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An Innovative Way of Trackable GDS in the Field of CC
It is important to provide security and efficient data exchange in cloud infrastructure and achieve traceability and anonymity of data. mean For high levels of safety and performance in one Anonymously, this article addresses the topic It allows data to be exchanged and stored between members of the same group in the cloud. Proposed arrangement creates unique and traceable group data sharing policies using group signatures and special agreements Strategies to accomplish these goals. this Facilitates anonymous communication between systems Public clouds have many users and. Real people following up when needed. Also, the system implements the main agreement programs to make it easier for team members to. Obtain a shared session key for secure data exchange and storage facilities. Basic generation processes a Symmetric Balanced Incomplete Block Theory (SBIBD), significantly reducing the workload of team members a shared session key must be introduced. In cloud computing contexts, the suggested system guarantees efficiency and security for group data sharing, as shown by theoretical analysis and experimental validation. 2024 IEEE. -
An insight into the superior performance of ZnO@PEG nanocatalyst for the synthesis of 1,4-dihydropyrano[2,3-c]pyrazoles under ultrasound
The investigation presents a straightforward synthesis of fifteen 1,4-dihydropyrano[2,3-c]pyrazoles using ZnO@PEG nanocatalyst in ethanol via Multicomponent approach under the influence of ultrasound. The present methodology successively tolerates a variety of functional groups and offers several advantages such as excellent yields without chromatographic purification, milder reaction conditions, shorter reaction times, and the use of an environmentally benign reusable catalyst. Ecstatically, the reaction was successfully scaled to gram level ascertaining the wider applicability of ZnO@PEG nanoparticles in multicomponent reactions. 2019 Elsevier Ltd. All rights reserved. -
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 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 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 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 Interrogation and Analysis of Postmodern 'Self' in Robert Lowell's Sonnet Reading Myself
The interrogation and analysis of Self in Robert Lowell's Sonnet Reading Myself is the research statement. Jean Francois Lyotard proposed the idea of 'Delegitimation' of Grand Narratives in Modern Times (1). This concept of Delegitimation gives power to an individual to narrate her or his Self and gives complete control to have his power. The introspection of self in Robert Lowell's Sonnet is analysed critically in this postmodern sense. It aims at the liberation from the fixed system of beliefs or stereotypical norms of the society in writing a literary piece by analysing the lines of the sonnet in a postmodernist way. Specifically, the Sustainable Development Goal [SDG] of reducing inequality is examined through the poet's self in the paradoxical situation in a postmodern sense. It also questions the paradoxical existence and experiences faced by the poet in his life. The realisation of the self is significant in the present world gives the individual the freedom to create equal space for himself and others in society. The Electrochemical Society -
An Interrogation of Android Application-Based Privilege Escalation Attacks
Android is among the most widely used operating systems among consumers. The standard security model must address several dangers while still being usable by non-security users due to the wide range of use cases, including access to cameras and microphones and use cases for sharing information, entertainment, business, and health. The Android operating system has taken smartphone technology to peoples front doors. Thanks to recent technological developments, people from all walks of life can now access it. However, the popularity of the Android platform has exacerbated the growth of cybercrime via mobile devices. The open-source nature of its operating system has made it a target for hackers. This research paper examines the comparative study of the Android Security domain in-depth, classifying the attacks on the Android device. The study covers various threats and security measures linked to these kinds and thoroughly examines the fundamental problems in the Android security field. This work compares and contrasts several malware detection techniques regarding their methods and constraints. Researchers will utilize the information to comprehensively understand Android security from various perspectives, enabling them to develop a more complete, trustworthy, and beneficial response to Androids vulnerabilities. 2023 American Institute of Physics Inc.. All rights reserved. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Investigation on Machine Learning Models in Classification and Identifications Cervical Cancer Using MRI Scan
This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. During the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. 2024 IEEE. -
An Investigation on the Mechanical and Durability Properties of Concrete Structures Incorporated with Steel Slag Industrial Waste
The construction sector constantly looks for novel approaches to promote sustainability, minimize environmental impact and improve structural properties of construction materials. This work explores the incorporation of steel slag, a by-product from steel manufacturing industry, into concrete blocks. This research investigates the effects of steel slag on the mechanical strength and durability of the prepared concrete blocks, through a series of laboratory tests, including compressive, tension, flexure strength, water absorption and acid attack. This study evaluates the viability and feasibility of incorporating steel slag into concrete block production. In this study, samples of concrete mixture were set with 0% to 20% insteps of 5% steel slag as coarse aggregate. The findings show that concrete blocks consisting 20% of steel slag exhibited better compressive, tensile, flexural strength, reduction in water absorption and improved resistance to chemicals. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Investigation to the Hardness of the Cutting Tool During Machining Inconel 718 due to the Cryogenic Effect
The machining of superalloy Inconel 718 has seen a rapid demand in industries due to the superiority factor of its composition which makes it corrosion resistant, wear resistant and abrasive resistant. Due to these advanced features of this alloy, the cutting tool to be used to machine becomes a challenging one. There have been several cutting tools being used in the machine but wear of the tool and high surface roughness has been observed. Two cutting tools Tungsten Carbide RYMX 1004-ML TT3540 and Ceramic AS20 has been identified but the hardness on it is failed due to the machining conditions. The cryogenic treatment of these tools can see a remarkable change in machining and bring low surface roughness and reduce tool wear. 2023 American Institute of Physics Inc.. All rights reserved. -
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE.