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Unsupervised Feature Selection Approach for Smartwatches
Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This studys unsupervised feature selection approach can automate the process and help identify important features preferred by a particular segment of users. The unsupervised feature selection method is applied for smartwatches. Smartwatches continue to gain popularity. It is important to understand which features are most important to users to design and develop smartwatches that are more engaging, user-friendly, and meet the needs and preferences of their target audience. The rapid pace of technological innovation in the smartwatch industry means that new features and functionalities are constantly being developed. Multi-cluster feature selection, Laplacian score, and unsupervised spectral feature are used. Conjoint analysis is done on the most common features in all three selection methods. The unsupervised feature selection technique is used for identifying the relevant and important features of new smartwatch users.The practical implication of the research is in the application of the technique in the new product design of smartwatches. The result of the study also informs smartwatch manufacturers and developers on the features they need to prioritize and invest in. This can ultimately result in better and more user-friendly smartwatches and a good overall experience for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Disaster resilience of flood in Kerala, India
Kerala, the southern state in the Indian peninsula, has been affected by floods for the last three consecutive years. Changing weather patterns leading to heavy monsoon and development without considering the ecological vulnerabilities of the region has been pointed out as the reasons for flooding. Displaced communities, the destruction of agricultural and industrial enterprises, and health concerns have made disaster management a challenge for communities and governments alike. Even though there were lots of difficulties, the way Keralites came out of all these miseries and their adaptation was really inexplicable and always provided scope for research in that area. This paper focuses on examining the flooding pattern and impact of floods in Kerala, India and assessing the resilience capacity of the affected community. Self-developed questionnaires were used to gather data from the flood-affected population in the most flood-affected districts in Kerala. To gauge the respondents' opinions, the questionnaire used a five-point variable Likert scale. When all was said and done, 260 valid questionnaires were successfully retrieved. The study found that communities show resilience to flood with partnership and decentralised management of disasters. The study could help recognise the strategies for building resilient communities through policy intervention and civil society participation. Published under licence by IOP Publishing Ltd. -
A Survey on Feature Selection, Classification, and Optimization Techniques for EEG-Based BrainComputer Interface
In braincomputer interface (BCI) systems, the electroencephalography (EEG) signal is extensively utilized, as the recording of EEG brain signals is having relatively low cost, the potentiality for user mobility, high time resolution, and non-invasive nature. The EEG features are extracted by the BCI to execute commands. In the feature set obtained, the computational complexity increases, and poor classifier generalization can be caused by the utilization of a lot of overlapping features. The irrelevant features accumulation could be avoided with the feature selection procedures application. The feature selection algorithms are utilized to select diverse features for each classifier. Classifiers are the algorithms that are run to attain the classification. The researchers have examined diverse classifier implementation techniques to identify the feature vectors class. A review of EEG-BCI techniques available in the literature for feature selection, classifiers, and optimization algorithms is presented in this work. The research challenges, gaps, and limitations are identified in this paper. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Blockchain Empowered IVF: Revolutionizing Efficiency and Trust Through Smart Contracts
Couples who are having trouble becoming pregnant now have hope thanks to in vitro fertilization (IVF), a revolutionary medical advancement. However, the IVF procedure calls for a large number of stakeholders, intricate paperwork, and highly confidential management of information that frequently results in inaccuracies, mistakes, and worries about data confidentiality and confidence. In this study, the revolutionary potential of the blockchain and smart contracts enabling the treatment of IVF is investigated. The IVF procedure may be accelerated by utilizing smart contracts, resulting in improved effectiveness, openness, and confidence among everybody involved. The paper explores the primary advantages of using smart agreements in IVF, including automation, implementing obligations under contracts, doing away with middlemen, assuring confidentiality and anonymity, and enabling safe and auditable operations. The implementation of electronic agreements and blockchain-based technologies in the discipline of IVF is also investigated, along with the problems it may face and possible alternatives. This study offers insightful information about the use of intelligent agreements and blockchain technology in the field of IVF, accompanied by conducting an in-depth evaluation of the literature on the topic, research papers, and interviews with professionals. The results demonstrate the possibility of lower prices, more accessibility, higher success rates, and better patient experiences in the IVF field. In general, this study intends to illuminate how blockchain and smart contracts have revolutionized IVF technological advances, opening the door for a more effective, transparent, and reliable IVF procedure. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Systematic Study on Unimodal and Multimodal Human Computer Interface for Emotion Recognition
A systematic study for human-computer interface (HCI) for emotion recognition is presented in this paper, with a focus on various methods used to identify and interpret human emotions. It delves into various methods used to identify and interpret human emotions and highlights the limitations of unimodal HCI for emotion recognition systems. The paper emphasizes the benefits of multimodal HCI and how combining different types of data can lead to more accurate results. Additionally, it highlights the importance of using multiple modalities for emotion recognition. The study has significant implications for mental health assessments and interventions as it offers insights into the latest techniques and advancements in emotion recognition. Future research can use these insights to improve the accuracy of emotion recognition systems, ultimately leading to better mental health assessments and interventions. Overall, the paper provides a valuable contribution to the field of HCI and emotion recognition, and it underscores the importance of taking a multimodal approach for this critical area of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring the Influence of Service Learning on the Socio-Educational Commitment and Self- Efficacy of Graduate Educators in the Artificial Intelligence (AI) Domain.
This study, conducted by a distinguished university, aims to contribute significantly to the professional development of educators dedicated to creating a fair, sustainable, and socially conscious world. The research focuses on a pedagogical approach using Service Learning to foster civic and social skills in higher education students. The main goal is to examine how graduate students, actively participating in Service-Learning initiatives, develop socio-educational commitment and self-efficacy compared to traditional university volunteering. The study, involving 1562 aspiring educators, employs a quantitative correlational methodology. The hypothesis suggests that Service-Learning leads to more positive outcomes in socio-educational commitment, pedagogical self-efficacy, and crafting instructional materials. The findings, statistically significant (p < 0.01), highlight the increased development of these metrics among participants in Service-Learning programs. 2024 IEEE. -
Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE. -
Regression Analysis using Machine Learning Algorithms to Predict CO2 Emissions
Precise measurement of fuel consumption and emissions plays an important role in evaluating the environmental effects of materials and stringent emission control methods, especially within the transportation sector. This sector represents a substantial contributor to both global greenhouse gas emissions and the release of hazardous pollutants, making accurate assessment imperative for addressing climate change. The primary objective is to construct accurate predictive models that estimate CO2 emissions based on vehicle attributes, fostering a deeper understanding of the environmental impact of vehicular activities. Leveraging the 'CO2 Emissions-Canada.csv' dataset, the paper embarks on an extensive journey of data preprocessing, exploratory data analysis, and model training. These algorithms are meticulously fine-tuned and evaluated through metrics such as R-squared and mean absolute percentage error, rendering insights into their predictive accuracies. In essence, this paper pioneers a pathway towards environmentally responsible mobility solutions, capitalizing on the fusion of data science and environmental conservation. 2024 Bharati Vidyapeeth, New Delhi. -
A Study of Emotion Classification of Music Lyrics using LSTM Networks
Emotion Recognition is a vital component of human-computer interaction and plays a pivotal role in applications such as sentiment analysis, virtual assistants, and affective computing. Long Short-Term Memory (LSTM) models are a subset of Recurrent Neural Networks (RNNs). It has gained significant popularity for their effectiveness in sequence modeling tasks, including emotion recognition. The study presents a review on the application of Long Short-Term Memory (LSTM) networks for emotion classification using music lyrics. It offers a thorough review of relevant literature and outlines the methodology for implementing LSTM models for emotion recognition. Furthermore, the study emphasizes the significance of hyperparameter tuning in building effective machine-learning models, particularly LSTM-based models. 2024 IEEE. -
Blockchain Integrated Pharmaceutical Cold Chain: An Adoption Perspective
A complex and sensitive chain needs to be appropriately maintained to manage public health and people's lives. This is especially true of the cold pharmaceutical chain. The primary objective of this study is to explain how blockchain adaption might meet a pharmaceutical cold chain's requirements. A comprehensive technological adoption model, partial least square structural equation modeling, and a quantitative cross-sectional survey approach were utilized to identify stakeholder adoption intentions toward a blockchain-enabled cold supply chain. This study provides evidence that blockchain technology has the potential to support the objectives of the cold pharmaceutical chain. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
GWebPositionRank: Unsupervised Graph and Web-based Keyphrase Extraction form BERT Embeddings
Automatic keyphrase extraction is considered a preliminary task in many Natural Language Processing (NLP) applications that attempt to extract the descriptive phrases representing the main content of a document. Owing to the need for a large amount of labelled training data, an unsupervised approach is highly appropriate for keyphrase extraction and ranking. Keyphrase Extraction with BERT Transformers (KeyBERT) leverages the BERT embeddings that utilize the cosine similarity to rank the candidate keyphrases. However, extracting keyphrases based on the fundamental cosine similarity measure does not consider the spatial dimension locally and globally. Hence, this work focuses on enhancing the KeyBERT-based method with a Graph-based WebPositionRank (GWebPositionRank) design. The proposed unsupervised GWebPositionRank is the composition of graph-based ranking, referring to local analysis and web-based ranking, referring to the global analysis. To spatially examine the keyphrases, the proposed approach conducts the keyphrase position analysis at the document level through graph-based ranking and the web level using the WebPositionRank algorithm. Initially, the proposed approach extracts the coarse-grained keyphrases from the KeyBERT model and ranks the extracted keyphrases, the modelling of quality and fine-tuned keyphrases. In the GWebPositionRank method, the quality keyphrase ranking involves the document-level position analysis and four different graph centrality measures in a constructed textual graph for each text document, whereas the fine-tuned keyphrase ranking involves the web-level position analysis and diversity computation for the quality keyphrases extracted from the graph-based ranking method. Thus, the proposed approach extracts a set of potential keyphrases for each document through the advantage of the GWebPositionRank algorithm. The experimental results illustrate that the proposed unsupervised algorithm yielded superior results than the comparative baseline models while testing on the SemEval2017 dataset. 2024 IEEE. -
A Methodology to Formulate Attainment Process of Outcome-based Education for Undergraduate Engineering Degree Programme
The Outcome-Based Education (OBE) has important role in accreditation of any engineering programme. The OBE involves attainment of programme mission, objectives and outcomes. The paper discusses a methodology to calculate attainment of programme educational objectives and programme outcomes. The results of particular batch 2020 were shown. The process would help in implementing OBE in any technical institution approved by AICTE, India. 2024 IEEE. -
Resume Ranking and Shortlisting with DistilBERT and XLM
The research presented in this paper offers a solution to the time-consuming task of manual recruitment process in the field of human resources (HR). Screening resumes is a challenging and crucial responsibility for HR personnel. A single job opening can attract hundreds of applications. HR employees invest additional time in the candidate selection process to identify the most suitable candidate for the position. Shortlisting the best candidates and selecting the appropriate individual for the job can be difficult and time-consuming. The proposed study aims to streamline the process by identifying candidates who closely match the job requirements based on the skills listed in their resumes. Since it is an automated process, the candidate's individual preferences and soft skills remain unaffected by the hiring process. We leverage advanced Natural Language Processing (NLP) models to improve the recruitment process. Specifically, our emphasis lies in the utilization of the distilBERT model and the XLM (Crosslingual Language Model). This paper explores the application of these two models in taking hundreds of resumes for the job as input and providing the ranked resumes fit for the job as output. To refine our approach further, two types of metrics for resume ranking, such as Cosine similarity score and Spatial Euclidean distance, are used, and the results are compared. Intriguingly, distilBERT and XLM result in different sets of top ten ranked resumes, highlighting the nuanced variations in their ranking approaches. 2024 IEEE. -
LBP-GLZM Based Hybrid Model for Classification of Breast Cancer
Classifying mammogram images is difficult because of their complex backgrounds and the differences in resolutions across the images. One of the toughest parts is telling the difference between harmless (benign) and harmful (malignant) tissue. This is hard because the differences between them are incredibly subtle. As a consequence, the distinctive features embedded within tissue patches become not just relevant but critical for the accurate and automatic classification of these images. Traditionally, efforts to automate this classification process have encountered limitations when relying on a singular feature or a restricted set of characteristics. The subtle variations in texture within these images often render such approaches insufficient in achieving high-quality categorization results. Recognizing this, the present investigation undertakes a more comprehensive approach by incorporating distinct feature extraction techniques - specifically, the utilization of Local Binary Pattern (LBP) and Gray Level Zone Matrix (GLZM). These techniques are adept at capturing and delineating the nuanced texture features inherent in mammogram images. By extracting and analyzing these textural nuances, the aim is to construct a hybrid model capable of classifying mammograms into three distinct categories: malignant, benign, and without the necessity for further examination or follow-up. This proposed hybrid model holds significant promise in the field of mammography classification by leveraging the strengths and complementary attributes of multiple feature extraction methods. The integration of LBP and GLZM aims not only to enhance the accuracy of classification but also to improve the robustness of the system in identifying subtle yet crucial differences in tissue textures. Ultimately, the goal is to create a hybrid feature extraction framework that augments the diagnostic capabilities of mammography, providing more precise and reliable categorization of breast tissue for effective medical decision-making and patient care. 2024 IEEE. -
Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During Covid
Presence of heterogenous huge data leads towards the 'big data' era. Technique's proliferation is rapidly increasing data and making dynamic changes that results in 'big data' world. Progressive transition in technologies and adoption of social media in the society also stepped into the 'big data' epoch. Social media popularity is uprising attention in the community. This platform reduces the communication gap among people. Recently, tweeter use increased with unprecedented rate. Presence of social media like tweeter has broken the boundaries and touches the mountain in generating the unstructured data. It opened research gate with great opportunities for analyzing data and mining 'valuable information'. Sentiment analysis is the most demanding, versatile research to know user viewpoint. Society current trend can be easily observed through social network websites. These opportunities bring challenges that leads to proliferation of tools. This research works to analyze sentiments using tweeter data using Hadoop technology. This study explores the big data arduous tool called Hadoop. Further, it explains the need of Hadoop in present scenario and role of Hadoop in storing ample of data and analyzing it. Hadoop cluster, HDFS, and Hive are also discussed in detail. Researchers enthusiastic work is deeply studied and presented here. Dataset used in performing the experiment is explained briefly. Moreover, this research explains thoroughly the implementation work and provide workflow. Next session provides the experimental results and analyzes of result. Finally, last session concludes the paper, its purpose, and how it can be used in upcoming research. 2024 IEEE. -
Design and Development of Teaching and Learning Tool Using Sign Language Translator to Enhance the Learning Skills for Students With Hearing and Verbal Impairment
This research paper presents a system designed for the students with verbal and hearing impairments by enabling realtime Sign-to-Text and Text-to-Sign Language conversion, with a specific focus on the Indian Sign Language (ISL). The proposed study aligns to the United Nations Sustainable Development Goal (SDG) of Quality Education. The system leverages cutting-edge technologies, MediaPipe for holistic key point extraction encompassing hand and facial movements, and Long Short-Term Memory (LSTM) architecture powered by TensorFlow and Keras for accurate sign language interpretation. This comprehensive approach ensures nuanced aspects of sign language, such as facial expressions and hand movements, are faithfully represented. On the receiving end, the system excels at Text-to-Sign Language conversion, allowing non-sign language users to interact naturally with sign language users through textual input transformed into sign language animations and Sign-to-Text conversion where the information from the sign language users is converted to text which ensures smooth communication. A user-friendly web application, developed using HTML, CSS, and JavaScript, enhances accessibility and intuitive usage for realtime communication. This research represents a significant advancement in assistive technology, promoting inclusivity and communication accessibility. It underlines the transformative potential of innovation infostering a more connected and inclusive world for all, regardless of their hearing abilities 2024 IEEE. -
Design and implementation of Adaptive PI control based dynamic voltage restorer for solar based grid integration
This paper introduces an innovative approach to address voltage fluctuations in solar-based grid integration by implementing an adaptive PI control-based Dynamic Voltage Restorer (DVR). This DVR is engineered to counteract voltage disruptions resulting from grid disturbances and the intermittent nature of solar energy generation. To achieve optimal performance in diverse operating conditions, the adaptive PI controller dynamically adjusts its parameters, adapting to changes in load and solar generation. The system is realized on a digital signal processor (DSP) and evaluated within a laboratory-scale solar-based grid integration setup. The findings reveal that the proposed system effectively mitigates voltage fluctuations, ensuring a stable integration of solar energy into the grid. The adaptive PI control-based DVR outperforms traditional PI control-based DVRs, particularly when dealing with variable solar energy generation. This approach holds significant potential for practical applications in solar-based grid integration systems. 2024 IEEE. -
Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms
In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE. -
Regression Analysis as a Metric for Sustainability Development: Validation of Indian Territory
The 2030 Development Agenda styled' Transforming our world The 2030 Agenda for Sustainable Development' was hugged by the transnational locales of the UN General Assembly in 2015. Monitoring the progress of countries towards achieving these pretensions is pivotal for sustainable development. This exploration paper offers an innovative stance toward foretelling the SDG Index of Indian states for the near future times using machine learning ways, logical and visualization tools. The paper focuses on India's sweats towards achieving the SDGs and investigates the factors impacting the SDG performance of individual Indians states. A comprehensive dataset is collected, encompassing a wide range of socio-profitable pointers, demographic data, and environmental criteria applicable to each SDG target. Literal SDG Index scores and corresponding state-specific data are collected to assay and find some trends. The study demonstrates the eventuality of vaticination ways in vaticinating the unborn SDG Index scores of Indian states. The time series graph showcases varying degrees of delicacy across different SDGs, indicating the complexity and diversity of experimental challenges. 2024 IEEE.