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Towards various applications of Big Data and related issues and challenges
A new trend in feature abstraction is Big Data Analysis combined with computational techniques. This includes gathering knowledge from reputable data sources, analyzing information quickly, and forecasting the future. Big data entails vast amounts of data that are challenging to analyze using typical database and software approaches. When using big data applications, a technological hurdle arises when transporting data across several locations, which is quite expensive and necessitates a huge primary memory for storing data for processing. Big data refers to the transaction and interaction of datasets whose size and complexity transcend the usual technical capabilities of acquiring, organizing, and processing data in a cloud environment. This article provides an in depth study of various applications of big data. It also provides a detailed view on various problems and challenges in Big Data. 2021 IEEE. -
Tracking and Localization of Devices - An IoT Review
S everal IoT applications have immediate impacts on daily lives. The notion of "connected life, which includes IoT has been discussed. Apps that rely on localization are also featured. IoT is originally used to determine the precise position of things, animals, and people. The second tracks everyone and everything that's on the move, including pets, kids, and the elderly people. Localization and tracking are integral parts of security and surveillance systems in interconnected homes. This study reviews the state-of-the-art IoT-based localization and tracking approaches and outlines the key technical aspects, and contrast localization initiatives based on Internet of Things (IoT) with those that do not show how they might be used in a variety of contexts. It is now well established that localization and tracking methods based on the Internet of Things (IoT) are more pervasive and accurate than their predecessors. 2023 IEEE. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Traffic Management in Forest and Ecosystem Conservation. A Study on NH 766 Through Bandipore National Park and Proposing a Traffic Management Plan with Alternate Route Consideration
Transportation network is inevitable in the developing world. In India where we have a rich forest cover, many of the roads are passing through eco-sensitive areas such as national parks and wildlife sanctuaries. There are issues being reported due to these roads passing through the eco-sensitive areas such as animal deaths due to road accidents, loss of habitats, fragmentation of ecosystems, and loss of forest cover. The CalicutKollegal national highway, NH766 is passing through Bandipore national park on the stretch which connects Sultan Bathery and Gundelpette. Recently, a conflict had risen between environmental activists and the public for imposing a complete traffic ban along the NH766 passing through the Bandipore NP. A baseline study had conducted on the NH766, and the impact of the same on the ecosystem existing is analyzed through the data collected. A network analysis is performed on the alternate route available for bypassing the traffic. Traffic management plan and policies are derived out of the analysis on the baseline data collected and the inferences drawn from the network analysis performed on the alternate routes. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Traffic Optimization and Route Detection Based on Air Quality and Pollution Level
This research outlines the development of a groundbreaking Traffic Optimization and Route Detection system based on pollution and air quality. Urbanization has led to increased vehicular traffic, exacerbating concerns about air pollution and its adverse effects on public health. The proposed system aims to address this critical issue by integrating real-time environmental data into route recommendations, prioritizing routes that minimize exposure to high-pollution areas. Beyond improving air quality, the system promotes the health and well-being of commuters, encourages the adoption of eco-friendly transportation modes, and contributes to overall environmental sustainability. An air quality detection system is developed to gather data for the development of the system. This innovative approach aligns with the goals of efficient urban mobility, sustainable transportation, and data-driven decision-making. Through this research, we anticipate providing valuable insights into the potential impact of integrating pollution and air quality considerations into urban transportation systems, ultimately contributing to healthier and more sustainable urban environments. 2024 IEEE. -
Transference of Love-type Wave Through Cobalt Ferrite Cofe2O4 Layer Structure, Governed by an Imperfect Interface
An analytical discussion of the wave transmission in a piezomagnetic (Cobalt ferrite) thin plate resting on an elastic substrate is presented in the problem. It is presumed that the geometrys interface is not ideal. The flaw of the considered structure is used to describe by following the linear spring model. The calculative method of the upper material is Direct Sturm-Liouville. Dispersion relations are drived for each of the magnetically open and magnetically short cases. Love-type wave velocity profiles have been depicted on graphs for various influencing factors, such as heterogeneity in the substrate, layer thickness, and interface imperfections. It has been demonstrated that raising these parameters raises the Love waves phase velocity. Furthermore, it is found that compared to substrate heterogeneity, layer thickness has less of an impact on the waves velocity profile. Additionally, it has been shown how the aforementioned cases compare when imperfect parameters are varied. It is discovered that the velocity in the open case is greater than that in the short case. The results have potential applications in the design of piezomagnetic semiconductor devices controlled by electric fields and are of great significance for developing surface acoustic wave (SAW) gyroscopes. 2024 American Institute of Physics Inc.. All rights reserved. -
Transformative Insights: Unveiling the Potential of Artificial Intelligence in the Treatment of Sleep Disorders - A Comprehensive Review
Disruptions to sleep have a substantial influence on people's overall health and quality of life. The conventional techniques for diagnosing and managing sleep disorders usually rely on subjective assessments and qualitative evaluations, that may have some accuracy and efficacy limitations. Nevertheless, recent developments in the field of artificial intelligence (AI) have presented new opportunities for better diagnosing and treating problems with insomnia. The paper reviews in depth the uses of AI in the domain of medical sleep medicine. We look at the use of algorithmic techniques for deep learning and machine learning for identifying indicators of sleep-related issues, the assessment of sleep quality, sleep tracking, and the establishment of individualized sleep therapeutics. We also discuss how AI is being used to construct forecasting models that may be used to identify individuals who are at risk of experiencing sleep issues and improve treatment strategies. In addition, we talk about the challenges and potential outcomes of incorporating AI-based techniques into clinical practice. Overall, our research highlights how AI has the potential to transform the field of sleeping medicine and improve outcomes for people with sleep-related conditions. 2023 IEEE. -
Transforming online class recording into useful information repositories using NLP methods: An Empirical Study
Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords. 2022 IEEE. -
Transforming Pediatric Healthcare with CKD using AI: A Systematic Mapping
Artificial intelligence has been used on a much larger scale, from self-driving cars to biometrics. The daily lifestyle of civilization has changed dramatically due to scientific growth. AI has been pushed to a wide range of applications rather than limited to certain areas and has benefited the health industry, resulting in improved outcomes. Heuristics, support vector machines, artificial neural networks, and natural language processing are some of the AI approaches employed. Kidney diseases and treatment can be challenging, especially when working with youngsters. Children with Chronic Kidney Disease (CKD) experience a wide range of symptoms classified as either transitory or nosologic. Some of its traits influence not only during childhood but also during adulthood in the long run. This study will focus on strategies utilized to identify, predict, and categorize the impacts of pediatric kidney disorders in terms of aetiology, clinical features, and medicines that might assist children in transition to adulthood smoothly. 2023 IEEE. -
Transforming towards 6G: Critical Review of Key Performance Indicators
With the experiences acquired upon the successful implementation of 5G networks academia, researchers, and industry are envisioning the need for 6G networks. The vision of the 6G communication network is supposed to completely assist the creation of a Ubiquitous Intelligent Mobile Society. Already 5G technologies are in place and still few extended features of 5G are continuously being introduced. Even though the 6G communication network is expected to have greater capabilities than the existing 5G, there are no clear specifications on how far these capabilities shall be capitalized in 6G. The 6G technologies shall move past ordinary mobile internet services and advance to support ubiquitous Artificial Intelligent (AI) services from the network's core to end-to-end service devices/applications. The architecture, protocols, and operations which are the primary constituents of the 6G network shall implement AI technologies for self-optimization and actualization. This article brings an all-inclusive deliberation of 6G based on an assessment of preceding generations' evolving technology developments. 2022 IEEE. -
Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation
In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all. 2024 IEEE. -
Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases
This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions. 2020 IEEE. -
Trident Shaped Compact Planar Antenna for Microwave Applications
A compact planar antenna for X/Ku-band microwave communication is suggested in this paper. The presented geometry is capable of radiating the large frequency band from 6.8 to 20GHz, which covers the X-Band/Ku-Band Communication with high efficiency. The impedance bandwidth of the radiator is 98.5%, with an electrical size of. 34?x.34?x0.034A in lambda. The suggested design includes a modified patch in the trident shape fed by a microstrip line. Rectangular elements have been designed for better resonances at lower modes. The antenna is simulated with an FR4 substrate using CST Simulator. The exact dimensions of the antenna are 15x15x1.5 cubic millimeter. Five stages evolution process is also investigated by simulations, and corresponding S-parameter results are presented. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 3.1 dBi, and an efficiency of 87%. Therefore, it is useful for X-band, and Ku-band, including Radar, Space, Terrestrial, and Satellite microwave communication. 2022 IEEE. -
Trust Model for Cloud Using Weighted KNN Classification for Better User Access Control
The majority of the time, cloud computing is a service-based technology that provides Internet-based technological services. Cloud computing has had explosive growth since its debut, and it is now integrated into a wide variety of online services. These have the primary benefit of allowing thin clients to access the resources and services. Even while it could appear favorable, there are a lot of potential weak points for various types of assaults and cyber threats. Access control is one of the several protection layers that are available as part of cloud security solutions. In order to improve cloud security, this research introduces a unique access control mechanism. For granting users access to various resources, the suggested approach applies the trust concept. For the purpose of predicting trust, the KNN model was recently proposed, however the current approach for categorizing options is sensitive and unstable, particularly when an unbalanced data scenario occurs. Furthermore, it has been discovered that using the exponent distance as a weighting system improves classification performance and lowers variance. The prediction of the users trust levels using weighted K-means closest neighbors is presented in this research. According to the findings, the suggested approach is more effective in terms of throughput, cost, and delay. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
TSM: A Cloud Computing Task Scheduling Model
Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions. 2023 IEEE. -
Tumor Infiltration of Microrobot using Magnetic torque and AI Technique
Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. 2023 IEEE. -
TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE. -
Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model
Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Nae Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers. 2020 IEEE. -
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE. -
Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT
ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. 2023 IEEE.