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Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
Improved tomato (Solanum lycopersicum L.) growth and reduction of fungal pathogens utilising the plant growth-promoting and antifungal Bacillus albus NJ01 as a bioinoculant
Rhizobacteria that promote plant growth are crucial for improving the health, growth, and yield of plants. In this study, 14 isolates were obtained and the significance of Bacillus albus NJ01 as PGPR for the improvement of growth in tomato (Solanum lycopersicum) was assessed, as it showed plant growth-promoting traits like IAA, siderophores and ammonia production, phosphate and zinc solubilization, etc. Its role in increasing crop root and shoot length while avoiding the use of chemical pesticides and fertilizers was also studied. The root length of tomato control plants and plants treated with bioinoculant was found to be 5.58 0.15 and 7.98 0.24 cm, respectively. The shoot length of control plants and plants treated with bioinoculant was found to be 8.25 0.82 and 10.24 0.11 cm, respectively, therefore confirming the potentiality of Bacillus albus NJ01 bioinoculant as an able PGPR for improving the growth of tomato. 2025, Society for Advancement of Horticulture. All rights reserved. -
Improved tweets in English text classification by LSTM neural network
This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used. 2023 IEEE. -
Improvement in food preservation with nanozymes
To ensure safety, quality, and extended shelf life of perishable food products, food preservation is a critical aspect of food industries. Concerns regarding the potential health risks and loss of nutritional value of food because of traditional methods of preservation such as using chemical additives and high temperatures have set the need for finding alternative methods of preservation, for the betterment of health and the environment. Enzymes have the potential to kill microorganisms. Enzymes such as oxidases, peroxidases, hydrolases, catalases, and others have been extensively studied for their microbicidal activities. However, natural enzymes have shortfalls as they can be easily denatured and cannot be recycled. Nanozymes have gained the limelight in recent years as they can be applied in food industries to overcome the shortfalls of natural enzymes. They embody the highly beneficial properties of both enzymes and nanoparticles at the same time. Due to their enzyme-mimicking properties and versatile applications, nanozymes have become more popular in the last few years. Nanozymes have evolved as a promising alternative for food preservation and the detection of various contaminants in food. However, before the integration of nanozymes into the food industry, several factors such as their stability, biocompatibility, longevity, toxicity, cost-effectiveness, scalability, and regulatory approval need to be addressed. This chapter discusses the concept of nanozymes, its classification, and various applications in food industries specially designed for preservation of food products. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022. -
Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features
Speech emotion recognition (SER) is a dynamic area of research which includes features extraction, classification and adaptation of speech emotion dataset. There are many applications where human emotions play a vital role for giving smart solutions. Some of these applications are vehicle communications, classification of satisfied and unsatisfied customers in call centers, in-car board system based on information on drivers mental state, human-computer interaction system and others. In this contribution, an improved emotion recognition technique has been proposed with Deep Convolutional Neural Network (DCNN) by using both speech spectral and prosodic features to classify seven human emotionsanger, disgust, fear, happiness, neutral, sadness and surprise. The proposed idea is implemented on different datasets such as RAVDESS, SAVEE, TESS and CREMA-D with accuracy of 96.54%, 92.38%, 99.42% and 87.90%, respectively, and compared with other pre-defined machine learning and deep learning methods. To test the real-time accuracy of the model, it has been implemented on the combined datasets with accuracy of 90.27%. This research can be useful for development of smart applications in mobile devices, household robots and online learning management system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Improvement to Recommendation system using Hybrid techniques
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE. -
Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE. -
Improving crop production using an agro-deep learning framework in precision agriculture
Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. The Author(s) 2024. -
Improving EEG based brain computer interface emotion detection with EKO ALSTM model
Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based braincomputer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies. The Author(s) 2025. -
Improving Financial Audits and Management of Compliance using Artificial Intelligence and Secure Cloud Technology
Modern financial ecosystem requires highly complex audit trails and more stringent compliance issues therefore require highly advanced secure and intelligent systems. This research outlines a hybrid framework which juxtaposes Artificial Intelligence (AI) and Secure Cloud Technology to improve financial audit process and establish strong compliance management. Taking advantage of the strengths of AI, the strengths in question including Natural Language Processing (NLP), anomaly detection and machine learning classifiers, this system is used to enhance data accuracy, and detect irregularities in real time and automate regulatory reporting. At the same time implementation of Zero Trust Cloud Architectures, along with homomorphic encryption, provides data integrity, privacy, and end to end security. The proposed methodology is centred around the integration of intelligent document processing and blockchain-verified logs in the federated learning framework - where both transparency and decentralization are fostered. In addition, predictive analytics are used for the prediction of possible risks and non-compliance incidents to facilitate proactive decision making. Extensive simulations are used to reveal enhanced performance relative to traditional systems, with increased accuracy of anomaly detections, audit traceability, and validation speed-up of compliance. This integration is not only focused on streamlining audit workflows, but can also cut on operational cost and human error as well. The results emphasize the importance of employing AI-enabled secure cloud infrastructures as a primary strategy for financial institutions in a growing regulated digital economy while trying to sustain compliance. The new system achieves a 96.2% rate of accuracy while auditing and only consumes 91.3% the time in compliance to encourage efficiency. 2025 IEEE. -
Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset
Disasters significantly impact people's lives; among them, flooding is the worst common, and it causes sudden and secure damage to both lives and property. Addressing such real-time crisis demands intricate and sophisticated flood prediction models with enhanced capabilities. The development of efficient flood prediction models is often hindered by the lack of available datasets and the need for optimal feature. To address the challenge of data availability, in the proposed research, we have manually prepared a novel dataset by collecting data from NASA's (National Aeronautics and Space Administration) Power Project. The proposed dataset is experimentally evaluated and verified and has been organized into a balanced benchmark dataset with 33 features using the SMOTE algorithm. To enhance the provenance of flood prediction model, we propose a novel feature selection method. This method integrates outcomes from three different feature selection techniques to identify the most prominent features. The proposed feature selection method improves the model's performance and efficiency by identifying optimal predictors. Experimental results demonstrate that the artificial neural network trained with the selected relevant features accurately predicts flood occurrences, showing enhanced accuracy compared to state-of-the-art methods. 2026 John Wiley & Sons Ltd. -
Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. -
Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods
Super-resolution has advanced significantly in the last 20years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach
Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE. -
Improving maternal health by predicting various pregnancy-related abnormalities using machine learning algorithms
Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency. 2023 by IGI Global. All rights reserved. -
Improving organizational environmental performance through green training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023, IGI Global. All rights reserved. -
Improving Organizational Sustainable Performance of Organizations Through Green Training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour, in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance, and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023 IGI Global. All rights reserved. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024.