International Journal of Communication Networks and Information Security (IJCNIS)
Not a member yet
1021 research outputs found
Sort by
Creativity and Innovation on the Adoptions of Creative Arts Activities: Attitudes and Perceptions of Kindergarten Teachers in Yunnan
No full textThe study aimed to explore the influences of attitudes and perceptions of creativity and innovation of kindergarten art teachers on creative art activities in the kindergarten art classes of the first-class public demonstration kindergarten schools in Qujing City, Yunnan Province. Questionnaires were employed to collect data from 261 kindergarten art teachers. Descriptive analysis and multiple regression were used to analyze the data. The findings revealed the statistical differences among six variables of the adoption of creative art activities, which included: 1) Creativity in Creative Art Activities; 2) Evaluation of Perception of Teaching Activities; 3) Specified Art activities in Art Education; 4) Planned Classroom Goals; 5) Planned Classroom Activities; and 6) Adopting Creative Art Activities into Creative Art Teaching. For future research, longitudinal and cross-regional comparisons to track long-term changes with combined research methods can be applied to ensure the efficiencies of kindergarten creative art teaching activities to promote Chinese kindergartens’ artistic senses and skills
Bibliotherapy: It’s Implementation in Achieving Organisational Goals
No full textSo many strategies have been used in organisations to inspire motivation in the achievement of organisational goals but some of them were without positive impact. It is prudent for organisations to try bibliotherapy as human nature usually do not want to be criticized, condemned and complained against. Bibliotherapy can trigger motivated action to individuals in organisations without being forced. This paper explores the concept of bibliotherapy and its potential implementation in achieving organisational goals. Bibliotherapy, traditionally used as a therapeutic tool for individuals, can be adapted to the organisational context to support employees in personal and professional development. It discusses the various ways in which bibliotherapy can be used to enable the achievement of goals. By leveraging bibliotherapy, organisations can foster a culture of continuous learning, innovation, resilience and inclusivity, ultimately contributing to the achievement of organisational goals. Bibliotherapy refers to carefully planned and structured interactions with literature guided by scaffolded questions and formally produced reflections to foster a motivated action. In today's DVUCADD environment an environment characterized by dynamic, volatile, uncertain, ambiguity, diversity and disruptive and competitive business environment, organisations are constantly seeking innovative approaches to enhance employee well-being, foster leadership development, build cohesive teams, manage change effectively and promote diversity and inclusion. Bibliotherapy, a form of therapy that uses literature to support individuals in addressing personal issues and achieving personal goals, presents an underutilized yet promising avenue for achieving these organisational objectives. By leveraging the power of bibliotherapy, organisations can create a culture of continuous learning, personal growth and resilience among their employees, ultimately contributing to the attainment of organizational goal
Moderating Role of Individual Diversity in the Relationship between Financial Knowledge and Financial Behaviour
No full textThe current paper examines the relationship of financial knowledge and financial behaviour in the presence of moderating variable i.e. Individual diversity comprised of Gender, Qualification and Residential background. Theoretically, when consumers lack knowledge of financial concepts, they are not able to make sound financial decisions which can be most detrimental to their financial well-being. Various studies revealed heterogeneity in financial behaviour and show that the typical household does not manage household finances well. Financial behaviour is influenced by financial knowledge because the act of financial behaviour which includes expenditure planning, budgeting and ensuring financial safety and avoiding over-use of credit etc would not be possible without having the knowledge of basic financial concepts like budgeting, saving etc. There is evidence that those who were more financially literate had higher financial practices index scores. Literature also revealed that the level of financial literacy depicted in the surveys are often criticized for not showing the true state of financial knowledge and behaviour etc as they are influenced by respondents characteristics like their sensitivity towards the wordings of the questions. Following a descriptive research design, an empirical investigation was carried out by approaching 400 respondents from India through physical questionnaires. The research instrument was developed using a five-point Likert-type scale and items for the constructs in study were taken after literature review. The SPSS 21.0, AMOS 21.0 and PROCESS (Prof A. Hayes) and Daniel Soper’s statistical tool called “Interaction” for moderation graph were employed for data examination and hypothesis analysis. It was found that the relationship between financial knowledge and financial behaviour is influenced by level of education and residential background of an individual and proved to be a significant moderator except gender. The study is original in the sense as it provides insights into understanding the financial behaviour of individual and its association with financial knowledge. The present study is an attempt to present a model that shows association of financial knowledge and financial behaviour. Knowledge of how financial services operate in the financial markets among masses should ensure financial wellbeing and sound financial decisions. Basic financial knowledge of an Individual is must for sound financial behaviour and it differs among individuals as the level of education and residential background of individual i.e. rural and urban locality influence such relationship. The results of the study should help the concerned authorities to introduce financial education programmes particularly to cater the needs of rural population to ensure sound financial behaviour
Probit Regressive Preprocessing Based Stochastic Gradient Decision Stump Tree Boosting Sentiment Classification for Recommendation System
No full textSentiment classification plays a significant role on service recommendation. Few research works have been designed for classifying the customer reviews with help of different classification algorithms. However, accuracy of conventional sentiment classification algorithm was not sufficient. In order to overcome such limitations, a Probit Regressive Preprocessing based Stochastic Gradient Decision Stump Tree Boosting Sentiment Classification (PRP-SGDSTBSC) Method is proposed. In PRP-SGDSTBSC Method, customer reviews are taken as an input from large database. After that, data preprocessing is carried out in PRP-SGDSTBSC Method by using Probit Regression model to analyze terms in customer reviews and to remove the stop words. After that, Stochastic Gradient Decision Stump Tree Boosting Sentiment Classification (SGDSTBSC) is carried out in PRP-SGDSTBSC Method to classify the customer reviews as positive and negative sentiment with higher accuracy and lesser time. The designed SGDSTBSC classifier model is an ensemble of several weak classifiers (i.e., decision stump tree). For every weak learner, preprocessed customer reviews are considered as training samples. Then, the weak classifiers are combined to form strong classifier to provide the final results as positive sentiment or negative sentiment. After obtaining the classification results, the recommendation is given to the user for particular item. Experimental evaluation is carried out on factors such as preprocessing time, classification accuracy, error rate and computational time with respect to number of customer reviews
Enhanced Medical Image Reconstruction Using Deep Learning Classification: A High-Resolution, Noise-Resilient Approach
No full textThis study introduces a novel deep learning classification (DLC) method for reconstructing high-resolution medical images, showcasing its superiority over traditional techniques such as zero-filled (ZF) and compressed sensing (CS), as well as deep learning regression methods with L1 (DLR-L1) and L2 (DLR-L2) loss functions. Unlike conventional methods, DLC generates a probability distribution for each pixel, allowing for the approximation of continuous pixel values, which effectively reduces quantization errors and preserves fine image details. To address the computational challenges of high bit-depth imaging, a divide-and-conquer strategy was implemented, enabling the DLC network to handle 16-bit images without a significant increase in network parameters. The DLC method's performance was compared to ZF, CS, DLR-L1, and DLR-L2 across scenarios with high acceleration factors, low signal-to-noise ratios (SNR), and high bit-depths. The results demonstrated that DLC consistently produced images with superior resolution, sharper edges, and better preservation of low-contrast features. Quantitative metrics, including structural similarity index (SSI), peak signal-to-noise ratio (PSNR), relative error, and mean squared error (MSE), further validated the DLC method’s superiority, particularly in noisy environments. These findings highlight the potential of DLC as a powerful tool in medical imaging, offering significant improvements in image quality and diagnostic accuracy across various applications
Influence of Capital Structure on Financial Metrics of Packaged Foods Companies Traded on the Bombay Stock Exchange
No full textThe study investigates the capital structure and its impact on the financial performance of packaged foods companies listed on the “Bombay Stock Exchange (BSE). Capital structure, a mix of debt and equity financing, plays a crucial role in determining a company's financial health and value. The research aims to study Capital Structure & Financial Performance of Packaged Foods Companies. By analyzing data from financial statements and annual reports over the past decade, the study provides empirical evidence on the relationship between capital structure and financial performance in the Indian packaged foods sector. The findings will offer valuable insights for investors, financial managers, and policymakers, helping them make informed decisions to optimize capital structures and enhance financial outcomes. This study also contributes to existing literature by addressing the gap specific to the Indian context and providing recommendations for industry stakeholders1. The results indicate significant correlations between capital structure components and financial performance, highlighting the need for strategic financial management in the packaged foods industry”. The implications of this research extend to policy development, aiming to foster a financially stable and competitive sector
Design of EEG for Imagined Speech Translation System Using Deep Learning
No full textNeuroscientists attempting to create effective brain-computer interfaces have difficulty turning the wide variety of brain impulses into flawlessly conveyed words and images. Gradually, the science fiction notion of controlling machines or interacting with people using only the mind is becoming a reality. Despite the significant progress achieved with deep learning algorithms, converting brainwaves to words has been a major challenge for scientists. Deep learning algorithms create a neural network for the brain and a neural network-based mapping that translates all brain inputs into actions. The work focuses on the imagined speech recognition of electroencephalography (EEG) brain signals by expanding the brain-computer interface to include persons who struggle with speech and communication. Decoding imagined speech from nonlinear and nonstationary EEG brain signals is challenging. Research in imagined speech has indicated that decoding performance and precision must be enhanced. Developing deep learning technology increases the likelihood that imagined speech may be deciphered from EEG data with improved performance. To retrieve information from EEG signals, we suggested an unsupervised deep learning model Deep Belief Networks (DBN), for determining the subject's vowel from their cognition. Using DBN, the overall classification accuracy was 80 per cent. The investigation used an open-access dataset containing fifty individuals to imagine vowel voice sounds. Multichannel EEG raw data from multiple participants were pre-processed using bandpass filters, Independent Component Analysis and Median Absolute Deviation, and then the features were extracted using discrete wavelet processing. The model was trained and validated to improve the accuracy of imagined speech recognition
Proposed Framework for Electronic Personal Health Records in Malaysia: A Novel Study Integrating Medical Expert Verification, Patient-Viewable PHR Attributesand Patient-Centric Design
No full textHealthcare provision and access to patient Personal Health Records (PHRs) are improved in recognition of the critical significance of health to human well-being, particularly in Malaysia's healthcare system. Malaysia requires extensive research and nationwide adoption of electronic personal health records (e-PHRs), regardless of the worldwide acceptance of this technology. Patients' essential and preferred devices are equipped with the capability to conveniently manage and monitor health records via these digital tools. Nevertheless, we identified significant challenges in the form of patient self-care deficiencies that have been discovered in the current PHR system. In order to secure patient-centric care and improve e-PHR self-management, innovative methods have been devised in response to the lack of patient-accessible critical PHR attributes and data integrity concerns. Our research goal is to propose innovative designs and deployment strategies that enable patients to access health information via widely used platform devices. In order to enable medical access customized to the Malaysian context, we integrate crucial PHR attributes that are accessible both online and offline. Commencing an extensive research endeavor, our primary concern is the applicability of our findings to the medical environment in Malaysia. By examining the e-PHR framework, e-health literature, and the participation of Medical Experts (MEs) at the UTeM Health Centre, we determine crucial, necessary PHR attributes and develop novel e-PHR frameworks for Malaysia public healthcare. The deployment model provides a comprehensive overview of the execution of each phase and is informed by the responses to questionnaires, surveys, and interviews. The research, which was conducted at the Health Center, Technical University of Malaysia (UTeM) is an innovative case study. By constructing a blueprint for the architecture of the proposed ePHR system, our research affords medical professionals the ability to develop a solution that has been customized to the medical environment in Malaysia. 80% of the healthcare professionals agree that patients should have access to their own health information. Over 70% of respondents acknowledge the significance of patients comprehending vital sign data, and 65% emphasize the necessity of an e-PHR system that is easy to use. These observations underscore the significance of accessibility and patient engagement in the healthcare industry. Figures 3 and 4 illustrate the significance of precise data in establishing confidence among patients. In order to mitigate these concerns, the proposed framework places an emphasis on patient-friendly design and data integrity. A new era of medical accessibility has been brought in with the provision of multi-platform access to patients' health records via the proposed PHR framework and deployment paradigm. Through the provision of user-friendly health management tools, our research aids in the enhancement of medical outcomes in Malaysia. Despite the lack of extensive e-PHR research and nationwide deployment, the results embody Malaysia's aspiration to ensure that all citizens have access to efficient and accessible healthcare services
Control of PCO2 in perfusion System using Deep Neural network Internal Model Controller
No full textThe Auto regulation of PCO2 consists of an actual HLM setup with an Automatic Gas flow controller instead of manual control. The setup also consists of Blood Gas Analyzer (BGA), Blood Flow meter, Temperature sensor and Mode selector switch. In this setup the arterial blood sample is fed to the BGA and complete blood gas analysis is done. The PCO2 value in terms of (mmHg) measured by the BGA is given as an input to the automatic Gas Flow controller through mode selector switch. The mode selector switch is controlled by the temperature sensor. The sensor decides the mode to be operated which depends on the temperature of the various blood sample. The mode selector switch has four modes Mild, Moderate, Deep, Profound temperature conditions. In this work, profound temperature condition is considered and Deep Neural Network Internal Model Controller is proposed for PCO2 control in perfusion system. The performance of the proposed controller is tested with various PCO2 values and the outputs are recorded
Development of Solar energy harvesting (SEH) for Internet of Things (IoT) to enable continuously replenishing energy resources in mobile wireless sensor networks (WSN)
No full textThis research article introduces an intelligently based solar energy developed harvested systems designed to provide longer term & stable powers to a Wireless Sensor Network (WSN) using IoT devices. The system includes a solar panel, a li batteries & a controller circuitry, utilizing hardware’s for lithium battery charge management to enhance reliability and stability. It prioritizes solar energy utilization under adequate sunlight, with the lithium battery serving as a backup during unfavorable conditions. Integration of a maxm. power points tracking’s (MPPTs) circuit optimizes solar based energies utilization and prolongs the lithium battery's lifespan by reducing charge-discharge cycles. This approach supports the use of small power equipment, making it suitable for outdoor-based IoT applications. The system offers a reliable, efficient, and sustainable power solution for various IoT applications, ensuring uninterrupted operation in dynamic environments