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Food quality traceability prototype for restaurants using blockchain and food quality data index
As competition between organizations are evolving into competition between supply chains, to survive and indeed grow, it is necessary to deliver added value to customers. Traceability has emerged as one of the key measures of operational efficiencies within supply chains and ultimately, customer service. Over the years, organizations have deployed number of methods in delivering food traceability. This paper examines major methods of food traceability currently in existence and proposes a restaurant prototype for implementing more reliable food traceability using Blockchain and product identifiers. The prototype captures data from various stakeholders across the food supply chain, segregates it and finally, applies the Food Quality Index (FQI) algorithm to generate an FQI value. The FQI value helps in identifying whether the food is good for consumption on specified parameters. FQI value is generated based on extant standard storage and handling regulations specified by food safety authorities, and checks whether value so derived, is within the permissible range. The prototype helps in grading food quality for human consumption besides strengthening food (product) traceability. This prototype can be customized to address future requirements of traceability triggered through new information emanating from any stakeholder or the node in the supply chain. 2019 Elsevier Ltd -
Predicting Employee Attrition Using Machine Learning Algorithms
Employees are considered the foundation of any organization. Due to their importance, the Human resources department implements various policies to sustain them. Yet the attrition rate in any organization is increasing yearly. The attrition rate signifies the number of employees who leaves a firm without being replaced. It is regarded as a well-known issue that requires the administration to make the best choices to retain highly competent staff. It is interesting to note that artificial intelligence is frequently used as a successful technique for foreseeing such an issue. This review paper aims to study the different machine learning approaches that predict employee attrition and factors influencing an employee to attrite from an organization. A Hybrid model comprising the various ensemble models is proposed to predict attrition at its earliest. The forecasted attrition model aids in not only taking preventive action but also in improving recruiting choices and rewarding top performers who contribute to the company's success. 2022 IEEE. -
Adapting Employee Engagement Strategies Amid Crisis: Insights from the COVID-19 Pandemic
Crises are unpredictable events that have the potential to strike at any moment, causing significant disruptions to work, daily routines, and the normal course of life. The COVID-19 Pandemic served as a facilitator for transformative changes in the way we work, shifting to an era of remote and flexible work arrangements across industries. This crisis underlined the importance of employee engagement and organizational culture-building in navigating unforeseen situations. As organizations prepare for the future, it becomes crucial to anticipate and adapt to potential crises that may arise. The effect of the pandemic varied from industry to industry. When the technology industry worked towards creating a virtual workspace, the production industry strived to continue production without disruption. However, irrespective of the industry, HR teams across the board were dedicated to identifying and addressing the challenges posed by the crisis. They have worked tirelessly to ensure employee engagement remains a priority. This qualitative study explores the challenges encountered by HR teams during the pandemic and explores the strategies and policies they adopted to foster employee engagement. The data was collected through an in-depth interview with 39 HR Practitioners from different industries. The significant challenges included the need to cultivate a sense of community, navigate muddled up HR processes, sustain productivity amid disruptions, and prioritize employee wellness. To provide a comprehensive analysis, this study examined industry-specific approaches, employing within-case analysis to understand key strategies in communication, rewards and recognition, employee benefits, wellness initiatives, and fostering an enjoyable virtual workplace. This study offers a forward-looking perspective and serves as a guide for organizations aiming to thrive in times of uncertainty, ensuring that employee engagement remains a strategic priority regardless of the crisis at hand. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Review on the Electrochemical Sensing of Flavonoids
Flavonoids are bioactive polyphenolic compounds, widespread in the plant kingdom. Flavonoids possess broad-spectrum pharmacological effects due to their antioxidant, anti-tumor, anti-neoplastic, anti-mutagenic, anti-microbial, anti-inflammatory, anti-allergic, immunomodulatory, and vasodilatory properties. Care must be taken, since excessive consumption of flavonoids may have adverse effects. Therefore, proper identification, quantification and quality evaluations of flavonoids in edible samples are necessary. Electroanalytical approaches have gained much interest for the analysis of redox behavior and quantification of different flavonoids. Compared to various conventional methods, electrochemical techniques for the analysis of flavonoids offer advantages of high sensitivity, selectivity, low cost, simplicity, biocompatibility, easy on-site evaluation, high accuracy, reproducibility, wide linearity of detection, and low detection limits. This review article focuses on the developments in electrochemical sensing of different flavonoids with emphasis on electrode modification strategies to boost the electrocatalytic activity and analytical efficiency. 2022 Taylor & Francis Group, LLC. -
60Co gamma irradiation and annealing effects on transport properties of antimony telluride platelets grown by physical vapor deposition
Physical vapor deposition method was employed to deposit antimony telluride (Sb2Te3) crystals in a dual-zone furnace. The microstructure, surface topography and composition of samples were characterized using X-ray diffraction, atomic force and scanning electron microscopy. Seebeck coefficient (S?c), electrical conductivity (??c) as well as power factor (PF) were enhanced for pure Sb2Te3 samples upon annealing, and the samples annealed at 473 K exhibited the highest PF of 3.16 10-3 W m-1K-2 with an enhancement of 22% in the figure of merit (Z). When the delivered dose of 60Co gamma radiation was increased from 0 to 30 kGy in the stoichiometric crystals, ??c decreased due to the decrease in mobility. As a result of the increase in S, PF and Z improved by 12.11 and 13.7%, respectively, in the 30 kGy gamma-irradiated crystals. Both RH (B?c) and S?c were positive, suggesting that the prepared Sb2Te3 crystals retained the p-type semiconductivity after these treatments. The Chinese Society for Metals and Springer-Verlag 2015. -
Synthesis and Characterixation of Fluorinated Superconducting Y3Ba5Cu8Oy Compound
International Journal of Engineering Research and Applications, Vol-3 (1), pp. 927-930. ISSN-2248-9622 -
Fairness-Aware and Interpretable Depression Detection on Social Media Using BERT with Gender Bias Mitigations
Reddit and similar social media platforms offer substantial information regarding mental health issues. The automatic detection of depression raises different issues pertaining to fairness and transparency. This paper presents a Fairness Aware and Interpretable Depression Detection framework that utilizes BERT and incorporates an explicit gender bias mitigation mechanism. Data were obtained from gender-specific forums on Reddit. The Mistral language model based classifier was used to set a high confidence threshold, which helped in inferring gender labels while both depressed and non-depressed were among the patients assigned the labels. A balanced dataset with four groups (Depressed-Male, Depressed-Female, NonDepressed-Male, NonDepressed-Female) was prepared. Two pipelines were carried out where one involved a baseline BERT classifier while the other employed a fairness aware BERT model that incorporated gender embeddings during the training phase. The models were assessed using accuracy, precision, recall, F1 score, and confusion matrices and the fairness metrics applied were Demographic Parity Difference (DPD) and Equal Opportunity Difference (EOD). To enhance the model's reasoning transparency, SHAP was applied due to its capability to provide clear and comprehensive explanations. The results indicated that the fairness centered model effectively reduced gender biasness and equalized error rates among the different groups without losing its original accuracy. The essential point is that the model had learned to give precedence to clinical indicators over gender specific language. This study suggests a roadmap for the creation of ethical AI by combining fairness, interpretability and high performance into a seamless framework. 2025 IEEE. -
Artificial Intelligence and Machine Learning in Educational Apps
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in educational applications is transforming higher education by enhancing personalized learning, intelligent tutoring, and predictive analytics. This chapter explores AI-driven functionalities, including adaptive learning, NLP, and automated assessments, while addressing challenges such as data privacy, algorithmic bias, and accessibility. Through case studies, it highlights AIs transformative potential in shaping future education, offering insights for educators, researchers, and developers interested in AI-driven learning innovations. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Assessing the role and effectiveness of NGOs in enhancing elementary education in government primary schools in Karnataka: A SWOT analysis
Elementary education in Karnatakas government schools faces several challenges, including inadequate infrastructure and a shortage of human resources. Non-Governmental Organizations (NGOs) have emerged as pivotal players in addressing these gaps. This research paper conducts a comprehensive SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of NGOs involved in elementary education across Karnataka. Drawing on interviews with 50 NGOs across four revenue districts in Karnataka, the paper explores their internal strengths, challenges, and potential areas for increasing their impact. The findings highlight key strengths of NGOs including their strong relationships with stakeholders as well as their innovative and flexible approaches to implementing educational programs. However, weaknesses include limited funding and organizations heavy reliance on volunteers. Despite these challenges, opportunities exist to run more development programs and leverage technological advancements. At the same time, threats such as frequent changes in laws and regulations, resistance from authorities, and rigid teacher and parent mindsets pose barriers to the effective and efficient running of NGOs educational programs in Karnataka. The Author(s) 2026 -
Sustainable mosquito control: A tool in the fight against Aedes aegypti using Flemingia wightiana
Mosquito-borne diseases, particularly those transmitted by Aedes aegypti, have been proven to be a global health challenge. A. aegypti, a major vector of Zika virus, Dengue virus, Chikungunya, is traditionally controlled through synthetic insecticides. However, the factor of environmental issues and rising insecticide resistant breeds have prompted the exploration of eco-friendly and sustainable alternatives. Here, we attempt to use the leaf extract of Flemingia wightiana to produce silver nanoparticles (FWAgNP). The construct of AgNPs was first indicated by UV-Vis spectroscopy, with a peak at 461 nm. NP was then characterized by SEM, EDX and functional groups were analyzed using FTIR spectroscopy. Safety assessments of synthesized NP were carried out on Oreochromis niloticus. Percentage mortality was studied on A. Aegypti with both test samples, FWAgNP and FWME. FWAgNP were found to be effective; the lowest percentage mortality of 70 % was recorded for forth instar larvae and 100 % mortality was observed in the first and second instar larvae. Oxidative stress assays such as AChe, SOD, CAT, GSH and GST were carried out. SOD, CAT and GSH showed significant elevated levels. GST and AChe levels reduced as the concentration increased, indicating the role of test samples in oxidative stress. Antiviral assay was conducted to check the effect of AgNPs in inhibiting the growth and infection of Zika virus (ZIKV) on Vero cells. The percentage inhibition property of AgNP was found to be 25 %. In conclusion, the developed FWAgNPs have significant potential in the control of vectors and a limited inhibitory activity on Zika virus. The Author(s). -
Biocontrol potential of Flemingia wightiana: A natural weapon against Culex quinquefasciatus
Globally, mosquito-borne diseases, particlulalry those transmitted by Culex quincquefasciatus pose a significant public health challenge. Traditional methods of eradication using synthetic insecticides pose environmental concerns and a risk of developing insecticide-resistant varieties. Here, the use of plant-based biopesticides offers a safer and sustainable alternative. The study aimed to investigate the insecticidal properties of Flemingia wightiana (FW) leaves by synthesising leaf extracts and silver nano-particles. The toxicity of the test samples was tested on Oreochromis niloticus at concentrations of 0.1, 0.5, and 1 mg/L. Furthermore, the test samples were subjected to lethality assay on C. quinquefasciatus. Laboratory bioassays were conducted to evaluate the efficacy of crude extract and silver nanoparticles of F. wightiana at varying concentrations, specifically 0.5, 1, 2, and 4 mg/L. Ovicidal, emergency and larvicidal activity were studied. The results indicated significant larvicidal activity and exhibited better potential for toxicity against Culex larvae treated with AgNPs. FW-AgNPs have substantial effect in delaying the hatching of mosquito eggs. Moulting of larvae from one instar to the next was also delayed by treatment with AgNPs. The findings demonstrated that FW-AgNPs play a significant role in controlling C. quinquefasciatus populations. : Author (s). Publishing rights and ANSF. -
A Systematic Review on Traffic Management System and Security Flaws: Analysis Research
The number of cars on our roads has significantly increased in recent years, surpassing the advancement of our traffic and road infrastructure. Due to the ineffective traffic management caused by this imbalance, there have been noticeable increases in traffic jams, congestion, and pollution. Addressing the problem of growing traffic has become urgent on a global scale. By using cutting-edge technology, Intelligent Transportation Systems (ITSs) provide a viable solution for addressing these issues. This research explores the development of traffic control systems as well as the cutting-edge innovations that have revolutionized this field. Furthermore, it examines various techniques based on traffic signals, primarily focusing on scrutinizing their security vulnerabilities and the measures taken to enhance system performance. The review acknowledges significant strides made in implementing security measures, assessing their effectiveness through both qualitative and quantitative metrics. Additionally, this study delves into key discoveries and explores the rationale behind lessons learned, serving as a roadmap for future research endeavors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Womens perceived social support through self-help group: case study of Kudumbashree from capital district of Kerala
This study examines Kudumbashree, a womens self-help group (SHG) with a strong emphasis on microcredit, in Thiruvananthapuram, the capital of Kerala state in India. It is a valuable program because it provides access to credit and savings mechanisms, fosters a sense of agency, decision-making power, and social support among women in these areas. The study explores the demographic characteristics of members, their involvement in SHGs, and compares perceived social support levels across different demographic groups. The study uses a cross-sectional survey of 502 women using an extensive demographic questionnaire and Multidimensional Scale of Perceived Social Support (MSPSS) questionnaire. The study participants were mostly older, married women who were primarily housewives from both nuclear and joint families, predominantly in rural areas. Many women had been SHG members for over a decade and actively participated in meetings and decision-making. The study revealed that women with lower education levels, rural and those saving more than 300 INR monthly perceived higher social support from family and overall. Considering that women in rural India averaged around 250 INR in daily earnings from casual labor during 2022, the fact that a substantial number of participants saved more than 300 INR monthly highlights the significance of their savings. The study contributes to the understanding of Kudumbashree SHGs and emphasizes their role in providing social support, financial empowerment, and a sense of community. The study also highlights the need for ongoing support and capacity building to further enhance their impact. 2025 Taylor & Francis Group, LLC. -
Posture Classification Using a Hybrid Deep Learning Model
Automated posture detection is a critical task in ergonomics and healthcare, yet it presents significant challenges for standard computer vision models, particularly in handling class imbalance and understanding geometric constraints. This paper proposes an enhanced hybrid deep learning model that synergizes the feature extraction power of a pre-trained ResNet50 architecture with engineered geometric features derived from the Radon Transform and pre-calculated joint angles. Our approach utilizes a dual-balancing strategy, combining data upsampling with a custom weighted loss function, to effectively address the problem of underrepresented classes. By processing visual and geometric data streams in parallel and fusing them within a deep architecture, our model achieves a holistic understanding of the subject's posture. The fine-tuned model demonstrates strong performance on an unseen test set, achieving a final accuracy of 92% for wrist posture and 92% for neck posture. Crucially, it attains a robust F1-score of 0.74 for the challenging 'Bad Wrist Posture' minority class, a significant improvement compared to the ResNet50-only baseline (F1=0.24) and achieves excellent ROC-AUC scores of 0.9859 for wrist and 0.9838 for neck, proving the efficacy of our hybrid, dual-balancing methodology for realworld application. 2026 IEEE. -
Comparative study of benchmarking models for higher education institutions
Benchmarking is a systematic and ongoing process of assessing an organisations business processes against those of business process leaders to obtain data that will enable the firm to take corrective action to enhance performance (Pattison, 1993). Eight benchmarking models, namely the European Foundation for Quality Management (EFQM) excellence model, American Productivity and Quality Centre (APQC) consortium framework, Commonwealth Higher Education Management Service (CHEMS) model, Mckinnon model, Henderson-Smart et al. model, educational development efficiency (EDE) model, Tee benchmarking model, and fourth generation balanced scorecard method are being studied, analysed, evaluated and compared. While most models effectiveness depends on the cooperation and participation of benchmarking partners, few depends on secondary data are an exception. Most benchmarking models lack the implementation and are fluid and flexible models. This comparative benchmarking study helps an institution understand which benchmarking model needs to be used, as the study details each models essential features, advantages, and limitations. Copyright 2025 Inderscience Enterprises Ltd. -
Thematic Analysis of Quality Assurance Tools in Higher Education Institutions
Quality assurance tools are crucial in maintaining and improving higher education institutions standards, credibility and effectiveness. They ensure standardisation, institutional credibility, competitiveness, academic rigour and research quality, enhancing the teaching and learning experience. This article aims to qualitatively analyse various quality measurement tools, such as accreditation, ranking, peer review and student-led evaluation, used in the context of higher education institutions. Various qualitative analysis tools such as content analysis, thematic analysis and grounded theory approach were analysed first. Out of this, thematic analysis was selected for the qualitative analysis as the data and analysis are from literature and expert opinion, which lack primary data. The codes for the thematic analysis were taken from previous literature. The codes have operational definitions and are categorised into Input, Process and Output themes. Code values were assigned for the quality assurance tools for each code. The code count for the various themes helped to identify the usability of various tools. The study was based on Ludwigs theme generated from the Input, Process and Output system theory. The study found that accreditation is the best tool for qualitatively and quantitatively analysing an institution. It can be an ideal tool for quality assurance for an institution. Peer review, ranking and student-led evaluation ranked next in the quality assurance tool. This study aids organisations, governmental agencies and other educational institution stakeholders to select the best method for comparing institutions during admissions, grants processing and other processes. The study adopted the qualitative analysis tool thematic analysis. Hence, the study has subjective bias and is not free from the limitation of thematic analysis. The modelling approach is directed towards prediction rather than casualty. The study has taken the literature reviews and expert opinion into account. However, structured questionnaires and unstructured interviews helped better categorise the various codes into the respective themes. The statistical analysis would have made the observation more robust. This article fulfils an identified need to analyse the ideal quality assurance tool for quality assurance and measurement for institutions. This helps institutions meet national and international accreditation standards, policy formulation, strategic planning, continuous improvement, faculty workload distribution, accountability and boosts institutional reputation. 2026 Unisa Press. -
A Study on the Effect of Food Advertisements on Children and their influence on Parents Buying Decision
International Journal of Research in Commerce and Management Vol. 3, No. 7, pp 92-104, ISSN No. 0976-2183 -
A Study on the Role of Tea Tourism in Assam
Tourism Development Journal Vol. 10, Issue 1, pp. 1-14, ISSN No. 0975-7376 -
AI in creating inclusive work environments for neurodiverse employees
Purpose This study aims to examine the increased focus on neurodiversity in contemporary businesses. It shows how inclusive policies can capitalize on the special abilities of people with neurodiverse backgrounds, including their extraordinary problem-solving abilities, meticulous attention to detail and creative thinking. These policies benefit the individuals and contribute to a more diverse and innovative workplace. Design/methodology/approach Data was collected through semistructured interviews with HR experts and neurodivergent employees. The qualitative data were manually analyzed and coded, and themes were identified. Findings The results highlight the significant benefits of accepting neurodiversity in the workplace, enlightening the audience about its potential. For instance, artificial intelligence (AI) can be used to anonymize resumes, removing potential biases related to gender, ethnicity or age. In addition, AI can help in identifying the unique skills and strengths of neurodivergent employees, enhancing the fit between job responsibilities and their abilities. This study also emphasizes the wider effects of accepting neurodiversity on employee satisfaction, productivity and organizational innovation. This study promotes a deep learning framework that combines human-centered strategy with strategic methods to maximize the participation of neurodiverse workers and foster a more creative and dynamic corporate culture, convincing the audience of its benefits. Research limitations/implications This study is limited by its qualitative nature and relatively small sample size, comprising 15 HR professionals and 20 neurodivergent employees, which restricts generalizability. The sensitive nature of neurodiversity also made participant recruitment challenging, with some individuals hesitant to disclose their condition. In addition, companies were reluctant to share internal AI practices due to confidentiality concerns. The research focused on a select set of organizations, primarily from specific regions, limiting cross-cultural applicability. Furthermore, the absence of AI developers in the sample means insights into technical tool design and implementation remain unexplored, suggesting a gap for future multidisciplinary research. Practical implications This study provides actionable insights for HR professionals and organizational leaders aiming to improve neurodiverse hiring and support systems. It identifies specific AI tools such as Grammarly, Otter.ai and Pymetrics, that can be integrated into recruitment and workplace settings to enhance communication, reduce sensory overload and match roles to individual strengths. Organizations can use the deep learning framework proposed to design more inclusive policies and infrastructure. Training managers and customizing AI-driven accommodations can improve retention, engagement and performance among neurodiverse talent. This research supports firms in developing more equitable, adaptive and innovative environments aligned with diversity and inclusion goals. Social implications This study promotes a societal shift in how neurodivergent individuals are perceived and supported in the workforce. By emphasizing ability over deficit and proposing inclusive AI integration, it helps reduce stigma and encourages broader acceptance of cognitive diversity. The findings advocate for universal accommodations that do not require self-disclosure, promoting dignity and equity. Improved employment outcomes for neurodiverse individuals contribute to economic inclusion, reduce unemployment rates and challenge ableist norms. The research also aligns with broader Diversity Equity and Inclusion (DEI) movements, inspiring organizations and policymakers to build socially responsible frameworks that reflect the value of every individual, regardless of neurological difference. Originality/value This paper offers original value by exploring the underresearched intersection of AI and neurodiversity inclusion in the workplace. It contributes novel insights through qualitative analysis of HR professionals and neurodivergent employees, highlighting the role of AI in reducing hiring bias, customizing work environments and enhancing employee well-being. By proposing a deep learning framework and cataloging AI tools matched to neurodiverse conditions, this study bridges theory and practice. It uniquely positions AI as both a technological and ethical enabler for inclusive employment, making it highly relevant for scholars, practitioners and policymakers aiming to foster equitable, future-ready workplaces. 2025 Emerald Publishing Limited -
Innovative Hybrid Models for Predicting Diabetes: CNN-LSTM Hybrid and Calibrated Soft Voting Model
This study assesses four ensemble techniques - stacking, soft voting, hard voting, and calibrated soft voting - for predicting diabetes onset using the Pima Indians Diabetes dataset. Traditional single-model methods are contrasted with these advanced ensemble approaches, which integrate multiple models to enhance predictive accuracy. The evaluation included metrics such as accuracy, precision, recall, F1 score, and AUC. The CNN-LSTM model was also examined, achieving an accuracy of 75%, precision of 70%, recall of 69%, and an F1 score of 72%. Among the suggested methods, the calibrated soft vote model was the most effective, with improved performance compared to the rest of the techniques. Upcoming studies will address the combination of these models with real-time monitoring systems and deploying their use across a broad range of datasets and medical conditions. 2025 IEEE.