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Intelligent Time Management Recommendations Using Bayesian Optimization
This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. 2024 IEEE. -
Inter Frame Statistical Feature Fusion for Human Gait Recognition
Researches showed that gait is unique for individuals and human gait recognition gained much attention nowadays. The sequence of gait silhouettes extracted from the video sequences has its own significance for gait recognition performance. In this paper, a novel inter frame feature discriminating the individual gait characteristics is proposed. Consecutive frames within a gait cycle are divided into equal number of blocks and corresponding block differences are calculated. It can preserve the minute temporal variations of the different body parts within each block and the cumulative difference provide a unique feature capable of discriminating individuals. To avoid synchronization problems, secondary statistical features are extracted from the primary inter frame variations. Finally, feature level fusion schemes are applied on these statistical features with existing features extracted from CEI representation. The efficiency of the proposed feature is evaluated on widely adopted CASIA gait dataset B using subspace discriminant analysis. The experimental results show that our proposed feature has better recognition accuracy in comparison with existing features. 2019 IEEE. -
Interactions between emotional and spiritual intelligence and their effects on employee performance
The association among worker behavior, spiritual intelligence, emotional intelligence, and system effectiveness is explained by this study. Understanding how others communicate and being aware of how one's own emotions affect others around you are all characteristics of emotional intelligence. Spiritual intelligence, which is a higher level of intelligence, reveals one's actual attributes and abilities. As company's most asset, the effectiveness of employee behavior has a significant impact on the company's ability to survive and thrive. In contrast to other facets of human conduct, employee conduct is distinguished by more formal behavior. This study aims to determine whether those with emotional and spiritual intelligence perform well at work. This research also aims to comprehend the behavior of emotionally intelligent and spiritually inclined people at work. Attempts are made in this study to ascertain whether higher levels of spiritual and emotional intelligence might boost the efficacy of these abilities. In this study, productivity at work is the dependent variable, whereas emotional intelligence and spiritual intelligence are independent variables. The parameters that can assess the variables were established using a literature review and a few common surveys. An organized survey that considers the variables is developed to gather information from the working class. To determine the link between the variables chosen for this study, the gathered data was analyzed using statistical approaches such as partial correlation and correlation. 2024 Author(s). -
Internet of Things and Cloud Computing Involvement Microsoft Azure Platform
The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (Io T) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service's architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure Io Tservice, mainly Io THub and Io TCentral, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability. 2022 IEEE. -
Internet of Things and Machine Learning based Intelligent Irrigation System for Agriculture
Irrigated agriculture methods need a significant volume of water, and causes water waste. It is critically necessary to install an efficient watering system and lessen the volume of water wasted on this tiresome chore. It is a huge benefit of the computer vision (ML) - the Internet of Everything (Ot) era to construct expert machines that carry out this work successfully with little human endeavour. This work suggests an Embedded device Fluid ounces suggestion method for efficient water use with little farm involvement. In the agricultural field, IoT sensors are set up to capture important atmospheric and surface data. The obtained information is sent to and stored on a cloud-based server, where machine learning techniques are used to evaluate the information and recommend treatment to the farmers. This recommender system has an internal development process that makes the solution resilient and flexible. The test demonstrates that the suggested method operates admirably on the agricultural dataset from the National Institutes of Technology (Kit) Bhubaneswar as well as the information that we obtained. 2022 IEEE. -
Internet of things based metaheuristic reliability centered maintenance of distribution transformers
The transformer is a vital component of the power system. Continuous stress on the transformer due to overload, transient and faults will lead to physical damages. The isolation of the transformer causes significant revenue loss and inconvenience to the consumers at the distribution level. This invites the need to achieve a reliable power supply to the consumers and to perform maintenance activity appropriately. Optimized and predictive maintenance strategies are evolved to improve power availability for consumers. The model considers dispersive generation at the customer end, namely solar photovoltaics standalone system, diesel generation, and vehicle to load capabilities. Incipient or critical status of transformers' functional parameters are observed through the transformer terminal unit and sent to the internet of things platform. The remote processing unit acquires the information from all the distribution transformer and generates the optimized and reliability-centered maintenance schedule. In the proposed work, new reliability indices concerning the consumer dispersive generation are defined. The maximization of the reliability problem is solved using the coconut tree optimization technique. The highest reliability of power supply to the consumer and maintenance schedule are obtained. Economic facet of the estimated maintenance schedule exhibit benefit for both utility and consumer as it encapsulate time of use tariff. The heuristic dataset is used to synthesize the trained model by the machine learning algorithm and future maintenance schedule is predicted. The comparative study is made for the outcome of time-based optimized and predicted maintenance schedules against reliability. 2020 Institute of Physics Publishing. All rights reserved. -
Internet of Things Enhancing Sustainability of Business
When one assumes that the current era is the era for digital revolution then the Internet of Things (IoT) is supposed to be one of the most significant among all. It is the IoT which is assisting the bussinesses. Current IoT applications, on the other hand, are still in their early stages, and the true capacity of viable business opportunities has yet to be realised. However, IoT adoption may need considerable integration and experienced personnel. It also frequently generates new requirements in terms of security and interoperability, or the ability for different computer hardware systems as well as software applications to "speak"to one another. 2022 IEEE. -
Interpreting Scope of Predictive Analytics in Advanced Driving Assistant System
Distracted driving, caused by various factors such as human emotions or reading distracting messages on the roadside, has become a leading cause of traffic accidents today. Ensuring the safety of both individuals and vehicles while minimizing maintenance costs poses a significant challenge for the automotive industry. Fortunately, recent advancements in machine learning offer a potential solution. One promising method is the further development of Advanced Driver Assistance Systems (ADAS), for which machine learning serves as an ideal solution. The proposed model develops an advanced predictive learning enabled driving assistance system with prediction capabilities like traffic light behavior and parking availability detection. The model gave an optimum accuracy of 98.2% with 50 epochs count and the validation loss retains a constant value of 0.3 over epochs. 2023 IEEE. -
Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach
Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. 2024 IEEE. -
Interval-Valued Fuzzy Trees and Cycles
Interval-valued fuzzy tree (IVFT) and interval-valued fuzzy cycle (IVFC) are defined in this chapter. We characterize interval-valued fuzzy trees. We also prove that if G is an IVFG whose underlying crisp graph is not a tree then G is an IVFT if and only if G contains only ? strong arcs and weak arcs. It is shown that an IVFG G whose underlying crisp graph is a cycle is an IVFC if and only if G has at least two ? strong arcs. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Into the Dark World of User Experience: A Cognitive Walkthrough Study
In this age of AI, the unison of man and machine is going to be more prominent than ever, thus creating a need to understand the underlying framework that is adopted by app designers and developers from a psychological point of view. Research on the various benefits and harmful effects of user experience design and furthermore developing interventions and regulations to moderate the use of dark strategies in digital tools is the need of the hour. This paper calls for an ethical consideration of designing the experience of users by looking at the unethical practices that exist currently. The purpose of the study was to understand the cognitive, behavioural and affective experience of dark patterns in end users. There is a scarcity in the scientific literature with regard to dark patterns. This paper adopts the methodology of user cognitive walkthrough with 6 participants whose transcripts were analysed using thematic network analyses. The results are presented in the form of a thematic network. A few examples of the themes found are the experience of manipulation in users, rebellious attitudes, and automatic or habitual responses. These findings provide a basis for an in-depth understanding of dark patterns in user experience and provide themes that will help future researchers and designers develop ethical and more enriching user experiences for users. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Intricate Plane of Adversarial Attacks in Sustainable Territory and the Perils faced Machine Intelligent Models
The issue of model security and reliability in Artificial Intelligence (AI) is a concern due to adversarial attacks. In order to tackle this issue, researchers have developed sustainable defense strategies, but certain challenges remain. These challenges involve transferability, higher computing costs, and adaptability. Striking a balance between accuracy and robustness is difficult, as defense mechanisms often come with trade-offs between the two. Real-world situations demonstrate the practical implications of sustainable adversarial AI. For example, it improves the security of self-driving vehicles, enhances the accuracy of medical imaging diagnoses, and incorporates AI-driven defenses into network intrusion detection and phishing detection systems. It is crucial to consider ethical aspects throughout this process. Future trends in adversarial AI research for cybersecurity will involve ensemble defense mechanisms, adversarial learning from limited data, and hybrid attacks. By embracing the evolving landscape, researchers and practitioners can develop sustainable AI systems that are more secure and resilient, effectively countering adversarial threats. 2023 IEEE. -
Inverse Hilbert Fractal-Metamaterial Rings for Microstrip Antennas and Wideband Applications
A Novel Metamaterial (MTM) property is obtained using a fractal pattern known as Inverse Hilbert. The Mu-negative(MNG) characteristics have been recovered by adopting NRW method. This MTM characteristic is studied for 2.45 GHz using FR4 epoxy as substrate. The dimension of the substrate is 30mm36mm 1.6mm. This fractal metamaterial structure can be amalgamated with an optimized Microstrip antenna (MSA) for improvement in antenna parameters and can be used for RF energy harvesting. 2022 IEEE. -
Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI
The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictors of no-show behaviours among patients. Six machine learning models were developed and evaluated on this task using Area Under the Precision-Recall Curve (AUC-PR) and F1-score as metrics. Our experiment demonstrates that Support Vector Classifier and Multilayer Perceptron perform the best, with both scoring the same AUC-PR of 0.56, but different F1-scores of 0.91 and 0.92, respectively. We analysed the interpretability of the models using Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP). The outcome of the analyses demonstrates that predictors such as the patients' history of missed appointments, the waiting time from scheduling time to the appointments, patients' age, and existing medical conditions such as diabetes and hypertension are essential flags for no-show behaviours. Following the insights gained from the analyses, this paper recommends interventions for addressing the issue of medical appointment no-shows. 2024 IEEE. -
Investigating Personalized Learning Paths to Address Educational Disparities Using Advanced Artificial Intelligence Systems
This innovative study reimagines the role of Natural Language Processing (NLP) in individualized education by highlighting the critical need to incorporate cultural subtleties. While natural language processing (NLP) offers great potential for improving classroom instruction, current research frequently fails to account for the complex issues caused by cultural variation. This research fills a significant need by providing a novel framework for the detection and incorporation of cultural subtleties into individualized learning programs. Further research into common biases is driving the development of natural language processing models with greater cultural sensitivity and awareness, such as gender bias in Named Entity Recognition (NER) and sentiment bias in cultural preferences. In order to correct past biases and promote gender neutrality in educational content, the research makes use of an adaptive NER algorithm and a diverse training dataset. Similarly, to guarantee nuanced and fair sentiment evaluations, the study suggests regularly evaluating and retraining sentiment algorithms with datasets that represent multiple cultures. A Cultural Relevance Score of 0.9, Adaptive Content Embedding vectors [0.3, 0.6, -0.2.], and an impressive Cosine Similarity of 0.85 are some of the evaluation measures that highlight the effectiveness of the research. These measurements show encouraging gains, which confirms that the research might help make schools more welcoming and sensitive to different cultures. The research has the potential to revolutionize individualized education by making it more accessible and engagingfor students from all backgrounds. 2024 IEEE. -
Investigating the Use of Natural Language Processing in Electronic Medical Record
Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons' notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control. 2024 IEEE. -
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor. 2022 IEEE. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd. -
Investigation of detoxification nature of activated carbons developed from Manilkara zapota and de oiled soya
Heavy metals are poisonous and detrimental water contaminant. Their existence affects human beings, animals and vegetation as a outcome of their mobility in aqueous ecosystem, toxicity and nonbiodegradability. This work aimed at the development of new adsorbent in the detoxification of heavy metals using Manilkara zapota tree wood and de oiled soya. The study completely focused on the characterization of the developed activation in the view of using it as a adsorbent. The characterization of activated carbon was effected SEM analysis, FTIR, XRD analysis and surface area determination. Both the activation carbon have showed a tremendous characterization in their employability as adsorbent in adsorption of heavy metals in aqueous solution. 2019 Elsevier Ltd. All rights reserved. -
Investigation on AI-Based Techniques in Applications for Detecting Fatal Traffic Accidents
The difficulties with road accident rates today rank among the top concerns for health and social policy in nations across the continents. In this essay, we've spoken about the fatalities and injuries brought on by traffic accidents in several Indian states. We have also shed light on the various factors that contribute to traffic accidents. Many researchers have reported various methods for identifying automobile crashes or accidents that are discussed in this work. Additionally, we covered collision avoidance systems and their various kinds. An examination of the analysis techniques used to comprehend the numerous causes causing accidents is also included in the study. Traditional models are frequently used to identify problems such driver weariness, drowsiness, driving while intoxicated, and distractions. 2023 IEEE.