ACADEMIA Tech Frontiers Journal (ATFJ) is a peer-reviewed, quarterly scholarly publication dedicated to exploring cutting-edge advancements in technology, engineering, and applied sciences. The journal serves as a platform for researchers, industry professionals, and academicians to share innovative research, emerging trends, and transformative technological developments shaping the future.
Social Media Sentiment Analysis on Political Events in Pakistan
In Pakistan, social media has become an important tool of political discussion and a platform of expressing opinion. As the participation of such platforms as Twitter, Facebook, and Instagram grows, political participants, parties, and citizens discuss them, which indicates the mass opinion regarding the central events. This research paper explores social media opinion relating to political events in Pakistan in terms of both the positive and negative opinions depicted by the users. Sentiment scores were obtained with the help of natural language processing (NLP) methods, based on posts, comments, and tweets about elections, government decisions, and political scandals within the last three years. The analysis will help reveal the trends of the general audience, evaluate the influence of social media mood on the political activity, and point out the importance of the digital environment in the formation of political discourse. The results indicate that sentiment analysis can give timely and practical data to policy-makers, political strategists, and researchers in need of knowing the dynamics of opinion of people in the socio-political environment of Pakistan.
Copyright (c) 2025 Samaira Khalid , Saima Akram (Author) https://creativecommons.org/licenses/by/4.0
Social Media Advertising, Influencer Credibility, and Brand Trust on Online Purchase Intention among Gen Z Consumers
It is the quick increase of virtual structures which have modified the advertising and marketing environment, and this has predominantly affected the intake styles of Generation Z customers. Social media marketing and marketing has turn out to be one of the maximum influential merchandising, the use of influencer advertising and marketing and interactive verbal exchange with the emblem, to persuade the perceptions of customers and their shopping for styles. This paper will examine how social media commercial and credibility of influencers impact emblem accept as true with and on line buy purpose of Gen Z clients. Based at the Theory of Planned Behavior, Source Credibility Theory, and the Stimulus-Organism-Response (S-O-R) model, this studies will advocate that convincing commercial replica and straightforward influencers result in a extensive development in logo accept as true with; which therefore undoubtedly influences the acquisition aim. As digitally local and fantastically energetic customers of the social networking platform, era z has particular reactions to on-line promoting strategies, in contrast to the beyond generations. The studies can even upload to the prevailing literature on virtual advertising and marketing thru the empirical studies at the mediating impact of emblem consider among social media marketing and marketing and influencer credibility and on-line buy goal. It is thought that the findings will provide beneficial statistics to the entrepreneurs who might should layout greater consider-primarily based totally, influencer-primarily based totally campaigns withinside the tight on line markets to attain Gen Z purchasers.
Copyright (c) 2025 Hafiz Muhammad Sohail (Author) https://creativecommons.org/licenses/by/4.0
Low-Cost IoT Solutions for Air Quality Monitoring in Lahore
Air pollution has become a significant environmental and social health issue in the Pakistani urban centers, especially in Lahore since industrial emissions, motor vehicles and construction activities have led to negative air quality. Conventional air quality monitoring stations tend to be costly, not numerous and incapable of providing real time data. This paper discusses how a continuous air quality monitoring system with low-cost Internet of Things (IoT) can be utilized in Lahore. The deployment of low-cost sensors able to sample the particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), carbon monoxide (CO) and other pollutants is the goal of the study since the sensor will deliver real-time data to be used in problem definitions in policy, urban planning and even to enlighten people. The results of data obtained through the IoT sensors within three months were analyzed to determine spatial and temporal dynamics of air pollution in the city. The results prove that the low-cost IoT-based monitoring can be successfully used to supplement the traditional monitoring stations, offer granular data on multiple locations, and enable the stakeholders to take necessary mitigation actions in time.
Copyright (c) 2025 Muhammad Rashid Riaz (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Flood Risk Assessment in Riverine Areas of Pakistan
One of the most destructive natural disasters in Pakistan is flooding especially in riverine countries found in the Indus Basin. The occurrence and intensity of floods have increased over the past decades due to the increase in climate variability, unplanned urbanization, deforestation, and poor drainage systems. Pakistan has more or less applied the traditional flood risk assessment methods that have been based on hydrological modeling, past trends analysis which do not integrate real-time data and predictive intelligence. This paper examines how Artificial Intelligence (AI)-based methods can be used to assess flood risks in riverine Pakistan by analyzing machine learning (ML), deep learning (DL), and geospatial analytics. This study combines satellite geography, weather parameters, topography and past history of floods to come up with predictive models of flood prone areas. Algorithms suggested by AI, such as Random Forest, Support Vector Machines or Artificial Neural Networks, are discussed in terms of their potential to improve the early warning system and spatial risk mapping. The results show that AI-based models enhance prediction accuracy and uncertainty reduction and mapping dynamic flood risks is better than the standard statistical models. The paper highlights the role of implementation of AI frameworks alongside national disaster management systems like the National Disaster Management Authority as a way of reinforcing preparedness and response plans. The study has a contribution to the sustainable disaster risk mitigation by suggesting a data-based framework that is specific to the riverine geography of Pakistan, especially in the Indus River system.
Copyright (c) 2025 Muhammad Muzammil Asghar, Muhammad Saad Khan (Author) https://creativecommons.org/licenses/by/4.0
Augmented Reality as a Tool for Improving Conceptual Understanding in Engineering Courses
The development of educational technologies has had a great impact on the sphere of higher education and especially on the sphere of sciences and engineering where abstract ideas and complicated spatial systems may seem difficult to learn. Augmented Reality (AR) has become a new pedagogical experience that can be used to fill in the gap between the theoretical knowledge and their visualization through real-life settings by applying digital content to the natural surroundings. This paper examines how AR has been used to boost the conceptual learning in engineering classes. AR can be used to observe and interact with engineering models through a three-dimensional visualization that facilitates spatial reasoning and cognitive interaction due to its interactive three-dimensional visualization. AR application in the engineering field of study enables experiential learning, motivates learners, and promotes an active involvement of students. Empirical research shows that learning environments based on AR enhance performance on academic, problem-solving, and conceptual levels significantly in comparison to the traditional teaching strategies. Also, AR technologies facilitate group learning and offer feedback in real-time, which enhances knowledge acquisition and understanding of complicated engineering systems. Although it has benefits, there are challenges including technological infrastructure, cost and design of instruction that continue to pose an impediment to its widespread use. However, due to the growing accessibility of mobile AR applications and immersive technologies, potentials are high in the future integration in engineering programs. The study proposes the potential of augmented reality to transform the educational experience in the field of engineering and focuses on its ability to enhance conceptual knowledge, and engagement and learning outcomes in the contemporary classrooms.
Copyright (c) 2025 Muhammad Amir Ishaq, Umair Ramzan (Author) https://creativecommons.org/licenses/by/4.0
Federated Learning Approaches for Secure Edge Computing
Federated Learning (FL) has become one of the paradigms shifting towards machine learning, allowing a number of edge devices to jointly train models without exchanging raw data. This can be used to guarantee privacy of the data, less data communication overhead, and real-time decision-making under edge computing conditions. The paper discusses federated learning to secure edge computing, focusing on architecture, communication models, aggregation, and security. The experimental and theoretical studies show that FL is capable of reaching model accuracy that is similar to the centralized learning and reducing privacy threats and adversarial risks. The problems, including the heterogeneity of data, and the lack of computing power and communication efficiency, are discussed critically. The research paper ends with the suggestions on how to improve FL implementation in the actual edge networks, focusing on secure aggregation protocols, differential privacy, and client selection adaption strategies.
Copyright (c) 2025 Muhammad Talal Aslam (Author) https://creativecommons.org/licenses/by/4.0
Green Technologies: Innovations for Sustainable Development
Sustainable development has placed a center stage on green technologies. This is due to the fact that they pledge to address the heavy environmental, economic and social crises and threats of the 21 st century. The necessity of this shift towards appropriate technologies is the urgency and high demand of climate change, depletion of resources, and implications of those changes on human well-being and international stability.. This paper explores green technologies by outlining where they have been, a roadmap for their future, where they have or could be used and ways in which they can be focused to yield the best possible outcomes for people and the planet. The key aspects of the technologies identified in of energy efficiency, carbon footprints, circular economies and importantly ethical, legal and social aspects in their adoption, through discussing more than a dozen innovations, classified broadly by the areas of Sustainability, Agriculture, Waste & Circular Economy, Transport, Construction and Spaces. The inclusion of various case studies from different regions adds to the discussion by contributing examples of successes and limitations in the practice of green innovations. Throughout the discussion, two important challenges to scaling up the transition to the desirable green and sustainable technologies were identified: economic challenges, policy and government action challenges, and technology challenges. The report concludes with recommendations for global cooperative research and renewable energy focused policies to have the biggest impact on reaching the UN Sustainable Development Goals (SDGs). Green technologies occupy the place of responsibility between science society and policy - they are vital for the present and future of society and the planet, so the aim in this academic report is to demonstrate their importance for a sustainable future.
Copyright (c) 2025 Eman Babar (Author) https://creativecommons.org/licenses/by/4.0
Quantum Computing and Its Implications for Cybersecurity
The subsequent sport changer in computational strength is quantum computing, which additionally has the ability of presenting answers to tough issues that the classical structures aren't capable of handle. Although this technological step forward holds guarantees of creating a few technological achievements in fields like drug discovery, optimization, and synthetic intelligence, it makes it very bodily tough to cyber security. Conventional cryptographic hardware like the ones counting on each RSA and ECC algorithms is liable to quantum assaults ensuing in algorithms like the ones invented with the aid of using Shor. In this paper, the writer narrows right all the way down to the duality of quantum computing or its capability to convert the manner issues are labored out or the manner it's far destabilising modern virtual protection structures. Focus is at the feel of urgency to create encryptions that the quantum can smash and the global scramble in quantum-associated studies, in addition to the moral and political components of the issue. With a vital evaluation of possibilities and threats of quantum computing, the observe has proven a dire want to put together proactive strategies to make certain the integrity of facts withinside the corporation at some point of the post-quantum.
Copyright (c) 2025 Muhammad Javed (Author) https://creativecommons.org/licenses/by/4.0
Digital Health Interventions and Their Impact on Chronic Disease Management in Low-Resource Settings
Digital health interventions (DHIs), such as mobile health (mHealth), telemedicine, and decision support systems have a considerable potential for better chronic disease management in low resource settings. This paper is a review of the current evidence on the use and effectiveness of DHIs for non-communicable diseases (NCDs), such as diabetes, hypertension, and cardiovascular disease, in low- and middle-income countries (LMICs). While many interventions have positive outcomes in terms of the delivery and quality of others services such as adherence to medication and access to care, clinical outcomes are more mixed. Imposed barriers to implementation like: lack of infrastructure, low digital literacy, low integration with existing health systems and lack of political commitment often hinder their impact. Digital health literacy becomes an important enabler to support patients to play a more active role in the management of their own health, and to make informed health choices. This paper is suggesting that thoughtful design, context-specific adaptation, system-level integration, and policy alignment can increase the effectiveness of DHIs to contribute significantly toward the control of chronic diseases in resource-constrained environments.
Copyright (c) 2025 Muhammad Uzair Maqsood (Author) https://creativecommons.org/licenses/by/4.0
Blockchain-Based Security Models for Industrial IoT Networks
IoT networks have revx`xolutionized operations of manufacturing and industry through a seamless connection, real-time, and intelligent automation. However, these networks are extremely susceptible to security attacks, because there are numerous associated devices, a variety of protocols and significance of industrial processes. Some of the threats that are too difficult to be handled in the traditional centralized security systems are data manipulation, unauthorized access and denial of service attacks. The blockchain technology has been brought up as one of the possible solutions to make IIoT networks secure since it is decentralized, immutable, and transparent. The security models of blockchain about IIoT applications are discussed in this paper with the specific issue of authentication, data integrity, access control and secure data sharing The methodology will be based on a comparative analysis of the available blockchain frameworks, and experimental simulations and testing their functionality on the basis of the latency, throughput and scalability. These results demonstrate that the introduction of blockchain to IIoT networks has the positive impact on the introduction of higher levels of security, the reduction of single points of failure, and the establishment of untrusted interactions among industrial computers as well as the problems of resource scarcity and network congestion. The findings can be employed in offering feasible information on the way of building secure, efficient, and scalable IIoT systems.
Copyright (c) 2025 Muhammad Amir (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Predictive Maintenance in Renewable Energy Systems
Renewable energy systems (RES), i.e., wind turbines, solar photovoltaic arrays, and hydroelectric plants are fast being integrated as a result of global energy transition objectives. Nevertheless, it is extremely difficult to ensure the reliability and efficiency of such systems due to their distributed character, the harsh conditions under which they operate, and the complicated mechanical and electrical assemblies. Staffed or reactive maintenance is a classic approach to maintenance, which tends to lead to the high operational cost and unplanned downtime. Predictive maintenance (PdM) is an innovative solution proposed by artificial intelligence (AI) as an application of machine learning algorithms and sensor data and real-time analytics to predict the failure of equipment before it happens. The paper addresses the AI-based predictive maintenance models in renewable energy systems, addressing anomaly detection, prediction of faults, and the maintenance schedule optimization. The methodology used in the research is a literature review that is carried out systematically, modeling with data on past operations, and the performance of predictive algorithms in the context of accuracy, reliability, and cost-effectiveness. The results have shown that predictive maintenance using AI can provide a substantial decrease in the occurrence of unforeseen failures, an increase in energy production, and optimization of maintenance resources, as well as identify issues associated with data quality, model generalization, and the connection with existing energy management systems. The study contains practical implications on the energy operators and policy makers that can be used in ensuring that the measures of intelligent maintenance that can be undertaken to make the renewable energy systems more sustainable and efficient are implemented.
Copyright (c) 2025 Dur-E- Adan (Author) https://creativecommons.org/licenses/by/4.0
Post-Quantum Cryptography for Next-Generation Communication Networks
The potential of the swift development of quantum computing is an extreme danger to the traditional cryptographic programs, and they are the foundation of the modern communication infrastructure. RSA and ECC are classical public-key algorithms that can be successfully quantum-attacked, in particular, Shor's algorithm can easily factor large integers and calculate discrete logarithms (Bernstein et al., 2017). The Post-Quantum Cryptography (PQC) has become the key to secure the next-generation communication networks against these threats, which relies on lattice problem-based algorithms, hash-based based algorithms, multivariate polynomials-based algorithms, and code-based cryptography. The paper examines the concepts, design, and implementation issues of PQC within current network infrastructure focusing on the secure transmission of data, authentication, and key management in the upcoming 5G and 6G networks. The approach will include the survey of existing algorithms of PQC, model the integration of protocol in the communication systems and test computational efficiency, security strength, and scalability. It has been found that PQC is suitable to protect network communications against quantum attackers, and achieves reasonable performance, though trade-offs can be seen in terms of computation overhead, key size, and latency. The research offers understanding to the researcher, network engineers and policy makers in order to adopt quantum resistant cryptography protocols to secure the confidentiality, integrity and authenticity of information in the future communication networks.
Copyright (c) 2025 Daniyal Zaheer (Author) https://creativecommons.org/licenses/by/4.0
Next-Generation Human–Computer Interaction: Leveraging Artificial Intelligence, Extended Reality, And Neurotechnology For Seamless Digital Integration
A new cultural period in what is commonly referred to as human-computer interaction (HCI) is coming to an end as neurotechnology, augmented reality (XR), and artificial intelligence (AI) rapidly converge. Generally speaking, HCI has developed through mechanical interfaces, graphical user interfaces, and touch screen interfaces; yet, these methods are constrained by the cognitive and physiological limits of human users. HCI is being appropriated by academics as next-generation technologies appear, allowing for a change in the nature of human-machine interaction from command-driven input to immersive, flexible, and easy participation. Beyond creating algorithmic desecration points, AI-driven frameworks operate anticipatory, contextualized, and affectively sensitive systems that enhance and modify any information flow between artificial and human systems. Due to this, the use and development of XR technologies (also referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR)) is growing in simulated worlds where the distinction between virtual and real-world computational reproduction is not easy. Neurotechnology introduces another dimension namely Brain-Computer Interfacing (BCI) that controls the intelligence of the cognitive level without the aid of traditional input devices such as speech associates, touch displays or consoles. These trends, in general, demonstrate the process of the continuing convergence of people and machines that connect cognition, judgment and environment in an intimately interconnected computerized ecology. This shift in framing offers increased productivity and utility but also makes one reconsider human nature, creative work and its environment, and creativity in the digital era. Nonetheless, to work towards a meaningful progress and equal opportunity, the transformational process does not lack moral, technological and social repercussions and concerns that demand pre-planning.Counterfeit Insights serve as the foundation of these three domains, facilitating frameworks that learn, adapt, and customize continuously. AI-powered frameworks are able to convert customer expectations with extraordinary accuracy utilizing multimodal recognition, machine learning computations, and standard dialect preparation. Flexible learning models are examples of applications that adapt the learned experience to analyze and measure cognition styles while feeling recognition applications refined to outfit communication styles to emotional states. With these applications, HCI becomes more proactive rather than reactive, producing contexts in which machines become anticipatory rather than responsive to human needs. The use of XR offers immersive spatial computing that could assist customers in associating information, shapes, and contexts within a three-dimensional environment. Beyond thrills and games, XR has voluminous immersive application potential including healthcare for further surgical experiments and mechanical design to prototype more rapidly, along with introduction to and practice of applications to acquire hands-on learning. Meanwhile, neurotechnology---more specifically brain-computer interfaces, or BCIs---are beginning to enable applications from assistive devices for those with disabilities to enhanced cognitive performance in healthy customers. Each sector could combine to provide the possibility of whole biological systems made in which neurotechnology provides intentional purpose, XR provides recognition, and AI provides cognition. Ultimately, there are redefined boundaries of human growth constructed by this foundation of tri-fold next-generation HCI opportunities. Regardless of these opportunities, there is still the need to deal with the underlying design, usability, and interoperability aspects to enhance next-generation HCI. Contrary to traditional frames, next-generation phases should consider the complexity of human cognition as well as the adaptability of AI which is dynamic. Although XR technology is immersive, it cannot be popularized due to such problems as inactivity, vision impairment, and getting used to the device. Moreover, neurotechnologies can resolve significant obstacles, particularly, in the field of reduction in noise, flag security, and ethical concerns associated with cognitive security. The fact that various areas are mixed further complicates the issue as integration demands that different types of discernment, cognition and behavior be harmonised with each other. These professional problems are complicated by moral dilemmas: the most controversial issues related to the incorporation of next-generation HCI focus on the question of autonomy, the ownership of information, reconnaissance, as well as on the human organization. Moreover, socioeconomic inequality can exert further separation in case these gains are only accessible to first-class societies. The paper then examines the tools, developments, and methods that have enabled easier incorporation of AI, XR, and neurotechnology and at the same time, it considers the issue of planning and implementation with caution. The research will engage in a complex evaluation of the way next-generation HCI could be designed with regards to unbiased and moral opportunities through reviewing both the technological advances and the societal propositions. To analyse the opportunities and challenges of AI, XR and neurotechnology in the inception of advanced integration that can be consistently created, the researcher is provided with a multi-layered way of analysis. It also contains comprehensive writing audit of modern frameworks, comparison of the existing models of HCI, and analysis of test ideas to demonstrate certain progress. The study assesses the neurocognitive models in training AI-based versatile models, the ability of XR to stimulate epitomised communication, and the infringement of brain objectives by BCI through the lens of multidiscipline focus. To assess integration conventions in the healthcare, education, defence, excitement, and mechanical plan sectors, the focus is made to the fabric and methodical systems required. The research is also aimed at mapping the direction of the current research concerning future applications, which will facilitate knowledge transfer into possibly new directions. The approach to methodology ensures the study is not limited by the scope of the study because this bridges the gap between development and application by enhancing conceptual theorization to down-to-business study of usage strategies. By engaging in the combination of experimental data and theoretical frameworks, the study is set to bring holistic knowledge on the possibility of conceptualising, outlining, and scaling next-generation HCI. In conclusion, AI, XR, and neurotechnology have provided a bottom line in the enhancing human-computer interaction. The developments enhance human vision, thought, and decision-making in thoughtfully scaled situations through proactive, immersive, and cognitive level interaction. The scale of this change in any incident highlights the importance of approaching it carefully in order to deal with its complex social, professional and ethical challenges. The proposal by locating next-generation HCI within mechanical and humanistic dimensions contributes to the discussion as it facilitates a model of understanding gaps and limits. It is expected that two outcomes will be achieved by this study, the first one that it will contribute to the field of academic knowledge by offering specific and multidisciplinary insights on concentrated achievements in HCI, and the second one is the guidance of the architects, technologists, and policymakers as to the direction of conscious enhancement and organization. Ultimately, by highlighting the need to correlate the development of HCI with frameworks motivated by interfaces to a coherent cognitive integration, the paper suggests that to make the benefits of next-generation HCI fairly distributed, as well as beneficial to the society, the approaches to its development must be adjusted accordingly.
Next-Generation Human–Computer Interaction
Copyright (c) 2025 Dr. Ali Hassan (Author) https://creativecommons.org/licenses/by/4.0
AI and the Future of Work: Redefining Skills, Employment, and Human–Machine Collaboration
The quickened advancement of the Fake Insights (AI) is changing long term of work, challenging the customary worldview of business, and reevaluating the concept of skills and human-machines interaction. When AI advances are seen as a promising opportunity to attain productivity, advancement, and efficiency, they moreover raise questions approximately work uprooting, disparity, and the readiness of laborers to grasp unused shapes of substances. This paper talks about the two-fold impact of AI on the labor constrain, agreeing to which AI is modifying the labor markets, as well as influencing the request of cognitive, social, and specialized skills, and making novel ways of human-machine participation. The paper investigates the openings and challenges of AI in creating long haul work situations, based on later writing, reports, and cases around the globe. It states that rather than substituting human labor with AI, it'll upgrade human capacities, which can require reskilling, non-discriminatory arrangements, moral integration systems. The work is profitable to the current talk on the trade-offs between innovative progression and social supportability by giving understanding of the ways in which social orders can oversee the changing flow of work within the age of AI.
Copyright (c) 2025 Muhammad Daniyal (Author) https://creativecommons.org/licenses/by/4.0
Green Technology and Sustainable Innovation: Tech Solutions for Climate Change
The issue of climate change can be regarded as one of the most pressing worldwide crises in the 21st century, so there is a need to find bold and innovative solutions to the problem in every sector of society. Green technology and sustainable innovation is a new technology that has become significant in responding to environmental threats and green house gases, and resilient economies. Renewable energy and electric mobility, waste management, green architecture, and efficiency systems based on artificial intelligence, technology is changing the response to the climate emergency by nations, businesses, and communities alike. This paper discusses how green technology can be used as a driver towards sustainability, the applications of this technology, its potential and constraints. It also brings out the strategic value of innovation in the balance between the economic growth and the ecological responsibility and offers information about how governments, industries, and individuals can cooperate to create a better Green future.
Copyright (c) 2025 Ahsan Khan (Author) https://creativecommons.org/licenses/by/4.0
Machine Unlearning: Teaching AI to Forget
Over the past few years, artificial intelligence (AI) and machine learning (ML) systems have become excessively reliant on the collection of data. This reliance on data is to the benefit of model performance; however, it raises serious concerns about issues surrounding privacy, data retention, and ethical use, particularly given the introduction of regulatory instruments, including the General Data Protection Regulation (GDPR) and the “Right to be Forgotten”. Traditional machine learning models are not able to selectively forget certain data after the model’s training is Complete; therefore, it is difficult for users to request the deletion of data, or situations when data must be deleted because of legality, or ethics. Machine unlearning is a new field of research that aims to explore algorithms and frameworks to unlearn specific training data in a model, without the overhead of complete retraining of the model. There are multiple techniques to achieve unlearning from a model, including complete retraining, sharded and isolated slicing (SISA), knowledge distillation, and approximate gradient-based removal. These techniques are each designed to further enhance efficiency versus appropriateness in forgetting. Machine unlearning represents an area of completion that is more significant than simply regulatory compliance. It also allows for ethical AI because an system can effectively value the user’s choices in data, while facilitating that any out-of-date or sensitive information can be removed without compromising the model. As AI continues to permeate different sectors, such as healthcare, finance, and social media, we must ensure we can unlearn, for trust and transparency. Future work in this area will focus on scaling up and verifying unlearning effects in adversarial deep learning architectures, but we are getting closer.
Copyright (c) 2025 ACADEMIA Tech Frontiers Journal https://creativecommons.org/licenses/by/4.0
Blockchain-Integrated Cloud Architecture for Secure and Scalable Data Science Applications
Combining blockchain technology with cloud computing is beginning to provide a very strong and recognizable answer to the issues of security, integrity and scalability related to data science applications. Traditional forms of cloud architecture can give a flexible design and storage solutions, however they lack transparency and strong security protocols, which can raise issues, especially in multi-user environments. Blockchain creates trust through decentralization, tamper-resistant, immutable storage, shared security, and traceability by proving provenance thereby designing robust protocols, promoting assured integrity and ensuring confidentiality protocols which can help protect sensitive data that is being used in data science operational practices through machine learning, analytics, and artificial intelligence. The secondary utility added from smart contracts and immutable ledgers provide cloud solutions with both tamper-proof and secure data processing in an auditable fashion, in effect establishing verified multi-party systems, through decentralizing ownership as well as access. This decentralized data ownership paradigm supports collaborative activities which can provide enhanced trust through systems where, societal commitment to data privacy, is an overriding concern (for instance, any collaboration involving health care, finance or supply chain analytics). Meanwhile, the cloud utility also provides all the scale needed to analyse vast quantities of data whilst also containing many complex computations. A limitation that could result from scaling cloud architecture is blockchain throughput, however, interoperability and throughput issues can be redressed through an array of hybrid or semi-hybrid solutions, for instance, methodologies such as:- Off-chain data storage such as Filecoin, IPFS, etc Layer-2 blockchain protocols, CELO, ETH 2.0, Cardano, etc Standardized APIs to improve interoperability among varying blockchain properties and governance systems. These itemized approaches take away throughput barriers to developing a sustainable architecture that promotes secure, transparent, scalable data science ecosystems, that meet such as the demand for data privacy and increasing and developing calls for digital sovereignty.. Adoption expands, and this ecosystem-based approach should be instrumental in advancing the future of secure data-driven technologies.
Wearable Biosensors and AI for Real-Time Health Monitoring
Wearable biosensors and artificial intelligence (AI) are radically improving healthcare by facilitating continuous, non-intrusive, real-time monitoring of physiological and biochemical signals. Wearable biosensors are small, personal devices that can be worn on the body and gather metrics including heart rate, body temperature, blood glucose, and respiratory rate. Wearable biosensors lay the foundation for proactive, personalized healthcare. Chronic disease rates are on the rise, populations are aging across the globe, and there is need for smart systems that support remote care. Smart monitoring systems that utilize biosensors are being adopted for these reasons, especially AI-based monitoring. AI allows for data generated by biosensors to be processed, and used reliably. Machine learning and deep learning models are particularly useful in processing biosensor data. Example machine learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Deep learning methods are able to remove background noise, feature extraction, data interpretation, and anomaly detection, along with retrospective analysis to predict health events including arrhythmia, stress episodes, hypoglycemia. AI and ML algorithms can use biosensor data for on-time feedback and decision support to clinicians with high amounts of specificity and sensitivity and low connectivity latency. Using Internet of Medical Things (IoMT) based infrastructure, wearable biosensors can securely transmit data privately to discrete clinical or edge cloud providers, to facilitate either clinician oversight of patients in real-time as they do their monitoring, or clinicians being forewarned to support early intervention when monitoring for acute patient events. While these systems can be considered game-changing advancements which include many advantages, there still exists hurdles to adopting systems to improve healthcare. There must be work done to address issues with data privacy, algorithmic biases, device interoperability, and sensor accuracy. Compliance with some privacy laws, such as HIPAA and GDPR is needed and the use of ethical and transparent AI models is necessary. Technical issues that must be addressed more generally include motion-induced signal noise, battery lifespan, and hardware-software integration. In conclusion, wearable biosensors empowered by AI may represent a new frontier in personalized, real-time health monitoring that enables early recognition of health issues, remote supervision, and chronic disease monitoring while alleviating the need for traditional healthcare capacity. To realize the full potential of this functionality, subsequent research should strive to improve data security, model transparency, and system scalability, and to keep patients as the focus of innovation.
AI-Powered Chatbots in Education: Evaluating Their Effectiveness in Student Engagement and Learning Outcomes
Artificial Intelligence (AI) is having an ever-greater influence on a multitude of domains, including education through chatbots. Chatbots are AI-powered systems that facilitate communication, deliver real-time support, and promote personalized learning experiences. Chatbots have sparked great interest in educational domains due to their potential to increase student engagement and improve educational outcomes. This abstract examines the role of AI-powered chatbots on academic motivation, interactivity, and overall academic performance of students in educational domains. By reviewing recent research, surveys, and experimental data, the abstract discusses how chatbots contribute to a more engaging and interactive learning environment. AI powered chatbots can provide immediate feedback, clarify complex concepts and offer personalized support for students with various learning needs, which can contribute to increased confidence and involvement. Furthermore, chatbots are accessible 24/7, and can address a time-space gap involving teacher availability, especially in remote learning contexts. However, the effectiveness of chatbots is varied, depending on aspects of the AI design, content relevance, and the digital literacy of students. They currently also possess limitations, such as lack of emotional intelligence, and ability to acknowledge nuances in questions. Nevertheless, the potential of AI powered chatbots in supporting traditional educational approaches appears strong. Their escalating presence represents a shift toward more technology-integrated pedagogy and is an area of interest in educational research and innovation.
Security Challenges in Industrial IoT Environments
The Industrial Internet of Things (IIOT) represents a fundamental change in industries by connecting machines, sensors and control systems to the internet. This connectivity has moved industries toward enhanced automation, real-time data analytics, predictive maintenance, and operational efficiency, as well as increased functionality through the integration of complex technologies. However, the IIoT creates complications for organizations in terms of security vulnerabilities. The increased connectivity of devices creates a larger attack surface that exposes systems to a myriad of cyber threats including risk of unauthorized access, data breaches and tampering of systems. Many organizations that are adopting IIOT still operate legacy systems that do not have any modern security protocols, thus creating major gaps and vulnerabilities. As well, IIOT devices possess a diverse set of characteristics (manufacturer, protocol, operating systems), which complicates establishing unified security standards and providing real-time monitoring. Also, there is nothing novel in the protocols being used, as the "lightweight" characteristics of both the MQTT protocol and CoAP protocol indicate, are both software protocols that are velocity based - this, perhaps inadvertently, contribute to their vulnerabilities. In addition, as a factor of concern, engaging in poor authentication and encryption practices, and not having a secure update mechanism, pose the same challenge as was present with much older industrial control systems. It should also be noted that human factors such as social engineering scams, as well as cybersecurity training, will increase risk for organizations when implementing IIOT. This abstract reinforces the requirement for organizations to develop a thorough, layered security architecture that can meet the unique needs of their sectors. Strategies, including Zero Trust models, AI-enabled intrusion detection systems, blockchain for secure communications, and compliance with frameworks such as ISA/IEC 62443, can significantly impact the reliability of IIoT systems. As industrial sectors progressively digitize, it becomes essential to comprehend and work around these security hurdles in order not just, to protect data and operations, but also, to safely, reliably, and confidently facilitate trust in IIoT-enabled industries.