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TD?DNN: A Time Decay?Based Deep Neural Network for Recommendation System
In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Further-more, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD?DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weight-age to the recent ratings. This non?noisy weighted matrix is fed into the Deep Learning model, con-sisting of an input layer, a Multi?Layer Perceptron, and an output layer to generate predicted rat-ings. The models performance is tested on three benchmark datasets, and experimental results con-firm that TD?DNN outperforms other existing approaches. 2022 by the authors. Li-censee MDPI, Basel, Switzerland. -
A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System
Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user's ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers-Jaccard-Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that. 2014 IEEE. -
Clustering-Based Recommendation System for Preliminary Disease Detection
The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patients symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%. 2022 IGI Global. All rights reserved. -
Delayed in sensorimotor reflex ontogeny, slow physical growth, and impairments in behaviour as well as dopaminergic neuronal death in mice offspring following prenatally rotenone administration
The environment is varying day by day with the introduction of chemicals such as pesticides, most of which have not been effectively studied for their influence on a susceptible group of population involving infants and pregnant females. Rotenone is an organic pesticide used to prepare Parkinson's disease models. A lot of literature is available on the toxicity of rotenone on the adult brain, but to the best of our knowledge, effect of rotenone on prenatally exposed mice has never been investigated yet. Therefore, the recent work aims to evaluate the toxic effect of rotenone on mice, exposed prenatally. We exposed female mice to rotenone at the dose of 5mg/Kg b.w. throughout the gestational period with oral gavage. We then investigated the effects of rotenone on neonate's central nervous systems as well as on postnatal day (PD) 35 offspring. In the rotenone group, we observed slow physical growth, delays in physical milestones and sensorimotor reflex in neonates and induction of anxiety and impairment in cognitive performances of offspring at PD-35. Additionally, immunohistochemical analysis revealed a marked reduction in TH-positive neurons in substantia nigra. Histological examination of the cerebellum revealed a decrease in Purkinje neurons in the rotenone exposed group as compared to the control. The data from the study showed that prenatally exposure to rotenone affects growth, physical milestones, neuronal population and behaviour of mice when indirectly exposed to the offspring through their mother. This study could provide a great contribution to researchers to find out the molecular mechanism and participating signalling pathway behind these outcomes. 2023 International Society for Developmental Neuroscience. -
A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks
The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses. 2013 IEEE. -
Balance of payment crisis in India: What the figure say
Volume 2, Issue 5, September-October 2013 -
Environmental cost of food wastage: Integrated response through a mix of environmental policy instruments
Food, when wasted, reaches landfills and emits greenhouse gases. The impact of greenhouse gases (GHGs), in turn, is felt by even those who do not waste food in the place. Externalities thus created are known to distort market efficiency and the most widely discussed externality is climate change. This study takes the case of United States of America (USA) to ascertain the GHGs resulting due to food wastage. The difference between cost per capita due to emissions from animal-based products and emissions from plant-based products comes out to be $122. In the year 1997 total GHG emission for the entire population of the USA due to food wastage was 401.98 billion kgCO2eq, costing the country 45.42 billion US dollars. Two decades later, in 2017, the food waste costs went up by 6 billion US dollars amounting to 51.14 billion US dollars and 452.64 billion kgCO2eq of GHG emissions The novelty of this research lies in highlighting the carbon footprints of food wastage in terms of GHG's and monetizing these emissions. The study proposes an integrated response through a mix of environmental policy instruments of economic incentives, command and control and moral suasion. 2023 ERP Environment and John Wiley & Sons Ltd. -
Characterization of Line-Cut Signed Graphs
A signed graphS= (Su, ?) consists of an underlying graph Su and a function ?: E(Su) ? { + , - }. For a graph G, its lict graph written as Lc(G) is the intersection graph ?(E(G) ? C(G)). For a signed graph S, its line-cutsigned graph (in short called lict signed graph) is denoted by Lc(S) has underlying graph Lc(Su) and two vertices u and v joint by negative edge if u and v both are negative adjacent edges of S or u is a negative edge incident to a cut-vertex v of negative degree odd in S and by positive edge otherwise, here C(S) is the set of cut-vertices of S. In this paper, we establish structural characterization of lict signed graphs Lc(S). 2020, The National Academy of Sciences, India. -
Linking Metacognition, Workplace Cognitive Competencies and Performance: An Integrative Review-Based Conceptual Framework
A key driver of workplace cognitive competencies is metacognition which has been shown to impact performance among nurses, teachers and firefighters, however, it is scarcely studied among managerial employees. The research investigating this relationship is also scattered across multiple domains limiting its utility for researchers and practitioners. This paper, therefore, presents an integrative review of the existing empirical literature from the Web of Science and Scopus database to trace the linkages of metacognition, workplace cognitive competencies and performance at work. The identified linkages are then formulated into a conceptual framework clarifying how various workplace cognitive competencies and performance may be linked to metacognition. The findings indicate linkages between metacognition and various workplace cognitive competencies such as problem-solving, decision-making, innovation, creativity and knowledge acquisition. The present research also establishes the link of metacognition and cognitive competencies with learning, individual and firm performance. The review paves way for metacognition to be considered as a distinct construct in the workplace, identifies gaps and provides direction for future research. Copyright 2022 The Author(s). -
A Systematic Review of Green Apparel Manufacturing
The purpose of this paper is to conduct a systematic review of the literature on green manufacturing practices in the apparel industry to map green practices across various apparel manufacturing departments. The review includes academic journal articles that were retrieved between March 2013 and March 2023 from several different databases. As part of a comprehensive literature assessment, content analysis was applied to 138 publications that were published in peer-reviewed journals over ten years. Green practices in garment manufacturing process are covered, including product design, raw material procurement, fabric spreading, cutting, sewing and assembly, washing, printing and embroidery, finishing, and packing. The review of eco-friendly production practices at each phase of the production process shows the variety and complexity of green practices in apparel production companies. However, there is a lack of research on the conditions of developing countries, where the majority of apparel production takes place, as well as on the methods used in the manufacture of garments. The study is distinct in that it focuses solely on the garment manufacturing industry, and will not include textiles because the production processes for textiles and clothing are fundamentally different. This study assists managers in building a companys sustainability competency by outlining best practices at various phases of production. It also provides scholars with a uniform representation of environmentally sustainable practices to spur additional scholarly investigation. 2023, Kauno Technologijos Universitetas. All rights reserved. -
Green Manufacturing and Performances in Apparel Export Industry: Mediating Role of Green Innovation
Implementing green manufacturing in the production process of garment export-oriented enterprises not only contributes to environmental well-being but also enhances competitiveness in the global market and boosts overall company performance. Furthermore, it is critical to take immediate action to improve sustainable production levels via green innovation. This study aims to investigate the impact of green manufacturing on operational performance, environmental performance, and economic performance in the apparel manufacturing export industry. The study also investigates the mediating role of green innovation as a mediator between the green manufacturing and types of performances. A survey method is used to collect data from 123 garment export units, and structural equation modelling (SEM) is used to analyze the results. This study delivers empirical evidence of the impact of the imple-mentation of green manufacturing on green innovation and economic performance. Moreover, green innovation has a significant impact on economic performance and environmental performance. The results signify that green innovation partially mediates green manufacturing and economic performance. Green innovation fully mediates green manufacturing and environmental performance, and also green manufacturing and operational performance. This study is unique in its focus on the use of green manufacturing and green innovation in garment export production units. It provides insights into how these green practices might improve firm performance and the environment. 2024, Kauno Technologijos Universitetas. All rights reserved. -
Globalisation, Privacy, and Data Protection A Glimpse of Changing Contours from a Human Rights Perspective
Though the union government made efforts to protect the data of individuals with the introduction of the Personal Data Protection Bill, 2019 and subsequently with the enactment of the Digital Personal Data Protection Bill, 2023, there are various lacunae which still need to be resolved. Against this background, the right to privacy and data protection in a globalised world is discussed, along with the need to bring about reforms for the effective implementation and protection of the same. 2023 Economic and Political Weekly. All rights reserved. -
Future Technology and Labour - Are we Heading Towards a Jobless Future?
Technological innovations and the invention of machines powered by Artificial intelligence2have changed the way we work, interact and carry on our everyday lives. Automation wave has revolutionized the manner in which the traditional manufacturing and service-oriented industries are functioning today. The first industrial revolution was triggered with the invention of steam engine and also led to mechanical production. The invention of electricity and assembly lines resulted in the second industrial revolution where mass production became feasible. The third industrial revolution was driven by computer, digital technology and the internet. The future technologies have resulted in the fourth industrial revolution. The new age technological innovations and inventions such as the automated robots; big data and analytics; augmented reality; the cloud; cyber security; additive manufacturing; horizontal and vertical integration; the internet of things are transforming industrial production and labour relations. There is a drastic improvement in the entire chain of production ranging from design up to productivity, the speed and the quality at which the goods are produced. As a result of the new age technologies various concerns are raised especially its impact on the employment. Many labourers are rendered unemployed and redundant due to automation. The question that arises is whether we are approaching a jobless future?? The job market in India is also undergoing a transformation and posing many social, economic, legal and ethical challenges. Job structure is changing and the workers need to equip themselves with new skills to fit into the new jobs that are emerging as a result of technological innovation. The education system in any country plays a pivotal role in the overall development of an economy as it caters to the needs of the trained and skilled manpower. It is vital for the education system in the country to re-orient itself to cater to the needs of the students to fit into the changing paradigm. The focus of the education needs to be on imparting life-skills and to improve the thinking, problem-solving and decision-making ability of the individuals in a society. In the light of the above, it is also important to address and discuss the various changes, issues and challenges that are taking place in the labour market including the impact of these technologies on the working hours, wages, the working environment and the labour relations amongst others. 2019, Department of Law, University of North Bengal. All rights reserved. -
Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024. -
A multi-preference integrated algorithm for deep learning based recommender framework
Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short-term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi-preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1-score. Consequently, the MPIA can be integrated with real time recommender systems. 2022 John Wiley & Sons, Ltd. -
Blockchain for Securing Healthcare Data Using Squirrel Search Optimization Algorithm
The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situa-tion, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transac-tion size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies. 2022, Tech Science Press. All rights reserved. -
Effects of a Mindfulness-based Intervention on Well-being Among Rural Adolescents with Academic Anxiety
Background: Academic anxiety revolves around scholastic work and performance and can be detrimental to students health and overall subjective well-being. It has been found to be significantly high in adolescents, leading to consequences that prove to be detrimental to their academic performance, focus, and overall self-esteem. This phenomenon acts as a vicious cycle impacting all aspects of a students life. Method: The current study aimed to explore mindfulness-based intervention (MBI) as a possible option to deal with academic anxiety in rural adolescent students and improve their overall subjective well-being. A total of 600 students were screened for academic anxiety and a total of 47 students were subjected to an eight-week MBI. MBI aims to bring more present-moment awareness and cultivate overall well-being and thereby works against anxiety. Mixed repeated measures ANOVA was carried out to compare pre, post, and follow-up scores. Result: The results indicated a significant effect of MBI on adolescents, suggesting a significant decline in academic anxiety from pre-to-post and an increase in mindfulness and subjective well-being from pre-to-post and follow-up assessments. Conclusion: Academic anxiety and subjective well-being improved significantly with the MBI intervention, thereby implication that MBI is a feasible option for rural adolescents with academic anxiety. 2024 The Author(s). -
Smart Metering System with Google Assistant
This paper presents a unique research problem in the area of automation system by using IoT. The mentioned approach utilizes Google assistant, which is incorporated within Google home which uses voice-controlled inputs and voice feedbacks. This paper discusses a new method to develop a smart energy meter at a distributor level and to make use of this technology to monitor the power consumption of each device individually which can help the user to monitor the electricity usage in real time and thus helps to save electricity and reduce cost on your electricity bill. 2020, Asian Research Association. All rights reserved. -
One Pot Hydrothermal Synthesis and Application of Bright-yellow-emissive Carbon Quantum Dots in Hg2+ Detection
Carbon quantum dots (CQD) have drawn great interest worldwide for their extensive application as sensors due to their extraordinary physical and chemical characteristics, good biocompatibility, and high fluorescence in nature. Here, we demonstrate a technique for detecting mercury (Hg2+) ion using a fluorescent CQD probe. Ecology is concerned about the accumulation of heavy metal ions in water samples due to their harmful effects on human health. Sensitive identification and removal of metal ions from water samples are required to reduce heavy metals risk. To find out Mercury in the water sample, carbon quantum dots were used and synthesized by 5-dimethyl amino methyl furfuryl alcohol and o-phenylene diamine through the hydrothermal technique. The synthesized CQD shows yellow emission when exposed to UV irradiation. Mercury ion was used to quench carbon quantum dots, and it was found that the detection limit was 5.2 nM with a linear range of 15100 M. The synthesized carbon quantum dots were demonstrated to efficiently detect Mercury ions in real water samples. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Artemisia stelleriana-mediated ZnO nanoparticles for textile dye treatment: a green and sustainable approach
Textile effluents being one of the major reasons for water pollution raises major concern for water bodies and the habitation surrounding them. The lack of biologically safer treatment solutions creates a major concern for the disposal of these effluents. The present study focuses on the degradation of textile dyes using leaf extract of Artemisia stelleriana-assisted nanoparticles of zinc oxide (ZnO-NPs). ZnO NPs synthesized were confirmed using spectroscopic, X-ray diffraction and microscopic analysis. The current research utilizes widely used major textile dyes, Reactive Yellow-145 (RY-145), Reactive Red-120 (RR-120), Reactive Blue-220 (RB-220) and Reactive Blue-222A (RB-222A), which are released accidentally or due to the non-availability of cost-effi-cient, dependable and environment-friendly degradation methods, making this work a much-needed one for preventing the discharge before treatment. The biosynthesized ZnO-NPs were top-notch catalysts for the reduction of these dyes, which is witnessed by a gradual decrease in absorbance maximum values. After 320 min, ZnO-NPs under UV light exposure showed 99, 95, 94 and 45% degradations of RY-145, RR-120, RB-220 and RB-222A dyes, respectively. The phytotoxicity study conducted at two trophic levels revealed that the A. stelleriana-mediated ZnO-NPs have great potential for the degradation of textile dyes, allowing them to be scaled up to large-scale treatments. 2023 The Authors.