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Future perspectives on new innovative technologies comparison against hybrid renewable energy systems
The increase in the dispatchable amount of renewable energy and rural access to the point is proposed. The fuel is used to generate power and electrical energy for the machine. This causes the electricity to manage the single connection point to analyze the hybrid generations. Improving this hybrid generator of renewable power resources can be enabled for the analysis. Photovoltaic power sources have been introduced for converting the power loads and the dumps. The vehicle energy power management technique and the renewable energy system have been used for the analysis. This study shows how vehicle and renewable energy management can help develop geothermal against hydrothermal vents. Hydropower and vehicles can enable bioethanol for vehicle biodiesel. This study allows for the analysis of hydrothermal and biodiesel. In this study, the power of the energy enables the hybrid system, and the combination of the power generator to access the vehicle is proposed. 2023 -
Experimental Investigation of Salt Hydrate Phase-Change Material (Shape-Stabilized) Applied to a Solar Collector
A complex element of water heater by solar power involves the requirements of storage tank, which not only occupies considerable space but also adds com-plexity to the plumbing and installation procedures; this research marks the initial endeavor to practically utilize shape-stabilized (SS) phase-change material (PCM) within a tank-less, evacuated tube with direct absorption (ET-Direct Absorption) solar collector. The primary objective was to tackle challenge of storage of the solar power. A PCM (salt hydrate) was proposed, with different component concentrations explored to determine the most effective mixture. Once the optimal compound was identified, it underwent rigorous testing over numerous cycles to ensure its sustainable its storing capabilities. Additionally, the planetary system was charged in dormancy mode (without flow of water) and subsequently discharged at the rate of flows of 15, 25, and 35 liters per hour (LPH). Results indicated a note-worthy improvement in efficiency of the heat system in the stasis mode, which increases from 62 to 80% with the utilization of this heat storing cum collecting unit. Moreover, it was observed that transitioning from a rate of flow of 1525 LPH had minimal impact on the collec-tors heat gain, but using a rate of flow of 35 LPH sig-nificantly reduced efficiency of discharge. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
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Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks
Cyberthreat Intelligence (CTI) feeds serve as crucial resources for organizations seeking to fortify their defenses against emerging cyberthreats. However, these feeds often suffer from deficiencies such as incomplete data, false positives, and a lack of contextual information. This chapter proposes an innovative approach to address these challenges by leveraging Generative Adversarial Networks (GANs) to enhance CTI feeds. We introduce ThreatGAN, a novel GAN architecture specifically designed for cyberthreat modeling. Trained on accurate CTI data, ThreatGAN learns to generate synthetic yet realistic threat indicators, including malicious uniform resource locators (URLs), Internet Protocol (IP) addresses, and attack patterns. We demonstrate the efficacy of ThreatGAN in filling gaps in existing feeds, reducing false positives, and providing essential contextual information. The quantitative and qualitative evaluation shows that ThreatGAN significantly improves CTI quality. This technique can strengthen organizations cyber defenses by enabling them to work with higher quality, more complete Threat Intelligence. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. -
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. -
Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. 2024 Author(s). -
AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
The proposed research presents a two-fold approach for advancing Vehicular Ad-Hoc Networks (VANETs). Firstly, it introduces a Residual Convolutional Neural Network (RCNN) architecture to extract real-time traffic data features, enabling accurate traffic flow prediction and hazard identification. The RCNN model, trained and tested on real- world data, outperforms existing models in both accuracy and efficiency, promising improved road safety and traffic management within VANETs. Secondly, the study introduces a Genetic Algorithm-enhanced Convolutional Neural Network (GACNN) routing algorithm, challenging traditional VANET routing methods with metaheuristic techniques. Experiments in various VANET network scenarios confirm GACNN's superior performance over existing routing protocols, marking a significant step toward more efficient and adaptive VANET traffic management. 2024 Author(s). -
An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes. 2023 American Institute of Physics Inc.. All rights reserved. -
ELECTRIC EROSION MACHINING BEHAVIOR OF TITANIUM DIOXIDE PARTICLES REINFORCED WITH AL ALLOY COMPOSITE USING TOPSIS APPROACH
This research work describes the optimization of parameters in electric discharge machining (EDM) of AlFeSi (AA8011) alloy composite using the technique for order preference by similarity to ideal solution (TOPSIS) method. Initially, the AA8011 matrix alloy was synthesized with the addition of 10 wt.% TiO2 particles through the stir casting route. Scanning electron microscopy (SEM) was used to examine the morphology of the synthesized composite, which revealed that the TiO2 particles were evenly disseminated within the alloy. The machining factors, such as peak current (Ip), pulse-on time (Ton and pulse-o time (Toff), were chosen as input, whereas the material removal rate (MRR), the surface roughness (SR), and the tool wear rate (TWR) were selected as the output responses. According to the L9 (33array, the machining experiments were conducted using a brass (Br) electrode. By employing the TOPSIS method, the optimum combination of variables was determined. Based on the analysis, the Ip of 10 amps, Ton of 200 s, and Toff of 30 s provide the highest MRR (0.2379 g/min) with lower SR (3.284 m), and TWR (0.0258 g/min). ANOVA ndings exhibited that Ton was found to be the primary noteworthy factor contributing 50.67%, next by Toff (32.98%) and Ip (13.12%), respectively. Finally, the conrmation trials were carried out using the optimal parameters, which veried the predicted results. 2026 World Scientific Publishing Company. -
Markov based genetic algorithm (M-GA): To mine frequent sub components from molecular structures
Processing the molecular compounds to identify the internal chemical structure is a challenging task in bio-chemical research. Popular approaches, mine the frequent subcomponents from the molecules with chemical and biological properties represented in the form of feature vector histogram. Though this helps to identify the absence or presence of mined feature, calculating the frequency of every frequent substructure involves sub graph isomorphism test which is an NP-Complete process. To overcome the above mentioned bottleneck we proposed Markov based Genetic algorithm (M-GA) in which the chemical descriptors were considered from two-dimensional representations of molecules that classify chemical compounds using mining significant substructure and generates the binary vector that generate pure active classes, singleton reactors, descriptor sets. This method scales down the process of mining substructures that are statistically significant from huge chemical databases. The results shows that the performance of proposed algorithm is improved compared to the existing algorithms. 2020, Research Trend. All rights reserved. -
Economic and Urban Dynamics: Investigating Socioeconomic Status and Urban Density as Moderators of Mobile Wallet Adoption in Smart Cities
This research paper examines the complex correlation between socioeconomic factors, urban density, and the acceptance of mobile wallet technology in smart cities. The study investigates how socioeconomic status and urban density influence the adoption of mobile wallets. Smart cities have experienced a significant increase in the adoption of mobile payment solutions such as Apple Pay, and Google Pay, noted for their technological innovation and ability to enhance living standards. These digital payment platforms provide ease, security, and efficiency, revolutionizing how individuals engage in financial transactions and navigate urban environments. The study examines the many aspects that impact this phenomenon, focusing on the significance of comprehending how socioeconomic status and urban density influence the acceptance of mobile wallets. The study utilizes a meticulous research technique, which involves evaluating the reliability and validity of constructs, analyzing Heterotrait-Monotrait (HTMT) ratios, conducting tests for discriminant validity, and doing variance inflation factor (VIF) analysis. These measures are taken to ensure the strength and reliability of the report's conclusions. The research's importance is further supported by model fit statistics and hypothesis testing conducted through bootstrapping. The results emphasize that the inclusion of mobile wallet functions, the legal framework, and the development of smart city infrastructure have a substantial influence on the acceptance of mobile wallets. However, the impact of urban density on mobile wallet adoption is more intricate and multifaceted. This study provides significant insights into the dynamic field of technology uptake in urban regions, with implications for politicians, entrepreneurs, and urban planners seeking to promote financial inclusion and technological integration in smart cities. 2024 IEEE. -
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Adoption of enterprise risk management erm practices in the zimbabwean banking sector
Corporate failures that occurred in the mid-1990s as well as the global financial crisis that unfolded in the US in 2007 and subsequent banking crises in many countries underscored the need for banking institutions to develop and implement robust risk management systems and controls to prevent the occurrences of such crises. Enterprise risk management (ERM) has emerged as the best practice approach that provided banks with means for mitigating and controlling risks giving rise to such financial crises. Attempts have been made to find out the factors driving the implementation of ERM and the majority of these studies had conflicting newlineconclusions on the effect of some of these factors. Further, weaknesses were noted in variables used by researchers as proxies for ERM adoption. It was noted that several studies used the appointment of a chief risk officer as a variable representing ERM adoption while a number of other researchers focused on surveys or renowned frameworks such as COSO to ascertain the extent of adoption of ERM. These approaches however, had shortcomings. This study therefore sought to address some of the above gaps in literature. The purpose of this study is to determine the degree of adoption of ERM newlinepractices as well to examine factors (adequacy of risk governance structure, newlinequality of organizational culture, intensity of regulatory environment and size of the bank) influencing the adoption and implementation of ERM by banks in Zimbabwe. A mixed method approach was utilized in this study. The population of the study comprised of 18 commercial banks which have been operating in Zimbabwe since the adoption of the multi-currency system in 2009. Respondents for the study were selected using the purposive sampling approach. This was to ensure the respondents had the right experience and expertise to answer questions on enterprise risk management practices newlinewithin their respective banks. -
Correction: Effects of citric acid, ascorbic acid, and polyvinylpyrrolidone in overcoming medium browning during micropropagation of Phalaenopsis univivace (Horticulture, Environment, and Biotechnology, (2025), 10.1007/s13580-025-00729-4)
In this article the affiliation 2 and 3 have been corrected. Affiliation 2: "Department of Botany, Karnatak University, Dharwad 580003, India" to "CHRIST (Deemed to be University), Bangalore 580003, India" Affiliation 3: "Duy Tam University" to "Duy Tan University" The author name Thanh-Tam Ho was initially written as Than-Tam Ho. The original article has been corrected. The Author(s), under exclusive licence to Korean Society for Horticultural Science 2025. -
Effects of citric acid, ascorbic acid, and polyvinylpyrrolidone in overcoming medium browning during micropropagation of Phalaenopsis univivace
The most significant issues in the early phases of plant tissue culture are the browning of the explant and media. The objective of this research was to determine how well citric acid (CTR, 20 and 40 mg L? 1), ascorbic acid (ASA, 200 and 400 mg L? 1), and polyvinylpyrrolidone (PVP, 20 and 40 mg L? 1) could help Phalaenopsis Univivace overcome medium browning during the stages of shoot multiplication (MS medium supplemented with 150 mL L? 1 coconut water, 10 mg L? 1 adenine sulfate, 1 mg L? 1 thidiazuron, and 15g L? 1 sucrose) and rooting of shoots (MS medium with 25 mL L? 1 coconut water, 1 mg L? 1 indole butyric acid, 15g L? 1 sucrose). Numerous metrics were estimated, including the number of shoots that were regenerated during the shoot regeneration stage, the number of roots that were regenerated during the shoot rooting stage, and a number of growth parameters. The quantity of carotenoid and chlorophyll pigments, PSII quantum yield (Fv/Fm), the effective PSII quantum yield (YII), and the non-photochemical quenching (NPQ) were also measured. Additionally, oxidative stress enzyme malonaldehyde (MDA) and preventive antioxidant enzymes including catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) were measured in regenerated shoots and plantlets. The results showed that the medium browning issues during the shoot and root regeneration stages were resolved by supplementing with CTR, ASA, and PVP. In terms of shoot regeneration, rooting of shoots, and enhancing shoot and plantlet growth metrics, 40 mg L? 1 CTR supplementation was determined to be superior overall. The Author(s), under exclusive licence to Korean Society for Horticultural Science 2025. -
Effects of light-emitting diode (LED) light sources on in vitro protocorm-like body (PLB) proliferation, plantlet regeneration, and ex vitro acclimatization in Cymbidium Snow Pearl
One of the key environmental elements that influences plant growth in vitro is light quality. Currently, a variety of horticultural plants are regenerated in vitro using light-emitting diode (LED) light sources to produce healthy, high-quality plants that can adapt well to ex vitro transplantation conditions. Investigating the impact of various spectrum light sources at various phases of in vitro regeneration is essential, though. The objective of this research was to examine how red (R), blue (B), white (W), red plus blue (RB, 1:1), red, green (G), and blue (RGB, 1:1:1) LEDs affect the growth of protocorm-like bodies (PLBs), shoot regeneration, and the rooting stages of shoots. The findings showed that B-LEDs were accountable for PLB proliferation, whereas R-LEDs were responsible for increased shoot regeneration and improved growth matrices with shoots and plantlets as compared to other LED treatments. Plant height, leaf count, and dry matter percentage were all higher in the plantlets that were regenerated under R-LED. On the other hand, more root regeneration and longer roots were caused by the B-LED treatment. Plants cultivated under RB LEDs had greater levels of carotenoid pigments, total chlorophyll, chlorophyll a, and chlorophyll b. When compared to other treatments, photosynthetic fluorescence characteristics like maximum quantum yield of PSII (Fv/Fm), photochemical quenching coefficient (qP), relative electron transport in PSII (ETRII), and non-photochemical quenching (NPQ) were lower in plants cultivated under R-LED treatment. The best LED for in vitro Cymbidium Snow Pearl plant regeneration was as follows: The B-LED was good for PLB proliferation, the R-LED was appropriate during shoot regeneration, and the growth of plantlets, the physiological characteristics such as chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid content and number of epidermal cells per unit area were optimum with the plants grown under RB LED light. The Author(s), under exclusive licence to Korean Society for Horticultural Science 2026. -
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved. -
Leveraging gamification in the metaverse: Strategies for consumer engagement, innovation, and problem-solving across fashion industries
As the world adapted during the pandemic, virtual platforms became groundbreaking in terms of popularity, which brought forth the Metaverse, a transformative digital universe. This development is blurring the lines between gaming and the consumer internet and providing immersive, emotional, and socialized experiences. The variables driving deeper connections are technology readiness, user experience, and social influences. This study explores the effective use of animated agents in VR as an advertising strategy, linking findings to existing research. Much attention is given to gamification and VR, but little focus exists on how these experiences resonate with India's unique cultural context. The integration of Indian traditions into the Metaverse can revolutionize brand engagement, reshaping consumer perceptions and interactions. The paper discusses consumer engagement, readiness, and problem- solving, and it is based on the potential of culturally aligned VR experiences to transform industries, enhance connections, and create new avenues for brand immersion in the digital era. 2025, IGI Global Scientific Publishing. All rights reserved. -
Explainable AI for Secure and Trustworthy Autonomous Network Management
Rise of AI-driven autonomous networks for managing complex, dynamic infrastructures. While AI optimizes performance, it acts as a black box. This lack of transparency undermines trust and security, making it challenging to validate decisions, detect adversarial attacks, and understand why an AI model made a specific routing, security, or resource allocation decision. Security blind spots face significant challenges in detecting subtle adversarial manipulations or policy exploits because the reasoning behind the model's decisions is hidden. Additionally, poor diagnosability occurs when a network fault or performance degradation occurs, making root cause analysis slow and complex. Hence, the network operators are hesitant to cede control to systems whose actions they cannot verify or audit. Explainable AI (XAI) is critical for bridging this gap, ensuring management decisions are transparent, interpretable, and defensible. The proposed model makes real-Time management decisions. This model uses post-hoc techniques to generate explanations for each decision. It presents actionable insights and cross-references explanations against security policies and known threat patterns to flag anomalous reasoning. 2025 IEEE. -
Effects of bio-flocculated algae on the growth, digestive enzyme activity and microflora of freshwater fish Catla catla (Hamilton 1922)
In numerous ways, diets incorporating probiotics are beneficial to host animals. This study was conducted to evaluate the influence of bio-flocculated freshwater algae Chlorella vulgaris on the freshwater fish Catla catla. For the process of flocculating algae, probiotics Lactobacillus acidophilus (10307 MTCC) and Bacillus subtilis (MTCC 441) were used. The experimental fish were fed with Artemia franciscana enriched with flocculated algae for 60days. A control group was fed with unenriched A. franciscana. After the experimental period, there was a significant decrease in anaerobic bacteria and a significant colonization of candidate probiotics in guts of fish fed with flocculated algae-enriched Artemia. This treatment group also had a better growth performance with a higher average body length and weight (8.70.3cm, 5.830.9g) and survival % (981.02). High protease (7.8mg/protein?1) and lipase (2.56 mg/protein?1) activity were also found in the enriched A. franciscana-fed fish group. Comparatively, higher protein, lipid and PUFA/HUFA contents were also reported in this treatment group. The study found that flocculated algae-enriched A. franciscana has a positive impact on gut microflora, growth parameters and survival as compared to the unenriched group, and hence, the flocculated algae serve a dual purpose in rearing of C. catla. This study supports the inference that a bio-flocculated algae-incorporated diet is a preferable method for larval rearing aquaculture. 2020 John Wiley & Sons Ltd