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Experimental Design of Interoperable Smart Lighting for Elderly Care
Smart Home attains an active role in elderly care. Vision impairments caused by aging makes elders more dependent and affects the circadian rhythm or body clock. Some vision impairments can be improved by providing additional lighting. Smart lighting is the leading solution in providing adequate quality of lighting which helps elders to perform their daily activities independently. Various smart lighting solutions for elderly care are proposed in past and failed to consider about the energy loss due to over lighting. Additionally, the solutions are more independent in nature and not integrable to existing smart home solutions. To provide a solution to these ongoing challenges, an experimental design has been proposed to manage the adequate quality lighting for elderly people by controlling the illuminance and color temperature of the light with a feedback mechanism. Also, this experiment has integrated into a popular smart home platform. The proposed design keeps monitoring the ambient lighting and maintains the room's illumination as required for elderly individuals. The functional behaviors of the experimental design are evaluated using a testbed. The result shows that the proposed design reduces the energy usage more than 50% along with providing adequate lighting for elderly individuals. In addition, this experimental design promises that the proposed method can be easily integrated into any existing smart home solutions with its native scripting framework. 2024 IEEE. -
5G-UFMC System For PAPR Reduction Using SRC-Precoding With Different Numerologies
Universal Filtered Multicarrier (UFMC) has been incorporated in 5G and is likely to be considered in future generations (B5G). The prominent limitation of UFMC manifests as a high Peak-to-Average Power Ratio (PAPR). Our suggested approach to address the Peak-to-Average Power Ratio (PAPR) issue in UFMC signals involves the application of diverse precoding matrices, including Square Root Raised Cosine Function (SRC), Discrete Cosine Transform (DCT), and Discrete Hartley Transform (DHT).This technique reduces the PAPR performance of UFMC signals over current state of the art methods. In square root raised cosine (SRC) precoding techniques, a novel precoding matrix is adapted for minimizing PAPR and improvement of BER respectively. Results show that the different subcarrier was applied and surpasses all existing techniques in reduction of PAPR and BER improvement. A novel SRC-Precoding technique reduces PAPR by 5dB for considering 512 sample points with QAM modulation as compared to 10dB for the conventional technique. Additionally, the Bit Error Rate Performance is maintaining 14dB when compared to conventional technique. Furthermore, the evaluation of Bit Error Rate (BER) performance and Peak-to-Average Power Ratio (PAPR) in the UFMC system reveals superior results compared to conventional technique. 2024 IEEE. -
Single-Stage Bidirectional Three-Level AC/DC LLC Resonant Converter with High Power Factor
The increasing demand for efficient and high-performance power converters in electric vehicle technology and renewable energy integration has brought attention to LLC resonant converters due to their advantages in soft switching, inherent short circuit and open circuit protection, and high efficiency. These converters are particularly well-suited for high-frequency operation, making them ideal for electric vehicle battery charging and other power conversion tasks. However, when integrated with a front-end boost power factor correction (PFC) stage in AC-DC applications, challenges arise in maintaining power balance during transients, leading to voltage fluctuations and potential operational instability. Moreover, light load conditions can result in excessive switching frequencies, causing elevated switching losses and control difficulties. Additionally, traditional LLC resonant converters face limitations related to high voltage stress on switches, which affects device reliability and overall converter performance. To address these issues, researchers have explored the use of multilevel inverters, but they introduce complexity and cost. In this context, this paper proposes a novel single-stage, three-level bidirectional AC-DC LLC-based resonant converter with features like zero voltage switching and duty ratio control for output voltage regulation. The converter achieves a unity displacement power factor naturally through discontinuous conduction mode. Simulation results demonstrate the converter's effectiveness of the proposed topology. The proposed converter offers a promising solution for Electric vehicle chargers, combining unity power factor operation and efficient bidirectional power flow control in a single topology. 2024 IEEE. -
Synergy Unleashed: Smart Governance, Sustainable Tourism, and the Bioeconomy
This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions. 2024 IEEE. -
A Study on Factors Enhancing Immersive Virtual Reality Experiences
The objective of this study is to identify the various influential factors of immersive virtual reality (VR) experiences and examine the relationship between the immersion factors (technology, visuals, sound, interaction, and sound) and virtual reality experiential outcomes (satisfaction and loyalty). The survey comprises 412 participants who experienced VR games at the Orion Mall in Bangalore. The study has identified the prominent factors for enhancing the immersive experience. The factors are technology, visuals, sound, interaction, and sound. It also identified that there exists a positive association between VR experiential satisfaction and technology, visuals, sound, interaction, and sound. The results imply that service providers should focus on elevating immersive experience as it is closely associated with VR experiential satisfaction and VR experiential loyalty. This will increase the revisit intention and spread positive word of mouth about the virtual experiences. This paper provided valuable insights that pay way to analyze the association between immersion factors and VR experiential outcomes. 2024 IEEE. -
A Cognitive Architecture Based Conversation Agent Technology for Secure Communication
This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
AR and Online Purchase Intention Towards Eye Glasses
Augmented reality (AR) can be a potent tool for Indian online eyewear marketers by bridging the gap between online and offline purchasing experiences and meeting the needs of social validation and sensory engagement, which are preferences of Indian consumers. The present research explores how augmented reality (AR) technology affects Indian consumers' intentions to buy glasses online. A combination of descriptive and exploratory research design was used on the sample size of 236 consumers. Data was analyzed using frequency table and Structured Equation modelling (SEM) to identify the relationship amongst the variables. The findings indicate that accessibility to product information, telepresence, and perceived ease of use are important variables impacting purchase intention. AR can bridge the gap between online and offline experiences, meet consumer preferences, and create trust and confidence. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. 2024 IEEE. -
A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform
Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image. 2024 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. -
Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study
There is a strong need for having an operative security framework which can help in making IoT (Internet of Things) devices more secure and reliable which can further protect from adversarial intrusions. Federated Learning, due to its decentralized architecture, has emerged as one of the ideal choices by the research practitioners in order to protect sensitive data from wide IoT-based attacks like DoS (Denial of Service) attack, Device Tampering, Sensor-Data manipulation etc. This paper discusses the significance of federated learning in addressing security concerns with IoT (Internet of Things) devices and how those issues can be minimized with the use of Federated Learning has been deliberated with the help of comparative analysis. In order to perform this comparative analysis, we investigated the published work in FL based IoT application for the last five years i.e., 2018-2022. We have defined a few inclusion/exclusion criteria and based on that we selected the desired paper and provided a comprehensive solution to IoT based applications using FL approach. Federated learning offers an optimistic approach to intensify security in IoT environments by enabling collaborative model training while preserving information privacy. In this paper a framework named Federated AI Technology Enabler (FATE) has been envisaged which is one of the recommended frameworks in safeguarding security and privacy measures of IoT devices. 2024 IEEE. -
Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning
Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including nae Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. 2024 IEEE. -
Perception to Control: End-to-End Autonomous Driving Systems
End-to-end autonomous driving systems have garnered a lot of attention in recent years, and researchers have been exploring different ways to make them work. In this paper, we provide an overview of the field with a focus on the two main types of systems: those that use only RGB images and those that use a combination of multiple modalities. We review the literature in each area, highlighting the strengths and limitations of each approach. We also discuss the challenges of integrating these systems into a complete end-to-end autonomous driving pipeline, including issues related to perception, decision-making, and control. Lastly, we identify areas where more research is needed to make autonomous driving systems work better and be safer. Overall, this paper provides a comprehensive look at the current state-of-the-art in end-to-end autonomous driving, with a focus on the technical challenges and opportunities for future research. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Role of AI in Enhancing Customer Experience in Online Shopping
AI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression. 2024 IEEE. -
Hybrid Deep Learning Cloud Intrusion Detection
The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. 2024 IEEE. -
Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
Investors and other stakeholders like consumers and employees, increasingly consider ESG factors when making decisions about investments or engaging with companies. Taking into account the importance of ESG today, FinNLP-KDF introduced the ML-ESG-3 shared task, which seeks to determine the duration of the impact of financial news articles in four languages - English, French, Korean, and Japanese. This paper describes our team, LIPIs approach towards solving the above-mentioned task. Our final systems consist of translation, paraphrasing and fine-tuning language models like BERT, Fin-BERT and RoBERTa for classification. We ranked first in the impact duration prediction subtask for French language. 2024 ELRA Language Resource Association. -
Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE. -
An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. 2024 IEEE. -
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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.