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An Integrated and Optimized Fog Computing enabled Framework to minimize Time Complexity in Smart Grids
A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. 2023 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 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 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 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 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 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 Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems
The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved. -
An intelligent inventive system for personalised webpage recommendation based on ontology semantics
Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users web usage data. An overall accuracy of 87.73% is achieved by the proposed approach. Copyright 2019 Inderscience Enterprises Ltd. -
An Intelligent Model forPost Covid Hearing Loss
Several viral infections tend to cause Sudden Sensorineural Hearing Loss (SSNHL) in humans. Covid-19 being a viral disease could also cause hearing deficiencies in people as a side effect. There have been pieces of evidence from various case studies wherein covid infected patients have reported to be suffering from sudden sensorineural hearing loss. The main objective of this study is to inspect the phenomenon and treatment of SSNHL in post-COVID-19 patients. This study proposes a mathematical model of hearing loss as a consequence of covid-19 infection using ordinary differential equations. The solutions obtained for the model are established to be non-negative and bounded. The disease-free equilibrium, endemic equilibrium and basic reproductive number have been obtained for the model which helps analyse the models trend through stability analysis. Moreover, numerical simulations have been performedfor validating the obtained theoretical results. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach
Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE. -
An Intelligent Recommendation System Using Market Segmentation
Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Intelligent Stock Market Automation with Conversational Web Based Build Operate Transfer (BOT)
Zerodha, Upstox, Angel Broking, Groww, etc. Such companies have the most significant users of traders/investors in the equity share market. Their trust is based on their ease of use, less time-consuming process, and accurate graphs and charts of real-Time data. But what if such companies had an algorithm that could predict the future prices of any share? Not just based on historical data but also on sentimental data? This project aims to build a speech recognition chatbot like Alexa Google, which will use Recurring Neural Network-Long Short-Term Memory (RNNLSTM) and Natural Language Processing (NLP) to predict future intra-day prices. 2022 IEEE. -
An Intelligent System to Forecast COVID-19 Pandemic using Hybrid Neural Network
A current outbreak known as COVID-19 has been discovered from the coronavirus was informed by WHO. COVID-19 is a universal pandemic that has brought out the best and the worst of humanity. Due to an increase in the cases daily, COVID-19 is creating a menace to public health and establishes a disruption of the social and economic development of the countries. The problem is the hospitals are not able to provide proper facilities and treatments on time due to the lack of facilities in India. The purpose of this project to build an efficient hybrid deep learning model for forecasting the COVID-19 pandemic with multiple features that are responsible for the spread of COVID-19 in the top five states in India. In particular, a hybrid model that incorporates Auto-Regressive Integrated Moving Average and Long-term Short Memory is been used to forecast confirmed cases. The linear and non-linear dependencies in the dataset is been dealt with by an ARIMA-LSTM hybrid model. As a result, when compared to the outcomes of ARIMA, LSTM models independently, the hybrid model was giving better results and was performing well in forecasting COVID-19 cases. Through this, the policymakers will get prior information on COVID-19 cases in states which will help the government and healthcare departments to take prominent measures to prevent it. 2021 IEEE. -
An intelligent web caching system for improving the performance of a web-based information retrieval system
With an increasing number of web users, the data traffic generated by these users generates tremendous network traffic which takes a long time to connect with the web server. The main reason is, the distance between the client making requests and the servers responding to those requests. The use of the CDN (content delivery network) is one of the strategies for minimizing latency. But, it incurs additional cost. Alternatively, web caching and preloading are the most viable approaches to this issue. It is therefore decided to introduce a novel web caching strategy called optimized popularity-aware modified least frequently used (PMLFU) policy for information retrieval based on users' past access history and their trends analysis. It helps to enhance the proxy-driven web caching system by analyzing user access requests and caching the most popular web pages driven on their preferences. Experimental results show that the proposed systems can significantly reduce the user delay in accessing the web page. The performance of the proposed system is measured using IRCACHE data sets in real time. Copyright 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.