IITM Journal of Management and IT is a Bi-Annual Research Publication of Institute of Information Technology and Management.
Institute of Information Technology & Management
Machine Learning-Driven Framework for Enhancing Customer Retention and Reducing Churn
Customer retention remains a pivotal factor for organizational sustainability, with customer churn posing significant challenges to revenue stability across industries. Despite extensive advancements in machine learning techniques for churn prediction, the practical integration of these methods into cohesive, actionable frameworks that effectively translate predictive insights into retention strategies is insufficiently addressed. This study aims to develop and validate a comprehensive machine learning-driven framework explicitly designed to enhance customer retention and reduce churn by encompassing data preprocessing, model selection, predictive analytics, and intervention planning. Employing a mixed-methods approach, the research synthesizes findings from a systematic literature review and incorporates qualitative expert interviews with practitioners in customer relationship management and machine learning to ensure the framework’s practical relevance and operational feasibility. The outcomes demonstrate the critical importance of integrating human expertise for model validation and ethical considerations, further highlighting organizational and technological factors influencing successful implementation. The resultant framework bridges theoretical machine learning advancements and pragmatic retention applications by providing a robust, modular system that supports real-world decision making. This contribution not only advances scholarly discourse on AI enabled customer lifecycle management but also offers actionable guidance for businesses aiming to leverage data-driven strategies to mitigate churn and foster long-term customer loyalty.
Copyright (c) 2026 IITM Journal of Management and IT https://creativecommons.org/licenses/by-nc-nd/4.0
One Algorithm, Two Responses: Occupational Identity and Motivation in White and Blue Collar Platform Work
Purpose: This paper proposes the Occupational-Motivational Engagement Model (OMEM), a conceptual framework for thinking about how occupational identity shapes motivational experience under algorithmic management on digital work platforms. Design/methodology/approach: The paper is conceptual. It draws on integrative theorising, bringing together Self-Determination Theory, Herzberg’s Two-Factor Theory, and occupational identity research, and reading these alongside contemporary work-design scholarship (Parker & Knight, 2024) and recent empirical evidence from platform-work studies. Following a problematisation logic, we identify a gap in current frameworks and propose three psychological mechanisms (identity-based need prioritisation, cognitive framing of platform features, and occupation-specific social comparison) that, acting together, produce systematic differences in how white-collar and blue-collar platform workers experience algorithmic management. Findings: Seven propositions set out the occupational differences. White-collar workers tend to prioritise method autonomy, read algorithmic control as an encroachment on professional discretion, and anchor their comparisons on skill development and client quality. Blue-collar workers tend to prioritise temporal autonomy, treat algorithms as a source of predictability, and compare on earnings and performance metrics. Practical implications: OMEM points HRM toward occupationally informed algorithmic design. Platforms should protect method autonomy and invest in qualitative feedback for white-collar workers. For blue-collar workers, predictable workflows and legible quantitative metrics matter more. Originality/value: OMEM is, to our knowledge, the first mechanism-based framework that connects occupational identity, algorithmic job design, and motivational experience to explain occupational variation in platform work.
Secure and Sustainable Public Service Delivery with AI, Blockchain and IoT
The mix of AI, Blockchain, and IoT looks very promising for making public services in smart cities more secure and reliable. In this work we used a blockchain-based green edge computing dataset and tested anomaly detection on IoT streams using LSTM and GRU autoencoders. Both models gave perfect precision (1.0), but the GRU model performed better overall with higher recall (0.40 vs. 0.30) and F1-score (0.57 vs. 0.46), which shows it can catch more abnormal cases when the clean baseline data is limited. We also checked blockchain validation, that means classification accuracy of predictive models was modest (about 52%), but adding blockchain raised anomaly detection rates from 48.4% (database only) to 52.8%. This shows blockchain does help in tamper resistance. Then we combined AI accuracy, blockchain validation, and latency cost into a composite reliability index. Results showed AI-only had the best mean reliability (0.52), blockchain only was more variable (0.47), while the combined AI–Blockchain score was lower (0.30 mean) but more stable with reduced variance. Hence the study proves AI lowers false alarms, blockchain locks data against tampering, and together they give balanced reliability. Together they offer a solid framework for transparent, tamper-proof, and resilient IoT public services, supporting efforts like Digital India and applications such as smart grids, ration distribution, and city governance.
Unpacking the Motivational Dynamics of PRP: Insights from an Empirical Study of Indian ITFirms
This study aims to understand the motivational impact of Performance Related Pay (PRP) in the Indian IT industry based on a mixed-methods research. Semi-structured interviews with 10 employees provided qualitative information whereas the quantitative data were collected using a structured questionnaire among 55 respondents. The results show that PRP has a positive effect on professional engagement, performance, productivity, and job satisfaction of employees and it also helps to reduce turnover intention. However, the research also points out that PRP has a great number of disadvantages, such as subjectivity of evaluation, inefficiency of the developmental feedback, work intensification, and overestimation in terms of workload, competitive working conditions, and weakening of team work. Notably, the findings reinforce the importance of intrinsic motivators, that include: autonomy, flexibility, meaningful and challenging work, supportive relationships, skill development and learning opportunities, in workplace motivation. The research finds that PRP does have a beneficial effect on staff motivation but its application should also be thoroughly thought out taking into consideration the adverse effects and the supplementary influence of intrinsic motivation variables.
Communication As A Catalyst for Good Governance and Policy Innovation
This essay investigates how communication might improve policy innovation and good governance. Although transparency, accountability, and citizen participation are prioritized in governance frameworks, communication is the means by which these ideals are achieved. The study looks into how policy effectiveness and innovation outcomes are affected by three communication dimensions: transparent communication, participatory communication, and strategic communication. Structural equation modeling (SEM) was used to evaluate 240 respondents to a structured survey that included administrators, academics, and civil society players. The findings show that strategic communication directly promotes policy innovation, participatory communication greatly increases legitimacy and citizen participation, and transparent communication considerably improves accountability and trust. When taken together, these characteristics explain 41.2% of the variation in governance and policy innovation results. The study contributes empirical evidence to the literature on governance, highlighting that communication is not merely a supporting function but a core driver of institutional innovation. Policy recommendations stress the integration of communication strategies into governance frameworks to strengthen democratic practices and responsive policy making. The incorporation of communication tactics into governance frameworks is emphasized in policy recommendations as a means of enhancing responsive policymaking and democratic behaviours
The Evolving Landscape of Risk: A Study of Banking Governance and Stability
Risk management is an essential component of banking, serving both as a safeguard against financial instability and a strategic tool for maximizing shareholder value. This paper explores risk perception in banking, common misconceptions about risk management, the importance of calculated risk taking, and the interplay between risk controls, corporate governance, and business culture. This paper also explores how financial institutions define and assess their risk appetite, balancing internal and external risk factors to maintain financial stability through effective risk governance. It places particular emphasis on the influence of regulatory frameworks, credit ratings, and market conditions in shaping risk management strategies. Additionally, the paper highlights the critical role of corporate governance in risk oversight, focusing on the responsibilities of governing bodies, risk committees, and financial institutions in mitigating risks while fostering sustainable growth.
Prioritizing the factors Influencing Employer Attractiveness of Gen Z-—An application of fuzzyanalytic hierarchy process
The present study aims to explore the relative importance of the different dimensions of employer attractiveness on the job applicant's intention to join with special reference to Gen Z. Moreover, the study aims to provide suggestive measures for organizations to develop a comprehensive employer brand strategy that appeals to the multifaceted expectations of Gen Z. Design/ methodology/ approach- The selected values of employer brand attractiveness were included in AHP questionnaire. Ninety-two management students studying in different management institutions were requested to provide their response and these were included for the prioritization using Analytic Hierarchy Process (AHP) method. Findings- The research findings suggest that the most important perceived value of employer attractiveness was the Economic value carrying 31.85% importance, followed by Role value (16.85%), Psychological and Cultural value (12.72%), Career Growth value (12.54%) and Ease value (10.16%). The least important perceived values of employer attractiveness were found to be Employer Brand value (6.29%), Location value (5.77%) and Innovation value (3.82%). Practical Implications- The results of the present study carry significant implications for organizations aiming to successfully attract talent, recognizing the importance of various values, including economic, role, psychological & cultural, career growth, ease, employer brand, location, and innovation, provides employers with actionable insights to customize their recruitment strategies. Moreover, the employer attractiveness perceived value index developed in the present study can be referred by organizations to gauge their respective employer attractiveness perceived value.
Forecasting Stock Price Movements Using Fundamental and Technical Analysis: A CaseStudy of Ola Electric
The fast-growing electric vehicle (EV) sector in India has drawn considerable attention from investors and financial analysts. This research paper aims to evaluate and forecast the stock price trends of Ola Electric through a combination of fundamental and technical analysis techniques. As Ola Electric emerges as a major player in the Indian EV industry, it is crucial for investors and stakeholders to understand its financial health and how it fits into the broader market. The analysis starts with a comprehensive examination of the company's financials, including profitability, liquidity, solvency, and valuation metrics as presented in its financial reports. At the same time, technical analysis is conducted using historical stock data, incorporating tools like moving averages, the Relative Strength Index (RSI), MACD, and candlestick patterns to identify market trends and potential trading signals. By combining both fundamental and technical aspects, this study highlights how intrinsic value and market sentiment together influence stock price behavior. The findings show that the integration of these approaches leads to more accurate forecasting and a more thorough assessment of the investment opportunities in this dynamic market. This study not only explores the performance of Ola Electric but also contributes to conversations about investing in high-growth industries in the EV sector.
A Comparative Study of Total Quality Management Practices across Accredited Indian Higher Education Institutions
Here is the extracted text from the image: Abstract This research presents a comprehensive comparative analysis of Total Quality Management (TQM) practices implemented across accredited Indian higher education institutions. The study examines how different categories of institutions (central universities, state universities, deemed-to-be universities, and autonomous colleges) vary in their adoption and implementation of TQM principles. Using a mixed-methods approach, data were collected from 120 institutions across India through surveys, interviews, and document analysis. The findings reveal significant variations in TQM implementation across institutional types, with deemed-to-be universities and central universities demonstrating more mature TQM practices compared to state universities and autonomous colleges. The study identifies leadership commitment, customer focus, process management, and continuous improvement as critical factors influencing TQM success in higher education. The research contributes to the understanding of quality management in the Indian higher education context and provides practical recommendations for institutions seeking to enhance their quality assurance mechanisms. The implications for policy and practice are discussed, highlighting the need for a more standardized approach to TQM implementation across the Indian higher education landscape.
Machine Learning Techniques for Movie Recommendation: A Matrix Factorization Perspective
Recommender Systems have become a cornerstone of modern digital platforms; they are providing personalized content and suggestions there by significantly enhancing overall user experience. This paper explores the real-world applications of recommender systems with a particular focus on the movie domain. The paper discusses various areas where such recommender systems can efficiently be deployed and used, highlighting their relevance in entertainment, e-commerce, and content delivery platforms. Movie recommendation systems, in particular, leverage machine learning techniques to analyze users’ past behavior, preferences, and viewing patterns to generate tailored suggestions. These systems improve user satisfaction and play an important role in driving user engagement and platform retention. Moreover, the integration of contextual information—such as time, location, and user mood—further refines the recommendation process, making it more dynamic and responsive. This paper present a comprehensive overview of different approaches of machine learning being used in building movie recommendation systems, including collaborative filtering, content-based filtering, and hybrid models. The associated challenges with data sparsity, scalability, and cold-start problems are di, and propose potential solutions to address them. This paper aims to contribute to the growing body of knowledge in personalized recommendation by offering insights into both the technical foundations and practical implications of movie recommendation systems in today’s data-driven environment.
Content-Based Filtering and Collaborative Filtering
Sentiment Analysis using Lexicon based Approach
Triple talaq is also known as talaq-ebiddat instant divorce. It is a kind of Islamic divorce used by Muslims in India. It allows Muslims man to divorce their wife legally by simply stating the word ‘Talaq' three times in any form which can be in any way (verbal, written, or in electronic form). Now a day, the huge amount of data is posted on daily basis on the social media platform. Twitter is a well known social networking platform where the user can post their views, opinions, and thoughts freely. The sentimental analysis is a process of understanding opinions, thoughts and feelings of people about a given subject. This paper analyses tweets posted on Twitter on the subject Triple from the year 2002 to the year 2019. We have transformed unstructured data into well-informed data for getting the insights of people. The main focus of the work is to analyze the feelings of people using two well-known API like Text Blob, and Spa Cy. These APIs are based on Lexicon approach. This paper predicts sentiment into three classes positive, negative and neutral.
Copyright (c) 2019 IITM Journal of Management and IT https://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Analysis of Fuzzy C Means and Fuzzy C Means++
Cluster analysis is one of the most useful means for identifying relations and patterns in the area of data mining. It can be defined as partitioning of large volumes of data into various clusters that share some property or attribute. The most common clustering algorithm is k means. The more improved version of k means that incorporates fuzzy feature is fuzzy c means. To overcome some of the limitations of fuzzy c means, fuzzy c means ++ was introduced which was based on effective seeding mechanism of k means++ algorithm. The latter algorithm showed remarkable results but with some more limitations of its own. In this paper, we discuss both the methods and their algorithms in detail. We discuss the advantages and limitations of each in various scenarios.
Copyright (c) 2018 IITM Journal of Management and IT https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Driven Predictive Models for Infrastructure Health Monitoring and Failure Prediction
I would like to start my paper with a focus on a rising concern. The concern is related to the infrastructure industry, which faces challenges due to aging infrastructure, urbanization, and environmental pressures. While traditional maintenance methods rely on occasional maintenance checks, AI-based predictive models utilize machine learning and structural health monitoring (SHM) to provide a comprehensive solution, detecting errors and predicting failures. The failure to detect the errors is clearly seen in the I-35W bridge collapse of 2007. Our article will dive deep to know the potential of AI in improving the infrastructure reliability in bridges, dams, and buildings in the United States, India, and Canada. We will use six case studies, like as the Golden Gate Bridge, Tehri Dam, and Confederation Bridge, to study the results of monitoring time, maintenance costs, and safety incidents. We will study the data integration, environmental variability, and regulatory hurdles as key challenges. Along with this, technical advancements such as deep learning and digital twins are also studied, based on their scalability and adaptability. Our study also covers the global application of AI technologies in civil engineering. This will help us to know about the future developments of generative AI and the integration with Internet of Things (IoT) technology. These topics play a major role in sustainable and great infrastructure.
Towards Resilient PLM Architectures: Cyber Threats,Security Mechanisms, and Industrial Implications
Implementation of Product Life cycle Management (PLM) systems in firms from aerospace, automotive, and manufacturing industries helps in efficiently managing design, production, as well as the maintenance information of the company. PLMs help in managing the information at different levels and stages of the company. However, Exploitation of cloud computing, Internet of Things (IoT), and advanced supply chains poses these industries to multiple cyber threats. This document highlights the PLM applications life cycle from design and development to production, maintenance, and eventual decommissioning, highlighting the impact of significant cyber attacks at each stage. We focus on real-world vulnerabilities and attack vectors, like CVE-2021-37161 and Team center’s CAD file tampering, SQL in PTC Windchill, supply chain insider threats, and cloud misconfigurations. Analyzing breaches and defense frameworks reveals the application of ZTA (Zero Trust Architecture), AI-driven anomaly detection, and blockchain with integrity verification as viable countermeasure frameworks. We highlight the pressing importance of embedding security-by-design methodologies in PLM ecosystems while addressing the risks of AI-augmented cyber assaults and quantum computing. This research provides groundwork to aid industries and academicians in strengthening cyber defenses for PLM systems in the scope of a converging industry 4.0.
Copyright (c) 2025 IITM Journal of Management and IT https://creativecommons.org/licenses/by-nc-nd/4.0
A Survey on Secure and Scalable Cross-chain Provenance Techniques For Digital Forensics
Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state- consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.
Human Activity Recognition in Smart Homes: Advancements and Future Trends With Edge Computing Integration and Ethical Frameworks
Human Activity Recognition (HAR) in smart homes is a vital breakthrough in constructing intelligent systems for monitoring Activities of Daily Living (ADL). It improves healthcare, security, and minimizes energy requirements. This study utilizes ARAS data to construct and test sophisticated predictive models for multi- resident activity systems based on state-of-the-art machine learning classifiers, ensemble approaches, and deep neural network structures.We utilize advanced feature extraction and selection methods such as Information Gain, Recursive Feature Elimination (RFE), and Random Forest Importance to balance model performance and computational efficiency. Our work involves incorporating edge computing paradigms with ethical frameworks to respond to privacy issues and real-time processing needs. The hybrid architecture we put forth showcases improved performance with accuracy of 99.6% and 99.8% for households A and B respectively, while being low in latency and energy consumption. Additionally, we present thorough ethical guidelines and privacy- preserving methods to promote ethical deployment of HAR systems in home environments. Experimental verification on a variety of scenarios ensures the scalability and robustness of our method, qualifying it as a potential solution for next-generation smart home systems.
Review of Hybrid Deep Learning Techniques For Robust ECG Signal Classification by Addressing Noise and Class Imbalance
This review paper presents a comprehensive analysis of recent advancements in hybrid deep learning techniques for automated electrocardiogram (ECG) signal classification, with a central focus on overcoming challenges such as signal noise and class imbalance. Given the pivotal role of ECG analysis in the early detection of cardiovascular diseases, there is a growing demand for intelligent, robust, and clinically deployable systems. The paper examines hybrid model architectures that integrate Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms to effectively capture both spatial and temporal features of ECG signals. Key studies are reviewed in terms of their preprocessing strategies, feature optimization methods, and noise mitigation techniques. In addition to architectural insights, the paper outlines a structured taxonomy of hybrid approaches based on model composition, preprocessing methods, imbalance handling techniques, evaluation metrics, and deployment environments. It also explores emerging application areas, including real-time monitoring systems, telecardiology platforms, and clinical decision support tools. Common research limitations—such as computational complexity, limited interpretability, and real-world generalizability—are critically discussed. The review concludes with recommendations to guide future work toward scalable, explainable, and clinically relevant deep learning frameworks, ultimately aiming to support the next generation of intelligent cardiovascular healthcare systems.
ECG Signal Classification, Hybrid Deep Learning, Noise Resilience Class Imbalance
Human-ai Collaboration Models in Operations and Supply Chain Management
The integration of Artificial Intelligence (AI) into operational and supply chain processes is transforming traditional management models, creating new paradigms of Human-AI collaboration. This study investigates how different collaboration models enhance efficiency, decision-making, and resilience in operations and supply chain management (OSCM). Using a mixed-methods approach, including expert interviews (n=5) and a survey of 200 professionals, we develop the Adaptive Human–AI Collaboration Model (AHACM), a novel framework that explains how assistive, augmentative, and autonomous collaboration modes evolve with organizational data maturity and governance capacity. Through a literature review and analysis of case studies from manufacturing, logistics, and procurement, we identify three dominant models: AI-assisted decision- making, human-in-the-loop optimization, and autonomous AI-driven operations with human oversight. Key findings show that AI-assisted models improve forecasting and inventory control, human-in-the-loop systems enhance adaptability, transparency, and ethical compliance, and autonomous models strengthen real- time logistics and dynamic demand sensing. Challenges remain in data integration, workforce up skilling, and algorithmic transparency, influencing adoption across industries. The study concludes that the optimal Human-AI collaboration model depends on organizational maturity, data infrastructure, and strategic alignment. A hybrid approach, combining human intuition with AI-driven insights, emerges as the most effective strategy. By introducing AHACM, this paper offers both a conceptual and practical tool for assessing readiness and designing collaborative Human-AI systems in OSCM.
Stock Market Reaction to Merger Announcements in Indian Public Sector Banking: An Event Study Approach
Purpose: The study aims to analyze the stock market reaction to the merger and acquisitions announcement in the Indian Banking Sector. Design/Methodology: The Event-Study methodology was used in order to check the short-term reaction of the investors over a 21-day event window. The market model was used for calculation of expected return with the benchmark index as Nifty50. Statistical tests, including the Shapiro-Wilk test and t-tests (two-tailed), were applied to analyze whether the resultant Abnormal Return was significant. Findings: The findings indicate that no bank has a significant abnormal return on Day 0. However, on Day +1, 4 banks have showcased abnormal returns. Indian Bank continued to show a reaction on Day +2, and Union Bank again on Day +6. However, Bank of Baroda did not show any market reaction. It suggests that investors either expected the news or quickly adjusted to it. Originality/Value: The study contributes to the growing literature on M&A by offering empirical insights into investor behavior and sentiment, and the semi- strong form of EMH in the Indian context.
Women-LED Startups and Atmanirbhar Bharat
The vision of Atmanirbhar Bharat (Self-reliant India) emphasizes entrepreneurship, innovation, and inclusive growth, with women-led startups playing a transformative role. Women entrepreneurs are emerging as significant contributors to India’s socio-economic development, yet they continue to face systemic barriers in terms of finance, market access, social norms, and digital inclusion. This study explores the dynamics of women-led startups within the Atmanirbhar Bharat framework, identifies key challenges, and provides actionable recommendations for policymakers and stakeholders. Using secondary data, government reports, and thematic analysis, the research highlights funding gaps, limited mentorship, and inadequate policy outreach as primary barriers. The paper argues that enabling ecosystems, gender-sensitive financial products, and digital empowerment can accelerate women’s participation in nation-building.
Women Entrepreneurs, Startups, Atmanirbhar Bharat, Gender Equality, Innovation, Financial Inclusion, India
Maximizing Gen Z Engagement: A Modern Approach to it Sector Talent Management
Crisis in Generation Z Employee Retention in the IT Industry due to the rapid digital transformation witnessed in the IT industry, the challenge of retaining the Generation Z employee has become more pronounced, whose workplace expectations and career interests are different from the previous generations. This research aims to obtain better information on combination of AHP (Analytic Hierarchy Process) into talent management systems regarding Gen Z retention in IT ventures. AHP, which stands for Analytic Hierarchy Process, is one of several multi- criteria decision-making (MCDM) methods users can employ to structure their thoughts in a systematic manner, allowing them to prioritize factors affecting Gen Z retention, including work-life balance, career growth, workplace culture and compensation. By doing so, organizations can tailor retention strategies that meet Gen Z expectations by weighting key variables. This study demonstrates a model to solve with AHP and highlights that how AHP model is applicable to find out employee engagement and satisfaction drivers while providing HR leaders a structured perspective. The analysis indicates that a methodical AHP-based talent management strategy will improve job satisfaction lower attrition rate and create a flexible/ socialized workplace environment. Furthermore, this study highlights the importance of providing continuous feedback mechanisms and personalized career development plans in reinforcing Gen Z retention initiatives. The framework presented will provide relevant and useful information to IT companies for designing HR policies aiming for long-term workforce stability.
AI-Driven Digital Transformation in Business Process Optimization
Artificial Intelligence (AI) has emerged as a transformative force in modern business and industries, known as digital transformation, necessitates a fundamental shift in traditional understanding of a business process optimization and human resilience to turbulent socio economic and technological environment. AI emerges as a disruptive force, with immense potential to effect businesses and individuals on an unprecedented scale and exponential pace. This paper is to present AI-driven digital transformation in business process optimization through the theoretical fundamentals of the concept, business process management (BPM) by AI, finance and accounting and HR by AI-powered digital transformation for a business optimization perspective. AI accelerate how work gets done, making organizations organized, avoiding refusal, and increasing efficiency at scale.
AI, AI-Powered, Digital, Block Chain, Ecological Need
Information and Communication Technology Adoption: A Case study of Nigerian Tertiary Education Regulatory Agency
This research study examines the factors that influence adoption of ICT in an organisation that regulates tertiary education in Nigeria based on UTAUT model. Loading factor and Kaiser-Meyer-Olkin (KMO) were used to validate the model. The results of the study show that social factors have positive significant impact on the intention to adopt ICT. The study found that organisational, economic and individual factors are not statistically significant with intention to adopt ICT. This suggests that public organisations should encourage use of social networks to perform their mandates and encourage employees to interact freely to enhance their skills.
Copyright (c) 2020 IITM Journal of Management and IT https://creativecommons.org/licenses/by-nc-nd/4.0
There are number of overwhelming choices over the internet therefore there is a need to filter and efficiently deliver the relevant information so that we can overcome the information overload problem, a potential problem of many internet users. Thus recommender systems provide users the solution to this problem. They search through a huge volume of dynamically generated information to provide users with their choice of content, what they want to see. Various techniques have been proposed and many software have been developed for a different no. of applications. This paper therefore reviews and explore up to date applications of recommender system, different characteristics and types of prediction techniques in recommendation systems.
Usage of Internet of Things in Education
In the forthcoming years, technology will control the learning practice in many ways. Internet of Things (IoT) continues to approve its important location in the perspective of Information and Communication Technologies and the development of society. Now a day's one of the advance word in the information technology is Internet of Things (IoT). The main objective of IoT is to transform real world objects to the artificial intelligent virtual objects. IoT will provide everything in the real world object to provide shared infrastructure to control the things around the state of the things. In recent times this study represents IoT concepts by researcher's scholars, expert system, corporate white papers and online databases. Moreover this research paper will present definitions, issues of internet things, basic requirements and characteristics of IoT. The main goal of research paper is to provide a complete overview of advance technologies associated with their usage of ICT in higher education. However new research will provide goal comprehension and assist accumulation effectively.
Intrusion Detection Techniques for Mobile Cloud Computing in Heterogeneous 5g
As the importance of distributed computers is rapidly growing, they are becoming the target of more and more crime. Intrusion may be defined as the set of attempts to compromise computer network security. Besides the several security services, Intrusion Detection System/Techniques are taken into point that strengthen the system security and is more powerful in preventing internal and external attacks. This technique is considered to be very efficient in preventing wireless communication in Fifth Generation. In this paper we will discuss what Mobile Cloud Computing is and various Intrusion Detection Techniques for mobile computing along with challenges faced by each technique.
The purpose of this paper is to present an idea of machine learning tools that are currently in use or are being studied globally. This work explains the detailed explanation of what is machine learning. This paper introduces a machine learning study by various authors. This work is summarizing the various machine learning tools and their comparisons and recent studies of machine learning tools respectively. The material presented in this paper is the result of a literature review of different research papers and books. This work compares the various tools of machine learning.
Cross-layer security solution for secure communication of sensorsin Wireless Sensor Networks
Safe path-finding is extremely necessary for multihop wireless systems such as Wireless Sensor Networks. Multihop wireless systems are more unprotected to safety outbreaks as associated to single-stage wireless networks. Cross-layer design production is a precise significant character for wireless sensor Advanced security network is significant submissions. for the achievement of announcement among sensor nodes in wireless sensor network because the numbers composed is regularly less and the network is most vulnerable. Numerous Security methods have been suggested to deliver safety resolutions besides numerous threats to the Cross layer modification techniques in Wireless sensor networks. In this paper we overview the existing schemes for the security of cross-layer design in wireless sensor networks. The proposed Security model will provide more security between sensor nodes and the base station in wireless sensor network. Security is important for the success of the wireless sensor network because the data collected are often sensitive and the network is particularly vulnerable. In the cross-layer design, constraints are substituted between changed layers to protect the well-organized use of energy. In this paper, we propose a secure security model that is based on cross-layer design. It uses a cross layer optimization Machine Reproduction consequences confirm that our proposed safety model better in many situations and in unpromising violence-disposed to the situation.
Performance Analysis of Feature-Based Automated Measurement of Mouse Social Behavioral
Automated social behavior analysis in the mammalian animal has become an increasingly popular and attractive alternative to traditional manual human annotation with the advancement of machine learning and video tracking system for automatic detection. In this work, we study a framework of how different features perform on the different classifiers to analyze automatic mice behavior. We conducted experiments on the Caltech Resident-Intruder Mouse (CRIM13) dataset, which provides two types of features: trajectory features and spatio-temporal features. With this feature, we train AdaBoost and Random Decision Forest (TreeBagger) classifiers to classify different mouse behaviors to show which features perform best on which classifier. The experimental result shows that the trajectory features are more informative and provide better accuracy than the widely used spatio-temporal features, and AdaBoost classifier shows better performance than the TreeBagger on these features.
Managing Mental Health during COVID- 19 Pandemic and Beyond
The novel coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis of a scale not previously experienced in modern times. The whole of humanity is sharing a problem that is changing the world, and humans are living under more uncertainty. Most people have been feeling intense anxiety related to COVID-19 and the aftermath the world will leave in its wake. The paper intends to put up a deeper understanding of the anxiety reaction about various aspects of current life. Clear strategies for managing and turning off the anxiety response for short periods is developed based on the understanding of how one’s brain reacts to crises along with some powerful tools for managing the pandemic. The paper explores some of the strategies to address mental health during COVID-19.