Conference Archives is a dedicated platform that provides access to conference proceedings from academic and professional conferences held across the globe. It serves as a centralized repository for published research, enabling scholars, researchers, academicians, industry professionals, and students to explore a wide range of peer-reviewed conference papers. Our mission is to preserve and disseminate scholarly contributions by making conference proceedings easily accessible to the global research community. By bringing together publications from diverse disciplines, Conference Archives supports knowledge sharing, fosters collaboration, and promotes the exchange of innovative ideas. Whether you are looking for the latest research trends or exploring past conference publications, Conference Archives offers a reliable and organized resource for discovering high-quality academic content. Name: Conference Archives Established: 2026 Language: English Publication Mode: Online Subject Coverage: Multidisciplinary Source type: Conference Proceedings Publication Frequency: Continuous Publisher: Bonfring
ICLEAS 2026 (International Conference on Life, Environmental, and Applied Sciences) is a distinguished international conference that brings together researchers, academicians, industry professionals, scientists, and emerging scholars from around the world. The conference serves as a dynamic platform for presenting innovative research, exchanging knowledge, and fostering interdisciplinary collaboration across the fields of life sciences, environmental sciences, and applied sciences. Organized by Scientia Forum, ICLEAS 2026 is committed to promoting scientific excellence and encouraging meaningful discussions on contemporary challenges and emerging trends. Participants will have the opportunity to showcase their latest research, engage with leading experts, build global research networks, and explore collaborative opportunities that contribute to sustainable development and technological advancement. Through keynote lectures, technical sessions, paper presentations, and interactive discussions, ICLEAS 2026 aims to inspire innovative ideas, strengthen academic and industry partnerships, and support the development of practical solutions that address today's global scientific and environmental challenges. The conference welcomes contributions from diverse disciplines, making it an inclusive forum for knowledge sharing, professional growth, and impactful research.
Synthetic Biology Platforms for Scalable Biopharmaceutical Manufacturing
Synthetic biology platforms have emerged as a novel paradigm for large-scale biopharmaceutical production and offer significant benefits in terms of cost efficiency, speed, and customization. In this paper, synthetic biology will be discussed as an aspect of biopharmaceutical production, covering engineered microbial and mammalian cell systems and the metabolic engineering strategy. The main goal is to determine the ability of such a platform to execute large-scale production of biopharmaceuticals, such as monoclonal antibodies, vaccines and gene therapies. The methodology entails a detailed study of the existing methods of synthetic biology, including CRISPR-based gene editing, modular biosynthesis, and synthetic circuits, which provides precise control of the production pathways. An analysis of the case study is carried out, comparing the classical processes that entail bioreactors with synthetic biology processes. The major statistical indicators are that production costs are reduced by 30 percent and time to market is decreased by 40 percent when biopharmaceutical production is carried out using synthetic biology platforms rather than traditional approaches. Findings also suggest that synthetic biology platforms are capable of producing at a high yield with a reduced environmental footprint, and it is also feasible to create complex biologics with a better functional and stability profile. Also, these technologies facilitate the quick design of customized medications since they can create a manufacturing process that is flexible and adaptable. The conclusion points out the possibility of synthetic biology as a game-changing technology in the biopharmaceutical sector, its ability to deal with the existing manufacturing issues, enhance drug access, and decrease the cost of production, which will eventually change the world's healthcare systems.
Multimodal Earth Observation Using Deep Neural Fusion for Real-Time Environmental Change Detection
Environmental monitoring is critical for assessing climate change, deforestation, urban expansion, and other ecological changes. The conventional Earth observation techniques tend to use single-modal sources of data like satellite images, radar, or sensor networks, each having its limitations in terms of spatial resolution, temporal frequency, and coverage. The current paper suggests a multimodal Earth observation model that can be used to combine satellite imagery with radar/LiDAR and sensor data to detect environmental changes in real-time. With a combination of these various sources of information, our method will improve the change detection of the system and give it more time resolution. A deep neural fusion model is created to unite the capabilities of these modalities, allowing for the recognition of change efficiently even in adverse conditions, like the cloudy sky or areas with limited sensor information coverage. The model is trained on high-resolution satellite images, SAR (Synthetic Aperture Radar) data, and ground sensor measurements. Experimental evidence demonstrates that our system can work better than the conventional single-modal systems, as it has 96 percent change detection accuracy with a small processing time. The multimodal fusion approach proves to be useful in real-time monitoring of the environment, as it has proven beneficial in both the accuracy of detection and the responsiveness of the system, which are important attributes in disaster management, urban planning, and climate studies.
Leveraging Edge Computing for Real-Time Monitoring and Disaster Management in Flood-Prone Areas
Floods can be dangerous to human lives, infrastructure, and economies, not to mention that in flood-prone regions, fast monitoring and disaster management are essential. Slowness in data processing and decision-making usually curbs the traditional flood monitoring processes. In this paper, the integration of edge computing with real-time flood monitoring systems is examined for use in disaster management in the region. The benefit of edge computing is that it brings computational capabilities nearer to the source of the data, which allows quicker processing of the data, reduces latency, and improves the efficiency of flood prediction and mitigation techniques. The offered system uses sensors, IoT, and edge computing platforms to gather and analyze the generated real-time data, and issue timely flood warnings, assisting the emergency reaction. The paper incorporates design and implementation of edge-based architecture, mathematical models on flood prediction, and data processing algorithms. Performance of the system is evaluated based on a number of different metrics, including prediction accuracy and response time, and compared with traditional methods. The results indicate that edge computing can significantly enhance the results in flood detection, early warning systems, and disaster management. The paper will help in the development of the field of flood management by providing a robust, scalable solution to monitor floods in flood-prone areas in real-time.
Edge computing; real-time monitoring; disaster management; flood-prone areas; IoT; flood prediction; early warning system.
AI-Powered Waste Management Systems for Urban Sustainability and Zero Waste Goals
The blistering pace of urbanization in the world has created a massive amount of municipal solid waste, overwhelming the old methods of disposing of the waste and hindering the development towards the goal of sustainability. The present paper explores the strategic combination of Artificial Intelligence (AI) and the Internet of Things (IoT) in order to aid a shift towards the traditional linear models of waste into current circular and zero-waste models. The study suggests a multi-tiered innovative system with the application of advanced Convolutional Neural Networks (CNNs) to do real-time automated waste sorting, and Deep Reinforcement Learning (DRL) to optimize collection routes dynamically. The system detects the various types of waste at the disposal location by using a network of high-precision smart sensors and, in the process, eliminates contamination of the recycling streams. The suggested model is characterized by a significant decrease in operational costs and carbon emissions compared to the usual heuristic scheduling approaches. This research adds a strict mathematical framework for optimizing bin-level logistics and offers a systematic analysis of the performance of AI in fulfilling the urban zero-waste goals. By examining the concepts of accuracy, precision, and logistical efficiency, the results can indicate that the purity of recycling can be improved by more than 30%, and users can reduce the number of unnecessary vehicles idles using a combined AI-IoT solution. Finally, the study offers a technical roadmap that can be scaled by city leaders to increase the resilience and environmental care of cities by using data-driven environmental intelligence so that waste can be used as an asset instead of a liability.
AI, IoT; sustainability; waste-management; optimization; zero-waste.
AI-Driven Bioinformatics Framework for CRISPR-Based Genetic Analysis
Modern advances in gene editing technologies, such as CRISPR, have revolutionized researches in genes and their applications. However, CRISPR systems are so complicated that the need for a good bioinformatics system arises in order to process high-scale genomic data. In the paper, an AI-based bioinformatics system for CRISPR-based genomics is presented. First, the problem addressed by the bioinformatics system is to detect CRISPR targets and predict off-target effects for their accurate and safe gene editing. The system methodology includes the use of machine learning algorithms for predicting CRISPR targets and off-target binding, as well as deep learning for increasing gene-editing effectiveness. Statistical data obtained after analyzing datasets were utilized in order to enhance the performance of the model. Important metrics like sensitivity, specificity, and accuracy have been used to measure the effectiveness of the proposed model. The results show that the prediction accuracy is considerably higher than the existing models in the field by 15 per cent. Furthermore, the model proves to be better than the existing techniques in identifying potential genetic alterations while minimizing off-target effects. All in all, the proposed model could contribute to the effectiveness and precision of genetic analysis with CRISPR system, making it an effective tool for genetic specialists. The proposed model makes the process of gene editing easier and faster, allowing genetic researchers and practitioners to conduct their investigations much quicker and with less risk. The future research direction could be in expanding the scope of the model's application in the field of genomics and improving its efficiency in processing genetic data.
AI-driven bioinformatics; CRISPR; genetic analysis; gene-editing; machine learning; off-target effects; predictive modeling; deep learning; genomic data; precision medicine.
Federated AI Models for Cross-Border Environmental Risk Assessment and Policy Simulation in Transnational River Basins
Transboundary river basins present some of the most difficult environmental governance scenarios, characterized by the presence of multiple data sovereignty regimes, diverse hydrologic behavior, and conflicting national policy targets impeding harmonized risk management. Traditional watershed models assume that transnational data collaboration is simply a yes-or-no issue, requiring riparian countries to choose between transparency and sovereignty. This paper proposes FedRiverAI, a hierarchical architecture of federated artificial intelligence that integrates differential privacy preserving cooperative machine learning with the semi-distributed process-based hydrologic modeling (SWAT), and decision-making layers based on agent-based approaches for transboundary environmental risk assessment and policy analysis while avoiding the transmission of sensitive unprocessed data across national borders. FedRiverAI allows each nation in the river basin to build its risk model based on the sovereign hydrological, agricultural, and ecological data that are combined in the global system for simulations under the LOC function. Experiments conducted in the basins of Mekong, Niger, and Indus transboundary river systems show that FedRiverAI (i) attains risk-prediction accuracy within 4.2% of an ideal fully centralized system and simultaneously provides differential privacy guarantees (ε ≤ 1.5), (ii) identifies cross-boundary ecological infringements 2.1 times faster than isolated country-specific approaches, and (iii) discovers that high levels of cooperation beyond LOC = 0.7 decrease basin-wide eco-hydrological indicator infringements by 28%-37%, with positive impacts on food and energy security. The present work contributes to the emerging field of socio-hydrology and federated AI at its intersection, developing a data-sovereign architecture for environmental governance in accordance with UN SDGs 6, 13, and 17.
Federated learning; transboundary river basins; environmental risk assessment; agent-based modeling; differential privacy; policy simulation; socio-hydrology.
Biohybrid Urban Infrastructure Systems Incorporating Living Materials for Passive Mitigation of Urban Heat Islands and Improved Air Quality
Cities are rapidly growing, and as such, they face two important environmental concerns: urban heat islands (UHIs) and declining air quality. This research presents an innovative Biohybrid Urban Infrastructure System (BUIS), which is made up of a combination of living materials in the built environment (e.g., green roofs, vertical bio facades, spirulina algae bioreactor panels, mycelium composite thermal tiles, and urban tree canopy networks) with the integration of IoT sensing and machine learning control systems into one passive mitigation framework. The analysis uses previously validated datasets from Singapore, Phoenix, Cairo, Mumbai, Lagos, and Bangkok to demonstrate that the proposed system will produce average reductions in mean surface temperature of 2.8–5.1 °C from unconditioned baselines when compared to typical measured mean surface temperatures for UHI mitigation made by common 1-layer green roof systems, with an average net difference of 1.9 °C. The average annual concentrations of PM₂.₅ and NO₂, respectively, show a decrease of 28 % and 31 % across the six (6) monitored locations. A longitudinal carbon sequestration analysis shows a cumulative removal of 31.5 tCO₂ per 1,000 m² over a six-year operation period. Analyses of energy usage show aggregate savings of 24.7 % and, on average, 31.5 % of the cooling requirement from the total operating cost of the building(s). The BUIS design, supported by two (2) schematic design representations, consists of five interdependent layers: environmental sensing; living material modules; AI-facilitated analytics; infrastructure integration; and sustainability tracking. Statistical modeling validation (R² = 0.972 and RMSE = 0.148 °C) provides confirmation of high predictability. Thus, BUIS can be considered as a scalable and replicable solution that is in alignment with United Nations Sustainable Development Goals 11, 13, and 15, promoting livable, climate-resilient urban areas.
Urban heat island; biohybrid infrastructure; living materials; green roof; algae bioreactor; mycelium composites; air quality improvement.
Integrated Hybrid Space-Earth Observation Platforms Combining Satellite Remote Sensing and Ground-Based Sensors for Early Detection of Extreme Climate Events
Natural hazards like floods, cyclones, heat waves, and droughts can be detrimental to human life, physical infrastructure, and natural ecosystem. Existing methods used for monitoring climate events based solely on satellite and/or ground-based observations fail to provide the adequate spatial-temporal resolution needed for efficient event detection. In this work, an Integrated Hybrid Space-Earth Observation System is introduced to leverage data from multiple satellites (e.g., optical sensors, SAR sensors, thermal sensors, GNSS) along with ground observations (weather station, hydrological sensors, soil moisture sensors, flux towers, IoT devices) to improve the efficiency of detecting/predicting climate events. The proposed framework utilizes a six-layer architecture which includes the following components: multi-source data collection, Edge computing, Core Data Processing, Deep Learning-based analysis, Early Warning dissemination, and Feedback Loop. The evaluation metrics indicate that the hybrid system surpasses the performances of the single source systems, attaining high precision, recall, and F1-score, with longer lead times and fewer false positive and false negative results. Through ablation analysis, it was discovered that the combination of satellite and ground-based observations improved detection accuracy and reduced mean detection time in addition to boosting its resistance to environmental variance. Accordingly, it is suggested that climate monitoring applications incorporate multi-sensor fusion and AI-based analytics to ensure timely alerts and informed decision making. Through its operational setup, the system allows for the quick communication of relevant information for decision-making purposes.
Hybrid space-earth observation; extreme climate events; multi-source sensor fusion; deep learning; early warning systems; real-time monitoring; climate resilience.
Personalized Longevity Healthcare Through Artificial Intelligence and Predictive Modeling for Aging Populations
Population aging across the world is gaining momentum, whereby people aged 60 years and above will make up 2.1 billion, which is 22% of the total global population. Population aging results in the increased burden of chronic diseases, multimorbidity, and increased healthcare expenditure. Traditional “fit-for-all” models used in delivering geriatric care lack the capability of delivering proactive and personalized medical interventions. This research introduces a personalized longevity care system using AI, which will use data from electronic clinical records, sensors, lifestyle habits, and biomarkers. The proposed model uses deep learning for time-series predictions, clustering for patient segmentation and risk stratification, and SHAP (explanatory AI). Synthetic cohort study involving 5,000 elderly people was conducted using synthetic simulation of demographic and epidemiologic distributions to assess model performance. According to the results, the presented approach demonstrated 91.3% prediction accuracy of early onset of chronic disease, increased the accuracy of risk stratification by 27% as compared to conventional statistical approaches, and helped reduce preventable hospitalization by 32% due to early warnings. The personalized health care plans contributed to increasing the adherence to the prescribed medication and lifestyle changes by 25%. Therefore, the suggested framework may allow transitioning from treatment to proactive health care management, which will help use resources more efficiently and improve patients' quality of life. Through integration of medicine, data science, and public health, the presented model corresponds with the principles of lifespan innovation.
Artificial intelligence; personalized longevity care; aging population; predictive healthcare; wearable technology; chronic disease.
Hydro-Informatics Decision Engines Integrating Remote Sensing and Predictive Modeling for Water Resource Management
Among other environmental degradation phenomena, the rising demand and the alteration of the weather conditions have put the water resource management on a higher pedestal of a crucial factor. In order to overcome these challenges, successful decision-making instruments are created as a result of the combination of hydro-informatics and new technology including remote sensing and predictive modeling. The article is considered a thorough method of managing water resources, which integrates predictive modeling method along with the utilization of data acquired through satellites, creation of Hydro-Informatics Decision Engines (HIDE). This is all aimed at maximizing the use of water, maximizing the utility of water resources projections and making the process of controlling water resources sustainable. The prediction models to be constructed are the remote sensing data (precipitation, soil moisture, and vegetation index) that would be collected as the first phase of the multi-phase methodology. Machine learning algorithms on historical and real-time inputs are used to model water availability, quality and demand in the future. To demonstrate how much the predictions comply with the data obtained on the ground, statistical analysis is used. The prediction error was reduced by about 15 percent compared to traditional methods indicating that the accuracy of forecast of water resources is greatly improved. Finally, one can have an optimization of the utilization of hydro-informatics in conjunction with remote sensing and predictive modeling in the decision-making process concerning water resources. In the current paper, the authors have indicated the way in which these technologies can be applied to help solve the issue of water shortages in sustainable management practices. It also points to the way these technologies are to be employed to make the water resources resistant to the climate unpredictability.
Hydro-informatics; remote sensing; predictive modeling; water resource management; satellite data; machine learning; water scarcity; decision engines; forecasting; climate change.
Track 1: Engineering Innovations and Smart Technologies Track 2: Scientific Advancements and Emerging Technologies Track 3: Contemporary Management and Data-Driven Strategy
The International Conference on Intellectual Research in Engineering, Science, and Management is a premier international academic conference dedicated to fostering innovation, interdisciplinary collaboration, and the advancement of research across engineering, science, and management. Bringing together academicians, researchers, scientists, industry professionals, and students from around the globe, the conference provides a vibrant platform for the exchange of ideas, research findings, and emerging technologies. Organized by AVSD Groups, a non-profit organization committed to promoting academic and research excellence since 2009, “The International Conference on Intellectual Research in Engineering, Science, and Management” was held virtually on May 29, 2026. The conference aims to encourage meaningful discussions on contemporary challenges, technological advancements, and innovative solutions that drive scientific and industrial progress. Through keynote addresses, technical sessions, paper presentations, and interactive discussions, The International Conference on Intellectual Research in Engineering, Science, and Management offers participants valuable opportunities to present their research, connect with international experts, establish collaborative partnerships, and gain insights into the latest developments in engineering, applied sciences, and management. By promoting interdisciplinary knowledge sharing and global networking, the conference seeks to contribute to academic excellence, sustainable innovation, and real-world impact across diverse fields of study.
Mindfulness Practices for Fostering Resilience and Innovation in Operations Management in VUCA and BANI Environments
The accelerating pace of change and the growing complexity in the current business environment are becoming more uncertain and bifurcated, often characterized by the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) and BANI (Brittle, Anxious, Nonlinear, Incomprehensible) models. These aspects put pressure on the traditional ways of dealing with operations management, especially in promoting resilience and spurring innovation. The paper discusses how mindfulness practices can help in building resilience and innovations in operations management, that is, in VUCA and BANI environments. The fundamental aim is to study how the mindfulness practices may be used to assist operations managers to adapt to a turbulent environment and increase their ability to be innovative in organizations. It takes a qualitative approach that applies a mixture of case studies and interviews of senior operations managers in the various industries. The results show that meditation, focused attention, and self-awareness are some of the mindfulness practices that can help managers make better decisions when faced with pressure, lessen stress, and create a culture of innovation through promoting creative problem-solving. Also, organizations that incorporate mindfulness in their operations management methods have greater resilience, as seen through enhanced adaptability and responsiveness to disruptions. The current study sheds light on the fact that mindfulness is a potent tool in managing the complexity of today's operations management and has practical implications for managers looking to make their organizations highly agile and innovative. The paper requests more research into mindfulness as a fundamental competence of uncertainty management and achieving sustainable innovation in the sphere of operations.
Mindfulness; organizational resilience; innovation; operations management; VUCA; BANI.
Mindfulness and Positive Psychology for Enhancing Well Being and Performance in Neurodiverse Individuals in Educational Settings
The paper will discuss the combination of mindfulness and positive psychology interventions in improving the well-being and academic achievement of neurodiverse people in learning institutions. The concept of neurodiversity, like autism, ADHD, and dyslexia, poses some special problems to the learners, as it restricts their performance in the normal education setup. But studies have proposed that psychological interventions of promoting well-being can help a great deal. Mindfulness, which can be described as mindfulness without any judgment of the current state, and positive psychology, which primarily aims at embracing strengths, resilience, and self-improvement, are both evidence-based practices that have the potential to improve student performance. The research design is a mixed-method design, which also incorporates both qualitative interviews conducted with teachers and neurodiverse students and quantitative examination of performance data before and after the intervention. Preliminary results indicate that the integration of mindfulness exercises and positive psychology interventions creates a great impact on the regulation of emotions, reduction of stress, self-esteem, and general student performance. The paper presents a case in support of the significance of such holistic interventions in education policies to establish an inclusive and supportive version of the learning environment for neurodiverse learners. The research implications include that there is a need to integrate mindfulness and positive psychology in educational systems in a systematic manner to promote well-being and improve learning outcomes among neurodiverse students.
Mindfulness; positive psychology; neurodiversity; well-being; educational performance; inclusive education.
Exploring the Role of Mindfulness in Enhancing Emotional Intelligence and Leadership in the Business World
Leadership effectiveness and emotional intelligence (EI) are other aspects that have been of key importance in determining organizational success in the fast-changing business world. Although the notion of mindfulness as a psychological practice has become popular because of the possibility of improving individual well-being, the impact of this practice on emotional intelligence and leadership in the business domain is under-researched. The paper explores the connection between mindfulness and emotional intelligence, and how the two aspects can be used to achieve better leadership performance in the workplace. Study’s mission is to fill this gap by questioning how mindfulness training might help individuals become more emotionally aware, regulated, and interpersonally skilled, which are the key elements of emotional intelligence. This research is a quantitative survey with mixed methodology using quantitive studies to assess mindfulness, EI, and leadership effectiveness, and qualitative interviews to enable the understanding of the experiences of business leaders. Early evidence indicates that there is a positive correlation between mindfulness practices and emotional regulation and empathy, which eventually lead to better leadership performance. Mindful leaders have a stronger capacity to deal with stress, demonstrate adaptive leadership styles, and provide facilitating work conditions, which eventually translate into better organizational performance. The implications of this study can be that mindfulness training programs have the potential to be a useful tool in developing EI and leadership within the business, and they will provide a useful intervention in the development of leadership. This paper is relevant to the current debate on leadership development by linking mindfulness to the fundamental leadership competencies and offering an idea to practitioners who want to maximize business performance by applying emotional intelligence.
Mindfulness; emotional intelligence; leadership; business performance; organizational behavior.
Leveraging Mindfulness for Ethical Decision Making in Finance and Investment Strategies
Ethical dilemmas in the finance and investment industry include conflicts of interest, pressure to make short-term decisions, and cognitive biases, which may render decisions unreliable and lead to suboptimal outcomes. Such ethical concerns not only affect individual investors but also the entire financial system. Mindfulness, as a practice based on present-moment awareness and non-judgmental acceptance, has been proposed as a possible intervention to alleviate these struggles by increasing self-awareness and emotional control. This paper provides an insight into how mindfulness can influence ethical decision-making in the finance and investment strategies environment. The study explores how mindfulness practices may help financial practitioners and investors identify and address biases, enhance judgment, and encourage ethical decision-making. Adopting a conceptual approach, this paper combines theories of mindfulness and ethical decision-making with behavioral finance to develop a framework for mindfulness as an ethical decision-making tool in finance. The methodology consists of an empirical review of studies on mindfulness interventions and decision-making, and a theoretical analysis based on existing models of financial ethics and mindfulness. The results conclude that mindfulness is an essential tool for reducing cognitive biases, encouraging ethical conduct, and enhancing investment techniques by making people more aware and self-regulated. The implications for financial professionals and organizations are discussed at the end of this paper, and practical recommendations for integrating mindfulness practices into the financial decision-making framework are provided.
Mindfulness; ethical decision making; behavioral finance; investment strategy; cognitive bias; financial ethics.
AI-Based Cloud-Edge Collaboration for Enhanced Cybersecurity in Industrial IoT
The rapid proliferation of Industrial Internet of Things (IIoT) systems has posed significant cybersecurity challenges, as these devices are interconnected and distributed. Conventional security controls, which tend to be fixed and centralized, do not suffice to meet the dynamic, real-time needs of IIoT environments. The proposed paper presents a novel AI-assisted Cloud-Edge Collaboration Model to improve the cybersecurity of IIoT systems by leveraging edge computing to detect threats with low latency and cloud computing to scale and perform advanced analytics and threat intelligence aggregation. The proposed model is evaluated on the TON-IoT Network Intrusion Dataset, and the key performance metrics include Detection Accuracy, False Positive Rate (FPR), False Negative Rate (FNR), Response Time, and Scalability. The Proposed Model demonstrates significant improvements, with 98.6% detection accuracy, dramatically outperforming the Existing Model, with 90.5%. Also, the FPR decreases to 3.2 (instead of 7.5), and the FNR decreases to 2.4 (instead of 6.8), indicating improved performance in identifying true threats and reducing false alarms. The Proposed Model also shows that it has significantly shortened response time, which was 10-15 seconds in the Existing Model, to 3-5 seconds and is thus able to mitigate security threats faster. In addition to that, the number of devices supported by the Proposed Model is 500 while the Existing Model only supports 50 devices. These findings show the efficiency of combining AI, cloud, and edge computing in offering scalable, accurate, and efficient cybersecurity solutions for IIoT systems.
Industrial internet of things (IIoT); cybersecurity; artificial intelligence (AI); cloud-edge collaboration; intrusion detection; edge computing.
Hybrid Biometric Systems for Enhanced Security in IoT and Cloud-Based Applications Using Deep Learning and Signal Processing
The fast growth of the Internet of Things (IoT) and cloud computing services has led to the need to have sophisticated security architectures to ensure that sensitive user information is secured across a network of distributed devices. The single-factor authentication method, e.g., passwords or basic tokens, is becoming more susceptible to advanced cyber-attacks, such as phishing, credential stuffing, and brute-force attacks. The hybrid biometric authentication system suggested in this study will combine physiological characteristics, deep learning, and signal processing methods to offer a high-security level. The study builds on a multimodal solution, which means integrating facial recognition and signal-based biometrics to address the shortcomings of unimodal solutions to vulnerabilities to spoofing and presentation attacks, as well as external noise. The hybrid deep learning architecture that is used in the methodology is that of the Convolutional Neural Networks (CNN), which is used to extract features and process the signal to detect liveness and reduce noise. The experiments on virtual data show that the given hybrid model is the most effective one, with an accuracy of authentication of 98.6, which is much higher than the results of the traditional methods, with an average of 90-92. In addition, the system has a 15.8 percent lower rate of false acceptance than the old structures. The statistical analysis proves that signal-to-noise ratios of biometric data are minimized by the integration of signal processing by 12 percent, which increases the reliability of the signal processing in edge clouds. This study finds that hybrid biometric systems optimized through deep learning offer a scalable, secure, and user-friendly solution to the continuously changing IoT. Based on the current peer-reviewed sources, this study creates a standard of multi-step authentication algorithms in securing data storage and real-time identity control in high-stakes applications such as healthcare and banking. The framework proposed shows resistance to adversarial threats of the present-day world.
Biometric security; IoT; cloud computing; deep learning; signal processing; multimodal authentication; hybrid encryption.
Advanced Mathematical Models for Predicting Complex System Behavior in 6G Networks
The research indicates that the rapid evolution of wireless communication technologies has prepared the way for the creation of 6G networks that are likely to introduce revolutionary features, including ultra-low Latency, enormous connectivity, and AI-controlled networks. Also, artificial intelligence (AI), machine learning, and Internet of things (IoT) are the technologies which will be employed in such networks due to the dynamics and complexity nature of such networks. Nevertheless, it is not easy to make any predictions regarding such networks owing to the many factors that determine their performance. This study seeks to address the above issues through the formulation of complex mathematical models that make it easy to predict the behavior of the networks. The suggested mathematical models will incorporate machine learning algorithms together with traditional mathematical modeling to ensure precise predictions. The suggested models turn out to operate better with respect to the performance criteria due to their more accurate performance (95% vs. 88%), a reduced delay (10.5 ms vs. 15.2 ms) and increased throughput (1.2 Mbps vs. 0.85 Mbps). It is clear from this data that the suggested models will prove to be useful in terms of prediction and optimization of the 6G network and thus they can be utilized in the real-time management of the network. As a result of this research, more efficient, resilient and intelligent 6G networks have been developed and this research contains many important aspects that can be applied in the future.
6G networks; mathematical modeling; AI; machine learning; network prediction; resource optimization; real-time management.
Designing Quantum-Enabled Intelligent Systems for Sustainable Energy Solutions
The increasing demand for energy, in addition to the problems posed by conventional energy systems to the environment, necessitate the implementation of energy systems which are more efficient and sustainable. The introduction of alternative energy sources such as solar, wind and hydro-energy has become increasingly important in addressing such challenges; however, due to the nature of renewable energy resources, current energy systems face inefficiencies and inconsistencies in incorporating them into their operations. In this paper, a new Quantum-Enabled Intelligent Energy System will be introduced. The system makes use of quantum computing and artificial intelligence capabilities in order to maximize the performance of the energy system. In particular, the system aims to address the most significant issues of energy distribution, load balancing and prediction of renewable energy sources. By taking advantage of the computational power of quantum computing and prediction capabilities of artificial intelligence, the proposed system can greatly enhance energy efficiency and energy system performance. However, the outputs of the proposed model can be considered rather impressive – for example, a 15 % reduction in energy consumption, 5 % improvement in system reliability, and a 20 % improvement in throughput and renewable energy usage. Even though the quantum-enabled approach takes 25% more time to optimize (from 40 seconds to 50 seconds), the advantages of the overall performance in regard to energy management and sustainability are significantly higher. In addition, issues related to implementation (hardware limitations, computing speed, compatibility with the existing energy systems) are highlighted, and future research is suggested for improvement of scale, privacy of the data, and energy systems optimization with the use of quantum technology.
Quantum computing; artificial intelligence; energy optimization; smart grid; renewable energy; load balancing; system reliability.
AI-Driven Crop Disease Detection and Prediction Using Convolutional Neural Networks (CNNS) for Sustainable Agriculture
Crop diseases are a major challenge to agricultural productivity in which the end result is normally very little yield and loss of money. Conventional disease identification techniques, such as hand inspections and pesticide spraying, are slowly becoming ineffective, particularly in large-scale agriculture. The given paper introduces an AI-powered solution based on Convolutional Neural Networks (CNNs) to detect and predict agricultural diseases at the initial stage, which will help enhance the sustainability of agriculture. The new model is innovative because it will work on the principle of identifying diseases at an early-stage using the capabilities of CNNs to process high-resolution images of crops and predict the spread of the disease successfully. The methodology will involve gathering images of the publicly available crop disease datasets, then the data will go through the data preprocessing strategies that include image augmentation, image normalization, and image labeling to make the data ready to train the model. The CNN model is trained to classify the images into healthy and diseased and also gives a prediction on whether the disease will spread in the future on the basis of the current state of the image. Accuracy, precision, recall, and F1-score are some of the key metrics that are used to evaluate the performance of the model. The findings indicate that performance is high and the model obtains 95% accuracy, 92% precision, and 90% recall, which is much better compared to the traditional methods. Being an AI-based system, this solution is efficient, reliable, and eco-friendly to manage crop diseases, allowing farmers to take timely measures and minimize the use of pesticides. The paper closes by pointing out the possibility of real-world implementation with respect to large-scale agriculture and outlining the need to conduct additional research to enhance the model's generalization to various crops and environmental conditions. Also, the incorporation of real-time information may improve the predictive ability of the model.
AI; convolutional neural networks; crop disease detection; sustainable agriculture; image classification; precision agriculture; disease prediction.
Optimizing Edge Network Performance with Software-Defined Technologies for Secure 6G Integration
An SDN-based edge network optimization framework incorporating AI is proposed to improve performance and security in 6G networks. Software-Defined Networking (SDN) and edge computing can be combined to enable dynamic resource allocation and thus optimize throughput, decrease latency, and minimize packet loss. Algorithms based on AI, especially reinforcement learning, can control network traffic most effectively and allow real-time adjustments to network settings based on current needs. This integration will allow the system to quickly adjust to the conditions and optimize performance independently. Also, the AI-enabled security module is integrated to identify and prevent possible threats in real-time to overcome security issues associated with 6G networks that are likely to use enormous IoT devices and complicated autonomous systems. The findings of the simulation prove that the proposed framework provides better performance than conventional 6G models. Throughput is increased by 47% (620 Mbps vs. 420 Mbps), while latency is reduced by 37% (24 ms vs. 38 ms). The 50% reduction (1.7% vs. 3.6) in packet loss also creates a more reliable network. The accuracy of intrusion detection in the proposed model is 15% better (97 % vs. 82 %), and the false positive rate is lower by 32 % (7.8 % vs. 11.5 %). These enhancements show that the framework not only maximizes network performance but also makes 6G networks a lot more secure. By combining SDN and AI with edge computing, it offers a scalable and efficient solution that meets the high requirements of future communication systems, including smart cities, autonomous vehicles, and other AI-driven services in the 6G era.
SDN; AI; edge computing; 6G networks; network optimization; security; reinforcement learning.