Search results

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

The study aimed to assess the digital literacy skills of the students of Sacred Heart College, Chalakudy, to know whether they possess the digital literacy skills to perform well in the digital environment. The study also analyzed how digital literacy skills were affected by various factors.

The study used stratified random sampling technique, and data were collected through a self-assessment survey using an online questionnaire designed based on DigComp 2.1: The Digital Competence Framework by the European Commission.

The results indicated that the college students needed training on digital literacy skills, as the majority students had only moderate to low digital literacy skills. It was proven that exposure to technology and the Internet will not necessarily yield skills to perform well in the digital environment. Also, digital literacy skills were not affected by various factors like age, level of study, etc.

The study helped to identify the digital literacy deficiencies in the students of Sacred Heart College, and it can serve as a valuable model for conducting similar investigations in diverse educational institutions. Conducting such studies offers institutions valuable insights, enabling them to create and implement personalized digital literacy training programs that can enhance students' abilities to navigate the digital landscape with proficiency and effectiveness. The study results can be insightful for educators, policymakers and the Kerala Government to reassess the current approaches and design an effective curriculum for integrating technology in education.

Although there are several studies that evaluated college students’ digital literacy in India and other countries, there are very few studies in the context of Kerala. Therefore, this study is distinctive and will serve as an example for all such studies in the future.

The concept of inclusive education has been largely debated and developed within a western context and its application within other cultural situations can be challenging. This chapter considers how the interpretation of inclusion within India is influenced by traditional values from within that society which may challenge some of the more conventional ideas within this area. In particular, consideration is given to the ways in which teachers and policy makers define those conditions that might support inclusive schooling and evaluate the ways in which schools are responding to change.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

The purpose of this paper is to examine the literature published on information literacy (IL) from 2005 to 2014 and reveal the key aspects of IL publication trends.

The study analyses the literature indexed in Web of Science database on IL from 2005 to 2014 and used the required bibliometric measures to analyse specific aspects of publishing trends.

The findings of the study reveal that increase in literature on IL from 2005 to 2014 was noticed. A high amount of annual growth of literature on IL is observed for the year of 2007, 2008 and 2011. “Pinto, M” and the “University of Granada, Spain” was productive author and institute. Journal of Academic Librarianship was the most productive journal, with 97 articles being published for the period. USA was the most contributing country. “Digital divide”, “media literacy”, “pedagogy”, “higher education” and “critical thinking” were current research topics in the IL domain.

The paper is very useful for researchers to learn about trends in the literature on IL, as well as possible areas for further research, and it provides the names of the most productive authors, organizations and countries, along with the most popular IL keywords.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Internal fluffy portion along with fibrous strands of ripe pumpkin is considered as waste in processing industries though it contains sufficient amount of ß-carotene pigment. The purpose of this paper is to use the leftover fluffy portion of ripe pumpkin (Cucurbita maxima) after the use of its flesh for the purpose of processing.

The data were analyzed statistically by following a complete randomized design (CRD). All analysis were performed using the software OPSTAT.

One hour pre-enzymatic treatment before solvent extraction showed significant improvement in extraction yield in comparison to the isolation of ß-carotene pigment through solvent only. Temperature time combination was optimized as 40°C for 2 h during solvent extraction to obtain maximum yield irrespective of the type of extraction method used.

Extracted carotene pigment can further be used as a natural food colorant in processed food products not only to enhance the color appeal but also it improves the nutritional value of the product as ß-carotene acts as a precursor of vitamin A.

Coloring agents of natural origin are becoming famous among society due to their health benefits. Consumers are becoming reluctant to use synthetic colors because of the undesirable allergic reactions caused by them, so carotene bio-pigment produced is a natural coloring compound with wide application in the food sector.

Even though few researchers have worked on the extraction of carotene pigment from pumpkin, but no researcher has reported the use of a waste fluffy portion of C. maxima for extraction of ß-carotene pigment.

The purpose of the paper is to describe the development and implementation of a specialized Virtual Learning Environment (VLE) designed to enhance the knowledge and skills related to sustainability in students with Down syndrome. This VLE serves as a means to make sustainable concepts more accessible and comprehensible to this specific student group, with the aim of promoting their engagement and understanding of sustainability, environmental awareness, recycling, and sustainable construction. The ultimate goal is to empower students with Down syndrome by providing them with a tailored educational tool that facilitates their learning in a manner that is engaging and effective.

The approach outlines the overarching plan for creating the e-learning platform, including the technological choices and design considerations necessary to make it effective and accessible for students with Down syndrome. It's a fundamental component of the methodology, as it sets the direction for the platform's development and aligns with the objectives of the study. And also encompass the strategy for teaching and learning sustainability aspects to students with Down syndrome.

Positive Feedback from Tutors and Professionals: The feedback from tutors and professionals is generally positive, with 91.4% finding the platform to be well-organized and 88.6% considering the content adequate and understandable. This suggests that the VLE met the needs and expectations of educators and professionals involved in the learning process. Utility for Professional Practice: Approximately 80% of tutors and professionals found the platform useful for their professional practice, indicating that it has practical applications beyond student learning. This information highlights the success and potential impact of the VLE for this specific target group.

The study may not have explored the depth of sustainability concepts covered within the VLE. Future research could delve into the specifics of the content and its effectiveness in teaching complex sustainability topics.

The incorporation of universal design principles in the VLE development could serve as a model for creating inclusive e-learning platforms. This has broader implications for improving digital accessibility in education. The positive feedback from tutors and professionals suggests the importance of interdisciplinary collaboration in education. Professionals from various fields, including special education and sustainability, can work together to create effective and inclusive learning tools.

This study can contribute to the broader discussion on inclusive education and the effective use of technology to enhance learning experiences for individuals with disabilities.

The study addresses a crucial gap in the field of sustainability education by focusing on students with Down syndrome. It highlights the importance of making sustainability education inclusive and accessible to a diverse range of learners, including those with disabilities. This originality contributes to the broader discourse on inclusive education and environmental awareness. The development of a specialized Virtual Learning Environment (VLE) for this specific target group is an original contribution. It demonstrates the potential for adapting educational technology to meet the unique needs of students with Down syndrome, potentially serving as a model for future educational tool development.

Alexa’s integration in rural primary schools has improved the pedagogy and has created an engaging and objective learning environment. This study investigates the integration, with a specific focus on exploring its various aspects. The impact of Alexa’s on students' English vocabulary, comprehension and public speaking are examined. This study aims to provide insights the teachers and highlight the potential of artificial intelligence (AI) in rural education.

This content analysis study explores the use of Alexa in primary education in rural areas of India. The study focuses on the types of the questions asked by the students and examines the pedagogical implications of these interactions. By analyzing the use of Alexa in rural educational settings, this study aims to contribute to our understanding of how voice assistants are utilized as educational tools in underprivileged areas.

Alexa significantly improved students' English vocabulary, comprehension and public speaking confidence. Alexa increased school enrollment and retention. Virtual voice assistants like Alexa may improve pedagogy and help India’s rural education. This study shows AI improves rural education.

The study only covers rural India. Self-reported data and observations may bias the study. The small sample size may underrepresent rural educational institutions in India.

Alexa is used to study rural India’s primary education. Voice assistants in rural education are understudied. The study examines Alexa’s classroom use, student questions, and policy and teacher education implications. AI’s education transformation potential addresses UNESCO’s teacher shortage. This novel study examines how AI can improve rural education outcomes and access.