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An Integrated Approach to Green Cloud Solutions for Energy-Efficient Sustainable IT and Carbon Footprint Reduction
Cloud computing has become a very important part in everyday life, but this has also made a lot of carbon footprint because of the energy consumption in the data centers. The pandemic had affected these emissions, and they quickly came back, which has shown the requirement for sustainable solutions which will help in fighting the increase in carbon footprint. For these problems, the green computing technology will give probable solutions by promoting the technology that would be responsible enough to decrease these effects of harming environment. It will have techniques like smarter system designs, operations that are energy efficient, and smart techniques for optimization. This study explores how the above set principles can reduce the overall digital carbon footprint and help to create economically viable businesses. This approach provides a forward path for technology progress and profitability aligning with the environment sustainability which is a necessary component for business longevity. 2025 IEEE. -
An Integrated Approach Towards Sustainable Waste Management: Decentralized and Community-Based Practices
Waste management has always been a growing concern, since enormous quantities of waste are generated in vulnerable tourism regions, leading to mounting environmental concerns and hazardous health issues, which are faced by the majority of the local bodies and local communities. Vulnerable destinations are unable to handle such large quantities of solid waste due to financial and institutional debilities. This chapter will present a comprehensive view of solid-waste-management mechanisms, and most importantly, will highlight important issues, like segregation of waste, an integrated approach for the treatment of waste and scientific disposal methods. Critical directions are presented to reiterate the several policies and programmes so as to improve the current scenario, and thereby, support the cities and towns by devising integrated strategies towards community engagement in waste management and the role of regulators in overcoming the challenges of solid-waste management in our country. This chapter is built on a sustainable outlook by providing an integrated framework of decentralized and community-based practices. It will also explore important dimensions of sustainability that will require greater attention towards a preliminary framework of sustainable community-based waste management. 2024 CRC Press. -
An integrated care model for elderly in home countries: the role of social work and legal services for transnational families
The need for integrated care for the elderly has grown globally due to ageing populations and the increasing prevalence of chronic health conditions. Many elderly individuals, particularly those in transnational families, face challenges in accessing adequate care and support, as their family members may be dispersed across different countries. This study addresses the role of social work and legal services in supporting elderly individuals in their home countries, focusing on transnational families. The integration of social work practices and legal frameworks is crucial in providing effective care for the elderly in these complex family dynamics. The research explores the challenges faced by elderly individuals and their families, emphasizing the importance of collaboration between social work professionals and legal experts. The study proposes an integrated care model that combines these services to enhance the well-being and rights of elderly individuals in home countries, offering insights into how this model can be applied in transnational family settings. The Author(s), under exclusive licence to Institute for Social and Economic Change 2026. -
An integrated framework for digitalization of humanitarian supply chains in post COVID-19 era
Digital Supply Chains (DSCs) are transforming industries across various domains. Digitalization can improve coordination, increase data collection and retention capacities, enhance funding mechanisms, and improve operational performance and resource utilization. However, DSC adoption is constrained by lack of funding, operational complexities, infrastructure issues, etc. Thus, the need emerges to explore the digitalization of the Humanitarian Supply Chain (HSC) and provide solutions that can ease the adoption of DSC. In this study, a framework is created to facilitate the digitalization process of HSC in post COVID-19 era. Nineteen related drivers are identified with the potential to digitalize the HSC. The drivers are identified from the previous literature and finalized with the assistance of HSC stakeholders. A Principal Component Analysis is carried out to discover the most pertinent drivers from the identified list of drivers. A Kappa analysis is adopted to perfect the priority map of the digitalization drivers. Further, the neutrosophic DEMATEL methodology is adopted to prioritize the potential drivers and find their dependency on each other. The results from the study indicate that the most influential drivers fall under the operational and technological categories. However, the social drivers have the potential to play a significant contribution in an effort to HSC digitalization. In addition, the study presents strategies for enhancing funds collection and data management using emerging technologies. These strategies can assist HSC decision-makers in formulating relevant policies and strategic interventions. 2023 Elsevier Ltd -
An Integrated Model for Team Dynamics for Enhanced Collaboration and Performance
The CTF model introduces a new approach to team organization, inspired by the atomic structure of carbon. This interdisciplinary model combines organizational psychology, team dynamics research, and chemistry principles. The paper evaluates current team models, such as Belbins team roles, Hackmans model of team effectiveness, and the GRPI model, emphasizing their strengths and weaknesses. CTF expands on these frameworks while addressing their limitations. It consists of a productivity core (similar to protons and neutrons) and critique networks (similar to electrons), connected by Team Cohesion Factors. Unlike previous models that focus on specific aspects, CTF provides a comprehensive structure that integrates leadership, execution, and feedback mechanisms. It balances a hierarchical structure with collaborative input, including internal and external feedback systems. Inspired by the adaptability of carbon, the model is suitable for dynamic environments. Although it shows promise in addressing the limitations of previous models, CTF needs empirical validation. This paper lays out the theoretical basis of CTF, compares it with existing frameworks, explores its potential benefits and limitations, and outlines future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
An integrated model to predict students online learning behavior in emerging economies: a hybrid SEMANN approach
Purpose: The online learning environment is a function of dynamic market forces constantly restructuring the e-learning landscapes complete ecosystemcape. This study aims to propose an e-learning framework by integrating the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) to predict students Online Learning Readiness and Behaviour. Design/methodology/approach: A structured questionnaire was used to collect data from 406 students through a survey. The data were analysed using two-stage structural equation modelling and artificial neural network (ANN). Findings: The studys results revealed that perceived ubiquity (PUB) positively influences perceived ease of use, usefulness and attitude. Similarly, perceived mobility significantly influences perceived ease of use and attitude. Furthermore, attitude, subjective norms, perceived behavioural control and perceived usefulness significantly influence readiness to learn online, which further influences students online learning behaviour. The root-mean-square error (RMSE) values obtained from the ANN analysis indicate the models predictive solid accuracy. Originality/value: The study contributes to the existing literature by proposing an Online Learning Behaviour Model by integrating the TAM and the TPB frameworks in association with two additional constructs, PUB and Perceived Mobility. Secondly, this study proposes a unique triangulation framework of recommendations for learners, educators and policymakers. 2024, Emerald Publishing Limited. -
An integrated model to predict students online learning behavior in emerging economies: a hybrid SEMANN approach
Purpose: The online learning environment is a function of dynamic market forces constantly restructuring the e-learning landscapes complete ecosystemcape. This study aims to propose an e-learning framework by integrating the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) to predict students Online Learning Readiness and Behaviour. Design/methodology/approach: A structured questionnaire was used to collect data from 406 students through a survey. The data were analysed using two-stage structural equation modelling and artificial neural network (ANN). Findings: The studys results revealed that perceived ubiquity (PUB) positively influences perceived ease of use, usefulness and attitude. Similarly, perceived mobility significantly influences perceived ease of use and attitude. Furthermore, attitude, subjective norms, perceived behavioural control and perceived usefulness significantly influence readiness to learn online, which further influences students online learning behaviour. The root-mean-square error (RMSE) values obtained from the ANN analysis indicate the models predictive solid accuracy. Originality/value: The study contributes to the existing literature by proposing an Online Learning Behaviour Model by integrating the TAM and the TPB frameworks in association with two additional constructs, PUB and Perceived Mobility. Secondly, this study proposes a unique triangulation framework of recommendations for learners, educators and policymakers. 2024, Emerald Publishing Limited. -
An Integrated Pythagorean Fuzzy Delphi-AHP Framework for Optimizing Foreign Direct Investment: Key Drivers for Success
Foreign Direct Investment (FDI) plays a pivotal role in global economic development, fostering cross-border collaborations and driving economic growth. Recognizing the significance of optimizing FDI drivers, this study employs a novel approach by integrating the Pythagorean Fuzzy Delphi (PFD) and Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP). The Pythagorean Fuzzy Delphi methodology was used to identify and classify drivers into Technological, Political, Environmental, Social, and Cultural categories. Subsequently, the PFAHP was employed to rank these drivers. The top three prioritized drivers are: advocating for favorable foreign investment policies and trade agreements; implementing advanced cybersecurity measures to safeguard sensitive technology and data; and developing cutting-edge research and development facilities to foster innovation and attract technology-intensive investments. The study concludes by discussing how implementing these top-ranked drivers can significantly enhance FDI by creating a conducive environment for international investment, thereby contributing to economic prosperity and technological advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less. 2021. All Rights Reserved. -
An Integrated Scalable Healthcare Management System Using IOT
Healthcare management is the challenging task of maintaining the patients medical-related data and images. Pervasive computing, which consists of a wireless network, is an innovative medium for medical data transmission. Here, we propose SHMS (Scalable Healthcare Management System) and interoperability, an available and user-friendly platform. It utilizes a huge amount of data and medical images that must be managed and stored for processing and further investigation. In our work, data like heartbeat, temperature, blood pressure, and ECG readings are collected using different sensors and in one gateway protocol. This design is used for transferring, managing, and accessing documents containing health-related information, which is scattered across different system and organization domains. It is scalable because cloud platforms provide communication APIs, the web service interfaces ensure interoperability, the availability makes patients, doctors, or administrators able to access medical-related data anywhere, and Android OS makes it user-friendly. The security of the data collected can be achieved by authenticating storage using a cryptographic ECC algorithm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Integrated Segmentation Techniques for Myocardial Ischemia
Abstract: Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately realistic in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia. 2020, Pleiades Publishing, Ltd. -
An Integration of AI Technique in the Field of Healthcare Industry
Over the last few years, the field of intelligent machines (AI) has experienced fast improvements in software algorithms to hardware deployment, and varied uses, especially in the area of healthcare. This thorough study aims to capture recent developments in AI uses within biomedicine, spanning disease diagnoses, living support, biological computation, and research. The primary goal is to record recent scientific successes, discern what is happening in the technological environment, perceive the enormous future scope of AI on biomedicine along and serve as a source of stimulus for researchers through related fields. It is obvious that, similar to the development of AI itself, the use of it in biology continues to remain in its infant state. This review expects ongoing breakthroughs and improvements that will push the limits and broaden the range of AI uses in the near future. In order to communicate the changing possibility of AI in biology, the study dives into individual case studies. These include anticipating of epileptic seizure events and the uses of AI in treating a faulty urine bladder. By studying these cases, the overview seeks to explain the visible impact of AI off healthcare and reinforce the chance of immediate developments in this evolving and promising field. 2024 IEEE. -
An integration of big data and cloud computing
In this era, Big data and Cloud computing are the most important topics for organizations across the globe amongst the plethora of softwares. Big data is the most rapidly expanding research tool in understanding and solving complex problems in different interdisciplinary fields such as engineering, management health care, e-commerce, social network marketing finance and others. Cloud computing is a virtual service which is used for computation, data storage, data mining by creating flexibility and at minimum cost. It is pay & use model which is the next generation platform to analyse the various data which comes along with different services and applications without physically acquiring them. In this paper, we try to understand and work on the integration model of both Cloud Computing and Big Data to achieve efficiency and faster outcome. It is a qualitative paper to determine the synergy. Springer Science+Business Media Singapore 2017. -
An Integration of Satellite A Based Network with Higher Level Type Network with the use of P-P Connection: A Deep Review
The Aerial Access 6g Network (AAN) is seen as a way to access remote and sparsely populated areas not served by traditional terrestrial networks, especially with the advent of 6G technology. This study presents a new approach for efficient data collection and transmission in point to point access networks using low earth orbit (LEO) satellites and high altitude platforms (HAPS). Incorporating LEO satellites as backlinks and HAPs as airborne base stations, the system provides low-bandwidth transmission to ground users. A Time Augmented Graph (TEG) model is proposed to represent the dynamic topology of the air access network according to time slots. With this example, this study can create an entire programming problem with the goal of maximizing data transfer to the country's data processing centre (DPC) while respecting resource constraints. Benders' decomposition-based algorithm (BDA) is proposed to solve the NP-hardness of the problem and is shown to perform well in producing near-optimal solutions. The effectiveness and efficiency of the proposed strategy is verified through simulation results performed in a realistic environment, showing high speed and performance comparable to search methods. By informing the design and optimization of future communication systems, this study will provide a better understanding of how HAP and LEO satellites work together in aerial access networks for the collection and delivery of remote terrain data. 2024 IEEE. -
An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine
The long-term prognosis and mortality rates can be improved with early identification of breast cancer. The time-consuming and expensive procedures of mammography, MRI, ultrasound, CT, PT, and biopsy have been the subject of much research; nevertheless, these approaches are not suitable for younger women and can be rather expensive. This study employed cutting-edge image processing to improve early breast cancer detection. The researchers utilised anisotropic filtering to reduce background noise in medical images after picking mammograms at random from the Digital Database for Screening Mammography. The use of morphology-based feature extraction allowed for autonomous and accurate categorisation after mass segmentation using a genetic algorithm with recurrent thresholding. By merging a KF with an ELM enhanced with an AV, a new model named KF-av-elm improves diagnostic accuracy. Medical imaging noise and estimating errors are both significantly reduced by the combination method. Their accuracy rating of 98.28% allowed them to outperform other approaches. The KF-av-elm model appears to be a reliable, efficient, and effective diagnostic tool; its adoption may lead to better identification and outcomes for breast cancer patients. 2025 IEEE. -
An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM
Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didnt differentiate the Logical Flaws (LFs) and anomalies in WAs, thereby increasing the misclassification rate. Hence, in this paper, a novel black-box testing framework that incorporates an advanced technique called Gated Transformer Memorized transferred Recurrent Mishswish unit (GTMRM) is proposed for distinguishing between LFs and other vulnerabilities, thus enhancing the reliability of WAs. Initially, the user registration is carried out, followed by Hash-based Message Authentication Code Hash-based Message Authentication Code (HMAC) creation. Afterward, the registered users log into the application to request a Uniform Resource Locator (URL) for access. In the meantime, to authenticate the user, the HMAC verification is performed. Once the authentication is successful, the user is granted for accessing the functionalities. Thereafter, the black-box-centric LF and anomaly identification is done; here, the raw dataset is initially pre-processed. Subsequently, concerning a similar domain, the pre-processed data is clustered. Next, the features are extracted, followed by feature selection. Then, from the grouped data, the graph is constructed. The pattern labelling is carried out centered on the graph features. Lastly, the Logical Flaws (LF), anomaly, and legitimate access are proficiently classified by the proposed GTMRM. A compensation measure is applied in the case of a LF. After that, the data is securely stored in the cloud server with an accuracy of 99.14%. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
An Intelligent Business Automation with Conversational Web Based Build Operate Transfer (BOT)
The field of AI chatbots with voice help capabilities has seen significant advancements recently because to the usage of NLP (Natural Language Processing), NLG (Natural Language Generation), and (DNN) Deep Neural Networks. Using the expanding skills of chatbots, which are assisted by AI and ML technologies, a variety of business challenges may be handled. Profitability is one of the most crucial features of a business. This is only achievable if top-level management is aware of the company's costs, revenues, and human resource performance. In this case, an AI-powered chatbot with voice help may be utilised to evaluate corporate data and provide a report. The Bot knows the meaning of words and responds to them thanks to the wordnet in the corpus. Corpus is basically a dictionary for ChatBot. Top management may ask the Bot anything, and the Bot will quickly undertake exploratory data analysis and create a report. The Bot first understands the data using feature selection and then performs exploratory data analysis. After the EDA technique, Bot activates the voice recognition mode to understand the question and give answers. The Bot can use a male or female voice (depending on the developer). Then BOT provides a data table and visualisations for better understanding. 2020 Copyright for this paper by its authors. -
An Intelligent Cognitive Framework for Crime Prediction in Smart Cities using Video Mining
Booming development in cities with dense population have led to urban policing and public safety emerging as urgent concerns in city environments.current monitoring practices including CCTV'S and other IOT sensors generate a vast amount of data ,thus making them inadequate for the task. However a combination of video mining,computer vision,artificial intelligence and data mining techniques,do offer us a better framework for monitoring and real-time detection of crime in Smart city"s environment. This paper proposes an intelligent and Cognitive framework for prediction of crime. by combining various advanced modals such as YOLO (You took Only Look Once) for detecting objects, 3D Convolutional Neural Networks (CNN) for recognizing actions, deep SORT for tracking multiple objects, One-class SVM for detecting anomaly and LSTM for behavioral analysis. These modals can organized to function in a coherent system which can be organized to distinguish examine and trail illegal activities such of mugging, robbery, pick pocketing, violence, utilizing available live video feeds. By efficient date processing, and overcoming shortcomings such of limited labeled datasets and real-time feed detection, this framework can provide practical conclusion making tool for law enforcement in urban smart city environments which can enhance urban safety.Besides effective crime detection,this tool compiles with established ethical standards such as upholding privacy and legal compliance. 2025 IEEE. -
An Intelligent Decision Support System to Aid Profit Planning in Manufacturing Companies
In order to assure accuracy in profit planning and decision-making, this study uses an intelligent decision support system to investigate an appropriate approach for calculating the "Break-Even" point in multi-product segments while taking into account the implications for contribution margin, demand, and capacity. The research's methodology and findings may be used to propose new projects, grow businesses, and make decisions in processes that focus on many products. Data are used to illustrate the advanced level of break-even analysis and application, and a description of the convenient and system-generated method of computation is given. A mathematical approach has been used based on actual data to show how to determine the break-even point without sacrificing the influencing aspects such as contribution margin, capacity, product mix, and demand for each. The researchers have created a good system application-oriented platform to make it simple to calculate the break-even point, which will be crucial for decision-making and profit planning even with more than 500 SKU (Stock Keeping Unit). This research evaluated the data and created formulas for actual data structure-based analysis. The study's conclusions have a significant influence on those companies that need to determine the true break-even threshold. The challenge area of concern might be the applicability of this activity for other sectors and other countries as this research was centred on the plastic bag industry in Malaysia. Future research can also analyse other important factors like start-up and semi-variable costs as they are not included in the current study. The identified break-even threshold can still be used effectively given the current market demand and the product's capacity. 2023, Ismail Saritas. All rights reserved. -
An Intelligent Framework for Evaluating Handwritten Responses: Integrating Bloom's Taxonomy with Adaptive Assessment
Traditional manual grading of descriptive-type answer scripts is inefficient and laborious, while existing technologies rely on strict keyword matching and cosine similarity, which fail to capture the semantic meaning and argumentative quality. This paper proposes a multi-layered intelligent framework for evaluating handwritten descriptive answer scripts by integrating Revised Bloom's Taxonomy with adaptive assessment methods. The system consists of a multi-dimensional evaluation strategy comprising four valuation metrics, namely, content relevance, coherence, depth, and argumentation quality. When compared with conventional methods of keyword matching or computing the cosine similarity, this proposed framework evaluates the semantic meaning and argumentative structure while adapting to varying response styles and contextual differences for personalised assessment. 2025 IEEE.