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Corporate diversification and firms financial performance: an empirical evidence from Indian IT sector
The aim of this research paper is to provide empirical evidence on the effect of geographic and segment diversification on the financial performance of the Indian IT sector. The study was done on 12 listed IT firms representing 93% market share on BSE/NSE. Standard econometric regression analysis on panel data was carried out to find the stated relationship. The results of the regression analysis revealed that international/geographic diversification impacted strongly on IT firms profitability whereas product/segment diversification had no significant impact on the firms profitability. This study also proves the existence of demand for Indian IT sector in other countries. These results could be useful in decision making for top managers of IT companies as they advocate the need for diversification (specialisation) and growth in size and also provide encouragement to small-scale Indian IT companies to undertake international diversification activities with confidence. Copyright 2023 Inderscience Enterprises Ltd. -
Kakkot List- An Improved Variant of Skip List
Kakkot list is a new data structure used for quick searching in a well ordered sequence of list like Skip list. This ordered sequence of list is created using linked list data structure and the maximum number of levels here will be limited to log n in all input behavioral cases. The maximum number of items in each level is halved to that of previous levels and thus guarantees a fast searching in a list. The basic difference between Kakkot list and Skip list lies in the creation of levels and decision of when an item has to be included in the higher levels. In skip list the levels are created and items are added to each level during the insertion of an item where as in Kakkot list this will be done at the time of searching an item. This modification have made drastic impact in searching time complexity in the Kakkot list. Another issue in Skip list is that it is not cache friendly and does not optimize locality of reference wherein this problem is also addressed in Kakkot List. 2020 IEEE. -
Impact of macroeconomic variables on the prices of gold /
Journal of Emerging Technologies And Innovative, Vol.6, Issue 2, pp.569-576, ISSN No: 2349-5162. -
The quantum key distribution, attenuation and data loss over foggy, misty and humid environment
The quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non interceptable. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
A new assessment of quantum key distribution, attenuation and data loss over foggy, misty and humid environment
Quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non intercept able. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2018, UK Simulation Society. All rights reserved. -
Machine Learning Based Crime Identification System using Data Analytics
Poverty is known to be the mother of all crimes, and a vast percentage of people in India live below the poverty line. In India, the crime rate is rapidly rising. The police officers must spend a significant amount of time and personnel to identify suspects and criminals using current crime investigation. In this research, the method presented for designing and implementing crime identification and criminal recognition systems for Indian metropolitans is utilizing techniques of data mining. These occurrences are represented by 35 predefined crime attributes. Access to the crime database is protected by safeguards. The pending four subjects are important for crime unmasking, identification and estimation of criminals, and crime authentication, in that order. The detection of crime is investigated with the help of K-Means clustering, which iteratively builds two crime batches based on congruent criminal features. Google Maps is to enhance the k-means visualization. K-Nearest Neighbor classification is used to examine criminal identification and forecasting. This is used for the authentication of the results. The technique benefits society by helping investigative authorities in crime solving and criminal recognition, resulting in lower crime rates. This research study describes a way for creating and deploying crime solving and criminal recognition systems for Indian metro's using data mining tools in this study. The method consists of data evulsion, data pre- processing, clustering, Google map delegation and classification. The first module, data evulsion, retrieves unformed or unrecorded crime datasets from several criminal sources online from 2000 to 2012. In the second module, Data pre-processing cleans, assimilates, and reduces the obtained criminal data into organized 5,038 crime occurrences. Several predefined criminal traits represent these instances. Safeguards are in place to prevent unauthorized access to the crime index. The remaining components are critical for detecting crimes, criminal identity and prediction, and crime verification, in that sequence. The investigation of crimes is investigated using k-means clustering, which gives results repeatedly. 2023 IEEE. -
Artificial Intelligence and Deep Learning Based Brain Tumor Detection Using Image Processing
In the field of medical science, applications that are particularly used for diagnostic purposes, are used in the detection of brain tumors since detecting an error in MRI scanning is becoming a major task for radiologists and requires a lot of their focus. Flaws that are prevalent during tumor detection must be taken care of to avoid further complications. MRI scanning is one of the most recently developing technologies. The radiologist is a key player in the identification of the brain tumor. Radiologists have to check every image perfectly to avoid the errors in identifying the brain tumor. There is a probability that sometimes cerebral fluid may also appear as mass tissue during the MRI scan. The model that is proposed in this research uses a machine learning algorithm which helps to improve the validity of the classification of the images that are taken in MRI scans. The study focuses on having an automated system that carries out an essential role in determining whether a lump is present in the brain or not. The study tries to resolve the primary flaws in detection necessary to evade further complications in MRI images in brain detection. The main aim of this study is to train the algorithm in a more extensive dataset and to check the patient-level validity with the help of various new datasets. 2023 IEEE. -
Hedging with the Indo-Pacific: why Southeast Asia might benefit from embracing the construct
ASEANs engagement with the Indo-Pacific is often framed as a strategic shift, but this paper argues it is instead a continuation of its established hedging strategy. Drawing historical parallels, particularly Thailands colonial-era diplomacy, the article examines how ASEAN balances major power competition while preserving autonomy. By assessing ASEANs economic and security engagements with China and the United States, the paper highlights how hedging remains essential amidst intensifying geopolitical tensions. The Indo-Pacific framework does not require ASEAN to choose sides but reinforces its flexibility. ASEAN centrality, as enunciated by many global powers, has given much impetus to the organisation to continue with the hedging strategy. As the USChina rivalry deepens, hedging offers ASEAN the best path to stability and strategic relevance. 2025 The Round Table Ltd. -
Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. -
Sustainable Innovations in Statistics and Data Science
Description: Sustainable innovations in statistics and data science are increasingly vital in tackling complex global challenges such as climate change, public health crises, and resource management. By developing and applying advanced analytical methods, these fields enable more efficient, equitable, and informed decision-making across sectors. Integrating sustainability into data practices ensures that technologies support long-term environmental, social, and economic goals. This intersection not only enhances the accuracy and relevance of insights but also promotes ethical data use aligned with global sustainability standards. Sustainable Innovations in Statistics and Data Science brings together cutting-edge research, methodologies, and applications that address sustainability challenges across various fields. It delves into insights, techniques, and case studies that drive sustainable outcomes in environmental science, healthcare, urban planning, and other critical areas. Covering topics such as air pollution, environmental science, and urban development, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, data science and statistics practitioners, policymakers, government officials, industry leaders, innovators, educators, curriculum developers, and more. Coverage: The many academic areas covered in this publication include, but are not limited to: Air Pollution Artificial Intelligence (AI) Cardiovascular Health Climate Change Corporate Social Responsibility (CSR) Data Science Environmental Science Geometric Distribution Healthcare Quality Control Smart Cities Statistics Sustainable Innovation Urban Analytics Urban Development. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A Compact Workflow Model for Cloud Computing
Scheduling tasks in the cloud computing environment, particularly for data intensive applications is of great importance and interest. In this paper, we propose a new workflow model presented in a rigorous graph-Theoretic setting. In this new model, we would like to incorporate possible similarities between requisite files which are needed to complete the given set of tasks. We show that it is NP-Complete to compute the make span in this model even with oracle access to the cost of retrieving a file. 2015 IEEE. -
Evaluation of machine and deep learning models for utility mining-based stock market price predictions
Considering the extreme volatility of stock market returns and hazards, accurate price prediction has attracted the attention of both financial institutions and regulatory bodies. Stocks, due to their historically strong returns, have long been considered by investors to be an excellent asset allocation strategy. Predicting stock prices has never ceased being a hot topic of study. Many early-day economists sought to foretell future stock values. In subsequent years, as computer technology has advanced rapidly and mathematical theory has been extensively studied, it has been shown that mathematical models, like the time series model, may be very effective in predicting due to their simplicity and superiority. Over time, the time series model is put into practice. Over time, the horizon widened. Support vector machines and other ML techniques have challenges when applied to stock data because of its non-linearity. In subsequent years, thanks to advancements in deep learning, models like RNN and LSTM Neural Networks were able to analyze non-linear input, remember the sequence, and remember valuable information,Stock data forecasting cannot be done without it. 2024 Author(s). -
Carmelight Trends in Social Sector Expenditure
The Multidisciplinary National Journal, Vol-10 (1), pp. 77-96. ISSN-0975-9484 -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on CO2 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards CO2 emission. 2020 - Kalpana Corporation -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on C02 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards C02 emission. 2020 Kalpana Corporation. All Rights Reserved. -
An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET
The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs. 2025 The Authors. Published by Elsevier B.V. -
Nurturing Adult Socio-Emotional Skills and Engagement: The Transformative Power of Mentoring Program
There has been a growing interest in understanding the ways education could integrate socio-emotional learning (SEL) skills in their curriculum. This chapter explores by considering mentoring approach as a channel to foster SEL skills that would be beneficial to both adult learners and educators alike. The chapter emphasizes on the key SEL skills and also focuses on the need for higher institutions to promote adult SEL, not only for faculties but also for adult learners. Two main types of mentoring have been addressed, viz the traditional mentoring versus alternative mentoring approach. The chapter also discusses about incorporating the train-the-trainer model for mentoring. In essence, this SEL-based adult mentoring ensures that both mentees and mentors benefit. The mentees have gained self-awareness, responsible decision-making skills, relationship skills and emotional intelligence through this mentoring approach, while the mentors have acquired a sense of accomplishment and fulfillment that promotes their emotional intelligence and decision-making skills. 2026 by IGI Global Scientific Publishing. All rights reserved. -
EEG Emotion Recognition Using PSO-Based Feature Selection and Convolutional Neural Networks
EEG signals have become a promising source for emotion recognition due to their ability to capture the brain's electrical activity connected with different emotional conditions. In this work, a novel approach is proposed that integrates Particle Swarm Optimization (PSO)-based feature selection with Convolutional Neural Networks (CNNs) for improved EEG emotion classification. The method with the preprocessing of a notch filter to eliminate noise and enhance the quality of the EEG signals. Key features, including Magnitude Squared Coherence Estimate (MSCE) and Power Spectral Density (PSD), are extracted to capture essential frequency-domain information. PSO is employed to optimize the selection of features, reducing dimensionality while preserving the most relevant and informative attributes for emotion recognition. The optimized feature was subsequently passed to a CNN classifier, which improves the model's capability to accurately differentiate between different emotional states. This study is implemented using Python software to analyze emotion, and the effectiveness of the proposed approach is assessed using the EEG Brainwave dataset. Experimental results demonstrate that the proposed approach delivers an accuracy of 92.6% and a precision of 91%, highlighting its effectiveness in real-time, high-precision emotion recognition from EEG data. 2025 IEEE. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida.