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An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach
Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have been used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superiorto the widely usedBiCNN and RNN methods. 2023 IEEE. -
An Innovative Method for Enterprise Resource Planning (ERP) for Business and knowledge Management Based on Tree MLP Model
This strategy highlights the benefits of utilizing cutting-edge IT to back up company goals and genuinely assist in changing internal procedures by implementing an ERP-appropriate solution. Any organization, no matter how big or little, can benefit from an enterprise resource planning (ERP) system, which is an integrated suite of tools designed to streamline and improve internal business operations. Staying true to this approach will ensure that you get the greatest results while training the model, selecting features, and doing preprocessing. In order to use dense vector embedding for preparing the raw system logs, ERP system logs are typically represented by a combination of alphanumeric characters. While selecting features, SIM uses Particle Swarm Optimization (PSO) to create uniform product configurations. Using a Tree-MLP, the model was trained. This new strategy outperforms the old one, including Decision Tree and MLP. A 94.30% improvement in accuracy was achieved after implementing the technique. 2024 IEEE. -
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE. -
An Innovative Method for Housing Price Prediction using Least Square - SVM
The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM. 2023 IEEE. -
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 microscopy and analytical techniques for morphological, structural, chemical, and thermal characterization of cellulose
Cellulose obtained from plants is a bio-polysaccharide and the most abundant organic polymer on earth that has immense household and industrial applications. Hence, the characterization of cellulose is important for determining its appropriate applications. In this article, we review the characterization of cellulose morphology, surface topography using microscopic techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Other physicochemical characteristics like crystallinity, chemical composition, and thermal properties are studied using techniques including X-ray diffraction, Fourier transform infrared, Raman spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, and thermogravimetric analysis. This review may contribute to the development of using cellulose as a low-cost raw material with anticipated physicochemical properties. Highlights: Morphology and surface topography of cellulose structure is characterized using microscopy techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Analytical techniques used for physicochemical characterization of cellulose include X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. 2022 Wiley Periodicals LLC. -
An Insight into Photophysical Investigation of (E)-2-Fluoro-N-(1-(4-Nitrophenyl)Ethylidene)Benzohydrazide through Solvatochromism Approaches and Computational Studies
A fluoro-based Schiff base (E)-2-fluoro-N?-(1-(4-nitrophenyl)ethylidene)benzohydrazide (FNEB) has been synthesized from condensation of 2-fluorobenzohydrazide and 4?-nitroacetophenone catalyzed by glacial acetic acid with ethanol as the solvent. The dipole moment of FNEB in both the electronic states were found using different solvatochromic approaches such as Lippert-Mataga, Bakhshiev, Kawski-Chamma-Viallet, Reichardt and Bilot-Kawski. The experimental ground state dipole moment of FNEB was calculated using Guggenheim-Debye method and theoretical ground state dipole moment using Bilot-Kawski solvatochromic approach. The solvatochromic behavior of the Schiff base in different solvents was studied using absorption and emission spectra. Catalan and Kamlet-Abboud-Taft parameters were used from the multiple linear regression (MLR) analysis in order to study the solute-solvent interaction. The dipole moments were also calculated using Time Dependent-Density Functional Theory (TD-DFT). The chemical stability of FNEB was determined using computational and Cyclic Voltammetry by the use of obtained energy gap between the frontier orbitals. Using the frontier orbitals energy gap, global reactivity parameters were computed. Further, Light Harvesting efficiency was determined to comprehend the photovoltaic property of the Schiff base. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
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 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.