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Enhancing well-being: Exploring the impact of augmented reality and virtual reality
Virtual reality (VR) and augmented reality (AR) can revolutionize how individuals experience and perceive the world. Effective and engaging wellness practices are made possible by these technologies personalized, immersive experiences. The organization endeavors to foster empathy and understanding by attending to physical, mental, emotional, and social health. Nevertheless, ethical deliberations are of the utmost importance, including privacy, proper data handling, and secure data access. Education, support, and accessibility are critical determinants of user acceptance. Additional areas that warrant further investigation include treatment efficacy, diversity, long-term effects, and ongoing progress. A more inclusive, engaging, and productive approach to individual and communal health is anticipated due to the expanding use of AR and VR in well-being. 2024, IGI Global. All rights reserved. -
Enhancing Visual Passwords Using a Grid-Based Graphical Password Authentication to Mitigate Shoulder Surfing
Surfing Shoulder Surfing is a secret phrase-based attack which is a serious worry of protection in data security. Alphanumeric passwords are more helpless to attacks like shoulder surfing, dictionary attacks, etc., than graphical passwords. The creation of more muddled, challenging to-break passwords can be made simpler for clients with graphical authentication by consolidating the visuals and memory-based strategies like recall and recognition. In an imaged-based password, the user can choose pixels from the image to use as a secret key in the grid-based strategy, the user-selected image would show up on the screen with a framework overlay on it, and the client can pick explicit lattices to set their secret phrase. Besides, graphical passwords are powerless against shoulder surfing attacks, and due to this, clients are given a one-time made password via email. We investigated the limitations of image-based and grid-based authentication techniques and propose a grid-based graphical authentication system that addresses the limitations of image-based and grid-based techniques. The results of the grid-based graphical technique, as well as the image-based and grid-based approaches, have likewise been differentiated and analyzed. The convenience objective of our authentication system is to assist users in making better password selections, hence boosting security and broadening the usable password field. This method can be employed in many different contexts, such as forensic labs, banking, military, and other scenarios. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Enhancing Transparency and Trust in Agrifood Supply Chains through Novel Blockchain-based Architecture
At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demandsupply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains. Copyright 2024 KSII. -
Enhancing Traffic Incident Management and Regulatory Compliance Using IoT and Itms: A Mumbai Traffic Police Case Study
In the rapidly urbanizing landscape of Mumbai, a megacity confronted with significant traffic management and law enforcement challenges, the deployment of an advanced city surveillance system represents a transformative approach to urban governance. This paper examines the integration of over 11,000 CCTV cameras into the Mumbai Traffic Police's operational framework, covering an area of 438 square kilometers encompassing 41 traffic divisions and 94 police stations. Since its inception in 2016, the system has been pivotal in enhancing safety, order, and mobility within the city, especially amid obstacles such as ongoing infrastructure projects, traffic congestion, accidents, and natural disasters. Central to this study is the analysis of the Mumbai City Surveillance System Project (MCSP), which leverages CCTV technology to generate and classify Incident Reports (IR) based on severity, ranging from minor disruptions to significant emergencies. The period from October 2021 to 2023 saw a marked increase in IR generation, from 742 reports in 2021 to 10,392 in 2022 and 9,639 in 2023, indicating the system's growing efficacy in real-time traffic management and incident response.This paper further explores the cutting-edge integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies within the MCSP framework, highlighting the role of computational intelligence in enhancing the capabilities of Intelligent Transportation Systems (ITS). By employing AI-driven predictive analytics, the system effectively anticipates traffic conditions based on diverse variables such as traffic flow, vehicle speed, and weather, thereby optimizing traffic management strategies.The findings underscore the significant impact of AI and IoT technologies in redefining urban transportation networks, demonstrating improved efficiency, safety, and resilience in the face of Mumbai's complex transportation challenges. This study contributes to the discourse on smart city initiatives, offering insights into the role of advanced computational technologies in facilitating intelligent transportation solutions and shaping the future of urban living. 2024 IEEE. -
Enhancing the stability of electrochemical asymmetric supercapacitor by incorporating thiophene-pyrrole copolymer with nickel sulfide/nickel hydroxide composite
The practical application of a supercapacitor predominantly relies on its sustained cyclic stability. Hence it is essential to develop materials with high stability for the efficient supercapacitor applications. Herein, we demonstrate the integration of a copolymer of poly thiophene-pyrrole (cPPyTh) to surpass the limited cyclic stability of the nickel sulfide/nickel hydroxide (NSH) composite. Though the lower electronegativity of sulfur in coexistence with hydroxide achieves a superior capacity for NSH, it lacks extended cyclic stability. By incorporating cPPyTh into the layers of NSH, the stability of the resultant composite (NCP) could be enhanced by preventing the aggregation of layered NSH during longer runs. NCP electrode provides a specific capacity of 87 C/g at a current density of 1 A/g in a three-electrode system. An energy density of 25.47 Wh/kg and power density of 8.65 kW/kg is obtained for the asymmetric supercapacitor fabricated with NCP as positive and modified activated carbon (MAC) as negative electrode. The NCP demonstrates a superior cyclic stability of over 94% for 10,000 cycles in comparison to NSH with stability ? 73% over 5,000 cycles for the asymmetric supercapacitor. 2021 -
Enhancing the stability of DSSC by Co-activation of microwave synthesized TiO2 with biomass derived carbon dots
Dye-sensitized solar cells (DSSCs) that utilize natural dyes have garnered interest due to their low cost, eco-friendly manufacturing process, and competitive photovoltaic performance. However, their efficiency and stability issues have hindered their widespread implementation. To enhance their performance, this paper proposes a novel approach of modifying the photoanode with carbon dots (CDs) to align the band gap for easier carrier collection. The material properties were thoroughly characterized by examining their structural, morphological, optical, and electrical properties. In this study, titanium dioxide (TiO2) was synthesized using the microwave-assisted solvothermal method, while nitrogen-doped CDs derived from Citrus medica fruit juice were prepared using a simple hydrothermal treatment. Three sets of Natural Dye Sensitized Solar Cells (NDSSC) devices were created using co-activated photoanode (CD/TiO2) and unmodified photoanode (TiO2) with Platisol T/sp coated ITO serving as the counter electrode. Hibiscus (Hibiscus rosa-sinensis) and Onion (Allium cepa) peel extracts were utilized as sensitizers and Iodolyte HI-30 as the electrolyte. The most efficient device attained an efficiency of 3.5 % with Voc = 0.81 V and Jsc = 6.57 mA/cm2. This marks the highest efficiency reported using Hibiscus as a sensitizer with the current configuration, accompanied by prolonged device stability. This study showcases the potential of Citrus medica-derived nitrogen-doped CDs in achieving durable device stability. 2024 Elsevier B.V. -
Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. 2024 IEEE. -
Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems. 2023 Elsevier Ltd -
Enhancing the performance in education by implementing gamification
The gaming industry is growing rapidly in the present generation along with the advancement of technology. Gaming has captured all the young minds with its high and realistic graphics. What makes the gaming industry so attractive is that the players have complete freedom in the game. Freedom to fail, they can try until they succeed another feature is that game is user-centric. Consequently, a lot of research is been in the field of education to increase student's engagement towards studies. The main aim of this paper is to combine these game elements with learning to see if it yields better results. A quantitative approach is used to analyze the student's performance and interest in learning. Using these game elements in education will encourage the students to learn as well as have the flexibility to complete the course at their own pace. Copyright 2019 American Scientific Publishers All rights reserved. -
Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm
In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright 2023 Inderscience Enterprises Ltd. -
Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network
A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. 2022 Northeastern University, China. -
Enhancing the efficiency of parallel genetic algorithms for medical image processing with Hadoop /
International Journal of Computer Applications, Vol.108, Issue 17, pp.92-97, ISSN No: 0975-8887. -
Enhancing the digital consumer experience: The role of artificial intelligence
In the era of rapid technological advancement, the integration of artificial intelligence (AI) and digital consumerism has created new trends in business. Consumers are able to communicate with brands in new ways owing to developments in digital media. AI-powered recommendation systems, chatbots and virtual assistants are the main drivers of this change. It allows businesses to offer product recommendations, customer support, and personalized content to increase user engagement and satisfaction. This chapter provides real-world examples of AI applications across industries, highlighting success stories of companies using AI to create better value and customer satisfaction. Finally, the integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. 2024, IGI Global. All rights reserved. -
Enhancing the confidentiality of text embedding using image steganography in spatial domain
Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components. 2021 American Institute of Physics Inc.. All rights reserved. -
Enhancing the biodegradability and environmental impact of microplastics utilizing Eisenia fetida earthworms with treated low-density polyethylene for sustainable plastic management
Low-density polyethylene (LDPE) is widely used in food packaging and agricultural mulching, but its disposal generates macro, meso and microplastics that infiltrate the food chain and carry harmful substances. The present study aimed to improve remediation strategies for soils contaminated with LDPE and enhance the survivability of Eisenia fetida. The study dissolved LDPE in trichloroethylene and treated it with starch, hydrogen peroxide, nitric acid and acetic acid, initiating thermo-oxidative reactions. The treatment decreased LDPE's crystallinity index from 48.48% to 44.06% (single treatment), 44.06% to 40.02% (double treat-ment) and 40.02% to 32.98% (triple treatment), achieving a 15.5% reduction in crystallinity. LDPE microplastics with 40.02% crystallinity showed lower mortality rates in Eisenia fetida earthworms compared to those with 44.06% and 32.98% crystallinity and untreated LDPE. When introduced to E. fetida, microbiota in the earthworm casts included unidentified species from Pseu-domonas and Zoopagomycota, known polyethylene degraders. Microbial analysis of treated LDPE microplastics showed changes in gut microbiota, including potential degraders from Aeromonas and Malassezia restricta. XRD (X-ray diffraction techniques analyses) and FTIR(Fourier Transform Infrared Spectroscopy) analyses provided insights into distinct LDPE degradation patterns, identifying hydroxyl and carboxylic groups as functional groups. The study also investigated the ability of altered mi-croflora with treated microplastics to degrade LDPE, favouring decreased earthworm mortality rates. The crystallinity index of treated polyethylene further reduced from 40.02% to 23.58% after 21 days of exposure to E. fetida. This research advances the understanding of oxidised plastics' ecological impacts and will help to develop environmentally sustainable and biodegradable LDPE. Author (s). -
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The models performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future. 2023 by the authors. -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization
Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE.