and utilization of extraterrestrial resources. This paper presents a thematic analysis of the ethical, societal, technological, economic, and environmental implications of integrating AI in space mining operations. Through topic modeling of relevant literature, five key themes were identified: AI integration and ethical considerations, economic efficiency and equity, technological innovations and advancements, international collaboration and governance, and environmental sustainability and planetary protection. These themes highlight the potential of AI to revolutionize space mining, enhancing efficiency and enabling the extraction of valuable resources beyond Earth. However, they also underscore the need for robust ethical frameworks, equitable economic models, international cooperation, and sustainable practices to address the multifaceted challenges posed by this frontier. The paper concludes with recommendations for future research and policy-making, emphasizing the importance of inclusive, collaborative approaches to ensure the responsible and beneficial advancement of space mining. 2024 IEEE. -
Ethical Tenets of Stock Price Prediction Using Machine Learning Techniques: A Sustainable Approach
The visible decline of ethics primarily gets reflected in financial markets, as it portrays human actions and sentiments in numerical terms than any sector. Accuracy in Stock market prediction remains inefficient due to many known and unknown variables. Academia and industry recently relied on ML at large to track the market and monetise the movements. The norms of fairness, accuracy, dependability, transparency in financing are left unattended in ML prediction models with assumptions far from reality. This study focuses on the ethical dimension of Machine Learning models and generates a sustainable framework for investors. Specifically, the Sustainable Development goals (SDG) can enhance the prediction models in ML with improved efficiency. Along with SDG, this research broadens the variables' horizon of prediction in ML of computer science domain with concepts of Socially responsible Investing (SRI), Environmental Social and Corporate Governance (ESG), and Carbon footprints. With One hundred fifteen articles reviewed, the proposed framework ensures sustainability in investments at the grassroots level. The Electrochemical Society -
Ethnic Food: A Solution With Sustainable Food Resources A Study On Consumer Awareness Of Ethnic Food And Its Impact On Consumption Attitude
Food has seen numerous transformations over the centuries and has been a focus of study pertaining to culture and evolution. Besides being a celebration of diversity and a marker of human adaptations, food is also a broad knowledge domain that represents various geographic, cultural and lifestyle outlooks. Ethnic food relates to a heritage or the culture of an ethnic group with them incorporating the local produce and animal sources into their diet. Ethnic food also has a sustainability aspect to it in terms of food miles and carbon emissions since more transportation involved means higher level of GHG released, economic aspect such as with composition changes and food security, and community relationship. This paper finds that when consumer awareness of ethnic food increases, the consumption attitude towards it does, too. This could be of importance in policy implementation and identifying sustainability systems. A connection with the land and a community relationship involving food could help represent more ethnic food, to increase awareness on a global level and also allow more people to experience these vast cultural diversities. If understood well and implemented, ethnic food could be of use as a tourism brochure, sustainability driver, economical promoter and community supporter. The Electrochemical Society -
Ethnic Food: The Food Way Forward
In the context of food security, two things are significant. To ensure availability, affordability and accessibility of adequate food to people throughout the country. Also, to promote entrepreneurship for sustainable food production and supply. This paper highlights differences between food security and food insecurity. The global population in 2050 is predicted at 9 billion in which case the output must double considering the dwindling and degrading resources. This may be a challenge for agronomists and policy-makers. Considering that food security must be achieved at individual, household, district, national and global levels, India may need an Integrated Farming System (IFS) to take agriculture further. There are numerous challenges besides the environment that must be considered for this. It is important to ensure that the dignity of the farmer is not compromised while strategizing food security. Currently, private-public partnerships are being introduced in some places as a potential model. However, all stakeholders in food security have their task cut out (1). This paper is a review of existing literature to understand the level of information we have documented. It tries to highlight ways in which consumption of ethnic food could be a way forward in terms of food security and sustainability. The Electrochemical Society -
ETL and Business Analytics Correlation Mapping with Software Engineering
Large information approach can't be effectively accomplished utilizing customary information investigation strategies. Rather, unstructured information requires specific information demonstrating methods, apparatuses, and frameworks to separate experiences and data varying by associations. Information science is a logical methodology that applies scientific and measurable thoughts and PC instruments for preparing large information. At present, we all are seeing an exceptional development of data created worldwide and on the web to bring about the idea of large information. Information science is a significant testing zone because of the complexities engaged with consolidating and applying various strategies, calculations, and complex programming procedures to perform insightful investigation in huge volumes of information. Thus, the field of information science has developed from enormous information, or huge information and information science are indistinguishable. In this article we have tried to create bridge between ETL and software engineering. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
EV Service Stations for Future Smart Cities
The market for electric vehicles (EVs) has been growing at a fast pace in recent years. It is expected to continue growing at a much faster pace in the coming decades. The emerging EV technology is increasingly gaining a high demand for continued good transport connections in smart cities. Most of the Smart Cities' charging infrastructure and future growth revolve around its public transport network, especially an EV service station. New technologies, therefore, need to be complemented with new and versatile charging options to cater to different types of charging options available for charging Li-ion Batteries with newer materials and charging capacity. Building an EV service station in the ongoing scenario anticipates smart engineering knowledge to complement innovative charging methods. An EV service station needs hardware, software, and test equipment before charging, during charge, and post-charge states. It is expected to inform the user of available options to choose and select from. This paper investigates the challenges and suggests solutions to meet the EV service station support for EV vehicles in present and future smart cities. It also highlights the demand for a skilled workforce to maintain these service stations, including updating their skills. Examples of a few smart cities in developed as well as developing countries have been quoted. These developments will contribute to the transport infrastructure needed for future smart cities. The paper paves the way for future research in this area. The Institution of Engineering & Technology 2023. -
Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE. -
Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting
Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. 2023 IEEE. -
EVALUATING THE ELEMENTS IN THE RECREATIONAL SPACE OF AN INSTITUTION
The concept of 'Recreation' justifies the human need for satisfaction, leisure, and a state of pleasure. The elements involved in a recreational space impact the activities of the user in that space. Recreational spaces act as the in-between sojourns for formal pedagogy or andragogy. Spaces of recreation are essential, especially in educational institutions, where students spend most of their time. Public, semi-public, and private spaces are all included in the institutional design, with a large percentage used by students. Open public spaces, including recreational places, are measured in terms of their physical characteristics and connections to nature. The components of a recreational area influence the activities that users engage in there. This paper seeks to list and assess the many components that are present in a recreational space. This study will evaluate those elements and their types. Informal outdoor areas or other breakout areas promote interaction and provide the students with refreshments and leisure. The focus of this paper is to draw out the quality of leisure space synonymous with a productive environment for the student, where they feel rejuvenated. Five recreational spaces of CHRIST University were studied, and the elements that combine to form this place were also observed. A survey among the students who are frequent users of these spaces was conducted, and their responses were evaluated. The elements that majorly help students go to a place were assessed, and the element's significant role was concluded. The result of this study to design professionals is to understand the need to incorporate recreational spaces while designing an educational institution and design a student-oriented space. ZEMCH Network. -
Evaluation of machine and deep learning models for utility mining-based stock market price predictions
Considering the extreme volatility of stock market returns and hazards, accurate price prediction has attracted the attention of both financial institutions and regulatory bodies. Stocks, due to their historically strong returns, have long been considered by investors to be an excellent asset allocation strategy. Predicting stock prices has never ceased being a hot topic of study. Many early-day economists sought to foretell future stock values. In subsequent years, as computer technology has advanced rapidly and mathematical theory has been extensively studied, it has been shown that mathematical models, like the time series model, may be very effective in predicting due to their simplicity and superiority. Over time, the time series model is put into practice. Over time, the horizon widened. Support vector machines and other ML techniques have challenges when applied to stock data because of its non-linearity. In subsequent years, thanks to advancements in deep learning, models like RNN and LSTM Neural Networks were able to analyze non-linear input, remember the sequence, and remember valuable information,Stock data forecasting cannot be done without it. 2024 Author(s). -
Evaluation of Maximum Bending Stiffness of Stranded Cables with Refined Kinematic Relations
The mechanical response of a helically stranded cable depends on the effective stiffness offered by the collective assembly of its constituent wires. This can vary between two extreme conditions, namely a monolithic state, also known as the stickslip state, wherein all the wires in the cable behave as a single unit with no relative movements among themselves, offering the maximum stiffness for the cable. In the other extreme condition, all the wires are free to move among themselves, with no frictional holding among them, thus offering the minimum stiffness. This paper reviews the various mathematical models that are available for the estimation of maximum bending stiffness and brings out the need for considering a vital parameter known as the wire stretch effect that has been neglected by many authors till date. The consequent fundamental changes that occur in the basic kinematic relations are brought out and refined expressions for the internal wire forces and moments are established for the first time in the coupled axial-bending analysis. Further, the shear displacement of the wire due to the stretch has also been included in the wire normal and binormal shear forces. A single-layered cable with core-wire contact has been considered for analysis and the numerical results are evaluated with these new inclusions and are compared with the published results. It is hoped that the refined model suggested in this paper for the accurate estimation of the maximum stiffness, will pave way for more reasonable cable analysis in the subsequent slip stages. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evaluation of Mechanical Properties and Microstructure of Polyester and Epoxy Resin Matrices Reinforced with Jute, E-glass and coconut Fiber
Composite manufacturing is a novel branch of science and often finds numerous applications in several industries. Some of them are sport, automobile, aerospace and marine industries. Some of the properties that can be highlighted are good mechanical properties along with stiffness and comparatively lighter weight. There is a continuous research in this area is as the constant pursuit to achieve greater performance by changing various materials and the combinations of those with various resins are experimented. In the current work, polyester and epoxy resins were reinforced with coconut, E-glass and jute fibers of 5-6mm length and were prepared by hand layup method. The fiber and resin were taken in 18:82 weight percentages. Post production of the composites they were subjected to various physical mechanical and microstructural studies to determine various properties. The morphological features were analyzed through the microstructural study done through scanning electron microscope. In comparison with the composites manufactured, The artificial fiber reinforced composite, E-glass fiber reinforced epoxy composites exhibited superior tensile strength, flexural strength, impact toughness and hardness values. Among the natural fiber reinforced composite, coconut fiber reinforced composites exhibited better tensile, impact and hardness than its counterpart jute reinforced composites. Thus the resins reinforced with E-glass fiber had the highest mechanical properties when compared with jute fiber reinforced composites (JFRC) and coconut fiber reinforced composites (CFRC). The cost effectiveness of the natural fiber reinforced composites is also an added advantage over the artificial fiber reinforced composites. 2018 Elsevier Ltd. -
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression
In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding peoples emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evaluation of the inhibition efficiency of Pogonatum microstomum for mild steel in acid medium using gravimetric, kinetics, electrochemical studies and statistical modeling
Mosses from a distinct lineage of bryophyte family are found as thick green carpet on the moist rocks, trees, soil or streams. It is acclaimed for its good antimicrobial properties and is a reservoir of various phytochemicals. The nontoxicity nature and abundant availability in nature was exploited for the first time to investigate its effectiveness as novel and green corrosion. Present study deals with the evaluation of corrosion inhibition efficiency of the moss, Pogonatum microstomum using the electrochemical studies and weight loss studies. The moss extract showed a maximum corrosion inhibition efficiency of 95.28 % for 3hrs of immersion period at 303 K. Increase in the inhibition efficiency with concentration of moss extract is the result of adsorption of the constituents which are active on the surface of the metal. Tafel polarization and electrochemical impedance studies gave results on par with the weight loss measurements. The experimental results obtained were further validated by statistical analysis and statistical modeling using SPSS 20 software. 2020 American Institute of Physics Inc.. All rights reserved.