Browse Items (11807 total)
Sort by:
-
The Belt and Road Initiative: Issues and Future Trends
The Belt and Road Initiative (BRI) is a China-led plan that involves infrastructure and construction projects in more than 140 countries, out of which 65 countries account for 30% of the worlds gross domestic product, 35% of the worlds trade, 39% of the global land, 64% of the worlds population, 54% of the worlds CO2 emissions and 50% of the worlds energy consumption (Du & Zhang, 2018, China Economic Review, 47, 189205). The project announced in 2013 is often considered Chinese Premier Xi Jinpings dream. It quickly grew in sectoral and geographical complexity from the Arctic to deep oceans, to Latin American countries, Africa and even collaborations in maritime and outer space. Nine years into the making, the project suffered disruption in the wake of the COVID-19 pandemic. Travel restrictions and lockdowns led to suspension and slowdown in the project. However, the Chinese leadership continues to remain optimistic regarding the BRI and is opting for digital, health and sustainability models to keep the initiative running. The article analyses the strategic and economic significance of the BRI from its inception to now. It focuses on the impact of the pandemic on the BRI and stakeholders responses to the project, and looks into attempts by China to make it a success in the post-pandemic world. 2023 Indian Council of World Affairs(ICWA). -
Nature of music engagement and its relation to resilient coping, optimism and fear of COVID-19
The COVID-19 pandemic has resulted in unprecedented lockdowns, a work from home culture, social distancing and other measures which badly affected the world populace.Individuals over the globe reported experiencing several psychosocial and psychosomatic problems.Nevertheless, this pandemic allowed us to be with ourselves, to understand the importance of healthy lifestyles and to devote time to our passions and hobbies when we were socially isolated.Against this background, the present study was undertaken to explore the nature of peoples everyday musical engagement and to examine how the experience and functions of music were related to resilient coping, life orientation and fear from COVID-19.In an online survey, a total of 197 participants responded to a questionnaire designed to assess the nature of musical engagement (level of musical training, functional niche of music, listening habits and involvement in musical activities), functions of music (FMS), resilient coping (BRCS), life orientation (LOT-R), and fear of COVID-19 (FCV-19S).Results indicate that for most of the respondents, music listening was a preferred activity during the pandemic which resulted in positive effects on their mood, heart rate and respiratory rates.More than 80 per cent of respondents reported music as a source of pleasure and enjoyment and claimed that it helped to calm them, release their stress, and help them relax.Significant positive correlations were found between the functions of music (memory-based and mood-based), optimism and resilient coping and mood-based functions of music and optimism were found to predict resilient coping among individuals.These results suggest that meaningful and active music engagement may lead to optimism which may result in effective resilient coping during the crisis.Moreover, reflecting upon our everyday musical engagements can promote music as a coping skill. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors. -
Yulu: Moving Towards a Sustainable Future
The rapidly rising rate of urbanization, which is closely linked to economic growth, has exposed the world to several challenges such as inequality, environmental degradation, traffic congestion, infrastructural concerns and social conflicts. Therefore, urban sustainability has emerged as one of the most debatable discussions across the world. The existing network of transportation can no longer keep up with the growing demand in metropolitan cities. Short distance travel has become an unresolved issue for daily commuters. The case presents how MMVs have emerged as an alternative mode of transport for resolving issues of daily commuters regarding the first-mile connectivity, last-mile connectivity and short distance travel to reach their final destination. MMVs are basically light-weight vehicles which occupy less space on road. These vehicles include bicycles, e-bikes, skateboards, hoverboards and other battery-operated vehicles. The case narrates the journey of Yulu, a dockless bike-sharing venture which promoted the concept of green consumerism among the daily commuters at affordable rates. The venture initially started in the IT city of Bangalore and later expanded its operations to other cities such as Pune, Navi Mumbai, Gurugram and Bhubaneswar. The speciality of this venture is that it offers a sustainable solution to ever-increasing problems of traffic congestion and aggravating air pollution issues in metropolitan cities. Dilemma: How to offer a sustainable solution to the ever-increasing problem of traffic congestion and aggravating air pollution due to rising vehicular traffic? How to make short distance travel affordable and more convenient for daily commuters? Theory: Three pillars of sustainable development. Type of Case: Problem solving applied case. Protagonist: Present. Discussion and Case Questions: What strategies should be employed by the start-up to make it a more popular form of commute? How can the increasing rate of damage to the vehicles be brought down? How does organization structure and cluster management practices of Yulu help it to become more sustainable? How can the regulatory bodies and government promote and adopt such start-ups in their urban planning projects? 2020 SAGE Publications. -
Robo-revolution: How automated financial advisors are reshaping global finance
Robo-advisors have the potential to revolutionise the financial service industry by making it more accessible and affordable. This study provides a comprehensive overview of robo-advisors in the arena of financial markets and investments and their gaining popularity in the fintech industry, particularly in emerging markets like India. It also discusses the changing landscape of the financial sector in India, benefits and challenges of fintech, and the legal and ethical implications of roboadvisors. The current study presents a comparative study between India and UK markets in terms of acceptance and penetration of robo-advisors. It highlights leading robo-advisory firms in India. The data visualisation is done with the help of Microsoft Power BI and Microsoft Excel on the statista survey data. The expected results of this study assist several stakeholders, such as academicians, researchers, investors, stock brokers, regulators, and policy makers. 2024, IGI Global. All rights reserved. -
Deep neural network architecture and applications in healthcare
Gaining insights related to medical data has always been a challenge, as limited technology delays treatment. Various types of data are collected from the medical field, such as sensor data, that are heterogeneous in nature. All of these are very poorly maintained and require more structuring. For this reason, deep learning is becoming more and more popular in this area. There are many challenges due to inadequate and irrelevant data. Insufficient domain knowledge also adds to the challenge. Modern deep learning models can help understand the dataset. This chapter provides an overview of deep learning, its various architectures, and convolutional neural networks. It also highlights how deep learning technologies can help advance healthcare. 2022 River Publishers. -
SarNet-1 -A Novel Architecture for Diagnosing Covid-19 Pneumonia and Pneumonia through Chest X-Ray Images
Coronavirus (COVID-19) is a contagious disease which begins with flu-like symptoms. COVID-19 arose in China and it rapidly spread throughout the globe, leading to a pandemic. For many, it was noticed that the infection started with fever, cough and finally leading to pneumonia. It is very necessary to differentiate between covid pneumonia and general pneumonia for appropriate treatment. Chest X-ray readings are useful for radiologists to identify the severity of infection. While computerising this mechanism, deep learning techniques are found to be very useful in extracting relevant features from medical images. This can help in differentiating pneumonia, COVID19 pneumonia and x-rays of a healthy person. Computer aided methods for identifying the presence of pneumonia can help health providers to a great extent for quick diagnosis. The X-rays gathered from freely available datasets are used in this work to propose an architecture for categorising X-rays into pneumonia and covid pneumonia. 2022 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved. -
Effect of VR Technological Development in the Age of AI on Business Human Resource Management
Human resource management (HRM) strategies are increasingly using AI and other AI-based technologies for managing employees in both local and foreign enterprises. An exciting new field of study has emerged in the last decade on topics like the media interaction of AI and robotics, the possessions of AI acceptance on independence and consequences, and the evaluation of AI-enabled HRM practices due to the proliferation of AI-based implementations in the HRM function. The use of these technologies has influenced the way work is organized in both domestic and global corporations, presenting new possibilities for better resource management, faster decision-making, and more creative issue resolution. Research on AI-based solutions for HRM is scarce and dispersed, despite a growing interest in academia. Human resource management (HRM) roles and human-AI interactions in major multinational corporations disseminating such advances need more study. As computing and networking infrastructure has advanced rapidly, so has the era of artificial intelligence. Now that in the age of AI, virtual reality technology has found many applications beyond gaming. Human resource management has emerged as a hot topic, with interest coming from both large businesses and government agencies. Many studies have been conducted on HRM in the business world, but in order to stay up with the trends, HRM must be constantly updated. This article does a demand analysis, and sets up and tests a fully-featured VR business human resource management system, all against the backdrop of the age of artificial intelligence and the present popularity of VR technology. 2023 IEEE. -
Automation using Artificial Intelligence in Business Landscape
The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks. 2023 IEEE. -
Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles
Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection for Privacy Protection in Social Networks
Malicious activity detection is a vital component of ensuring privacy protection in social media networks. As users engage in online interactions, protecting their sensitive information becomes paramount. Social networks can proactively identify and mitigate malicious behaviors, such as cyberbullying, data breaches, and phishing attacks by applying advanced AI and machine learning (ML) technologies. This detection system analyzes user behavior patterns, content, and network traffic to flag suspicious activities, thus safeguarding user privacy and fostering a safer online environment. The incorporation of robust malicious activity detection mechanisms helps maintain trust in social networks and reinforces the commitment to preserving user privacy in an increasingly interconnected digital landscape. This article introduces a novel Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection (MRFODLMAD) technique for privacy protection in social networks. The drive of the MRFODL-MAD technique is to detect and classify malicious activities in the social network. To accomplish this, the MRFODL-MAD technique preprocesses the input data. For malicious activity detection, the MRFODL-MAD technique employs long short term memory (LSTM) system. The MRFO algorithm has been executed to hyperparameter tuning process to improve the performance of the LSTM network. The experimental outcomes of the MRFODL-MAD algorithm can be tested on social networking database and the results inferred the improved performance of the MRFODL-MAD algorithm under various different measures. 2023 IEEE. -
Scientific basis for the preparation and characterization of iron based traditional drug annabhedi sindooram: A materialistic approach
Iron based traditional Ayurvedic drug Annabhedi Sindooram is used therapeutically for the treatment of diseases like Anaemia, Leucoderma, Prolapse of rectum and uterus, Spleenic disorders. The preparation method of iron based Indian traditional drug Annabhedi Sindooram involves conversion of a pure metal into its mixed oxide by drying and incineration. Commercially available ferrous sulphate is used as the source of iron for the preparation of Annabhedi. The structural and textural properties of the starting materials and the prepared drug were characterized systematically by different characterization techniques like PXRD, Zeta Potential Analysis, particle analysis, FTIR, ICP -AES, SEM and BET surface area analysis. The results obtained by characterization of the samples clearly explain the formation of Fe2O3, reduction in particle size, modification of surface energy and formation of metal complex with organic moieties. The strict post and pre preparation conditions followed play an important role in the morphology and medicinal activity of the drug Annabhedi Sindooram. -
-
Impact of Station Rotation Model in Enhancing Writing Skills and Academic Performance of Primary School Children
The core pursuit of the research is to evaluate the efficacy of technology-integrated English language instruction using one of the blended learning approaches. The intrusion of technology in education has increased rapidly ever since COVID-19, which altered the spheres of learning and teaching. The cross-section of education and technology has become higher and more robust than before because of the pandemic, which led to the exploration of varied dimensions in technologyintegrated teaching. The field of English language teaching has also undergone a significant transformation due to the advent of technology. The introduction of interactive multimedia tools, online platforms, and language learning applications has enabled educators to engage students newlinein more effective ways. Virtual classrooms have brought about a global connection, breaking geographical barriers and fostering cross-cultural communication. The use of adaptive learning systems and AI-driven newlinelanguage applications has personalised learning experiences, catering to individual needs and pace. Furthermore, technology has provided immersive experiences through virtual and augmented reality, which enhance language acquisition by providing real-life contexts for practice. Thus, technology has diversified teaching approaches and made English language learning more accessible, interactive, and tailored to the needs newlineof diverse learners. However, learners from socio-economically disadvantaged backgrounds are deprived of these advantages that could newlinehelp in improving their English language proficiency. The aim of the study is to evaluate the efficacy of technologybased English language instruction that integrates one of the blended learning approaches for improving the writing skills of primary school students from socio-economically deprived backgrounds who have little newlineor no exposure to English language learning outside of their classrooms. -
Bridging Service Quality Gaps
Indian Streams Research Journal, Vol-2 (10), pp. 38-42. ISSN-2230-7850 -
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
COLPOUSIT: A Hybrid Model for Tourist Place Recommendation based on Machine Learning Algorithms
Tourism is an important sector for a country's economic growth. The travel recommendations should be made focused on better growth and attract more travelers. There is a huge amount of travel information and ideas available on the web that allows the users to make poor travel decisions. This paper focuses on building a hybrid travel recommender system by implementing collaborative-based, popularity-based, and nearby place weighted recommender system. The proposed system recommends the travel spots to the users based upon their interests and other criteria specified. In order to implement these methods, we applied a comparative study on different machine learning algorithms for collaborative-based approach and have performed weighted hybridization. These methods provide a personalized and customized list of similar places with respect to places of interest to the users. Thus, a hybrid system built using these methods provides a better recommendation of places with the advantages of these methods. The obtained results confirm that the hybrid method better than other recommender approaches when used separately. 2021 IEEE. -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data
Autism is a heterogeneous neuro developmental disorder found among all age groups. Nowadays more patients are detected with autism but very less awareness is prevailing in the society related to it. This paved a way for many researchers to carry out serious study on autism and its characteristics. Studying behavior and characteristics of Autistic patients is very important for diagnosing the level of autism. Classifying the association of different characteristic in autistic patients at gene level using machine learning techniques can give an important insight to the doctors and the care takers of the patients. Research is being carried out to identify the genes responsible for autism. The changes in gene sequence may lead to different characteristics in different people. Thus genotypic research is found to reveal well defined insight about various characteristics in autistic patients and their associations with genes. Single Nucleotide Polymorphism (SNP) being high in features indicate human genome variability and is associated with identification of traits for many human diseases including autism. The main aim of the proposed work is to identify SNP sequences which are responsible for carrying the autistic traits. This paper explore the application of Constraint Governed Association Rule Mining (CGARM) technique on SNP data for dimensionality reduction and thereby selecting the strong predominant SNP features which are relevant enough to accomplish classification with high accuracy. The research work incorporates the application of CGARM and is carried out in two stages. In the first stage CGARM was used to choose significant SNP features resulting in dimensionality reduction. In the second stage classification was carried out by subjecting the selected features to Artificial Neural Network (ANN) algorithm. The main advantage of the proposed work is its ability to reduce the dimensions without compromising the quality i.e. using CGARM strong SNPs were selected by applying various constraints like Syntactical constraints, Semantical constraints and Dimensionality Constraints resulting in higher accuracy. The CGARM technique is applied on Autism data collected from National Center for Biotechnology Information (NCBI) repository. The data is divided into a set of 118 features, out of 118 features CGARM contributed in identifying 22 predominant SNPs. Further by applying forward selection method top 17 features were selected and were given as input to ANN. The 10 fold cross validation resulted in 76.9% accuracy which was found to be 50% more than that of original features. The proposed work contributed in reducing the dimension by 85% and provided 76.9% accuracy with the help of only 15% features. 2020 IEEE. -
Clustering Faculty Members fortheBetterment ofResearch Outcomes: A Fuzzy Multi-criteria Decision-Making Approach inTeam Formation
From a talent-pool of people, choosing an efficient team is tough. Faculty members of a higher education institution constitute the talent-pool. Teams have to be formed from them so that research output of each team is maximum. Amongst numerous research skills, thirteen are identified as most desirable skills. The level of these thirteen skills, viz., concept articulation, formatting according to templates/style sheets, identifying the relevant literature, initiative, logical reasoning, patience, problem formulation/problem finding, proof reading skills/identifying mistakes in written communication, searching/browsing skills/quick search techniques, sense of positive criticism, statistical knowledge, the ability to stay calm, and written communication skills, varies from person to person. Historical ranking of these skills and self-evaluation of the level of acquisition of these skills is used along with the years of experience, educational qualification, gender, marital status, etc., to rank individual faculty members. The fuzzy ranking of the faculty members thus obtained is used to cluster them into teams that are efficient in complementary skills. Each team thus formed is involved in collaborative research leading to research publication. The model is successfully implemented in a university department with 40 faculty members. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A meta-heuristic based hybrid predictive model for sensor network data
Many prediction algorithms and techniques are used in data mining to predict the outcome of the response variable with respect to the values of input variables. However from literature, it is confirmed that a hybrid approach is always better in performance than a single algorithm. This is because the hybridization leads to combine all the advantages of the individual approaches, leading to the production of more effective and much improved results. Thus, making the model a productive one, which is far better than model proposed using individual techniques or algorithms. The purpose behind this chapter is to provide information to the users on how to build and investigate a hybrid Feed-forward Neural Network (FNN) using nature inspired meta heuristic algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat Algorithm (BBAT), and hybrid BBATGSA algorithm for the prediction of sensor network data. Here, FNN is trained using a hybrid BBATGSA algorithm for predicting temperature data in sensor network. The data is collected using 54 sensors in a controlled environment of Intel Berkeley Research lab. The developed predictive model is evaluated by comparing it with existing two meta heuristic models such as FNNGSA and FNNBBAT. Each model is tested with three different V-shaped transfer functions. The experimental results and comparative study reveal that the developed FNNBBATGSA shows best performance in terms of accuracy. The FNNBBATGSA under three different V-shaped transfer functions produced an accuracy of 91.1, 98.5, and 91.2%. 2019, Springer-Verlag GmbH Germany, part of Springer Nature.