Internet of Things (IoTs) Research Papers (original) (raw)

2025, International Journal of Innovation and Scientific Research

This study explores the integration of the Internet of Things (IoT) into urban waste management in the Democratic Republic of Congo (DRC), focusing on Lubumbashi as a case study. The goal is to address the growing challenges of waste accumulation and inefficient infrastructure management using smart bins. These devices are equipped with sensors to monitor fill levels and communicate in real-time with management systems. The Unified Process (UP) method structured the development, while SysML modeling ensured precise design. A functional prototype was created, demonstrating the effectiveness of the proposed approach in an urban context of the DRC.

2025

Performance models used in the aircraft development process are dependent on the assumptions and approximations associated with the engineering equations used to produce them. The design and implementation of these highly complex engineering models are typically associated with a longer development process. This study proposes a non-deterministic approach where machine learning techniques using Artificial Neural Networks are used to predict specific aircraft parameters using available data. The approach yields results that are independent of the equations used in conventional aircraft performance modeling methods and rely on stochastic data and its distribution to extract useful patterns. To test the viability of the approach, a case study is performed comparing a conventional performance model describing the takeoff ground roll distance with the values generated from a neural network using readilyavailable flight data. The neural network receives as input, and is trained using, aircraft performance parameters including atmospheric conditions (air temperature, air pressure, air density), performance characteristics (flap configuration, thrust setting, MTOW, etc.) and runway conditions (wet, dry, slope angle, etc.). The proposed predictive modeling approach can be tailored for use with a wider range of flight mission profiles such as climb, cruise, descent and landing.

2025, Injoit Journal

These The flood disaster that occurred in several sub-districts in Tangerang city requires immediate assistance from the Tangerang City Social Service and the Disaster Management Agency. The Tangerang City Social Service, in collaboration with the Disaster Management Agency and the provincial government, provides aid in the form of food, medicine, and other relief supplies. Due to the limited number of relief personnel, a Decision Support System is needed to assist flood victims. The SMART method (Simple Multi-Attribute Rating Technique) and the Flodis Help application can be applied in this process. This research resulted in the Flodis Help application using the SMART method as a decision support system for providing aid to flood victims. The system's output includes a map of flood-affected areas, a priority aid list, victims' needs, access to the nearest hospitals, and flood relief posts. The application’s output provides guidance to help the government and humanitarian organizations deliver aid quickly, accurately, and equitably.

2025, JNRID

Livestock farming is currently undergoing increasing pressure to supply increased worldwide demand for animal products while addressing ongoing issues such as disease outbreaks, inefficient resource usage, and environmental sustainability. This review explores how AI and IoT technologies are transforming livestock management, focusing on increasing production and reinforcing biosecurity. The review reveals that in the United States beef sector, AI and IoT are being used to optimize herd health, improve feed efficiency, and reduce environmental impact through smart data integration. However, in African dairy systems, particularly among smallholder farmers, AI-powered mobile platforms and low-cost IoT sensors are proving critical for disease diagnosis, yield improvement, and market access, despite infrastructure constraints. These technologies not only increase operational efficiency but also encourage animal welfare and traceability, resulting in safer and more sustainable livestock supply chains. The continuous expansion of digital agriculture, combined with infrastructural development and inclusive innovation, indicates potential for developing resilient livestock systems capable of fulfilling future global food demands while maintaining animal and public health.

2025, IEEE

This study investigates integrating artificial intelligence (AI) techniques in mobile industrial wireless communication networks, focusing on enhancing network performance, reliability, and security in industrial environments. Through a comprehensive analysis of industrial testbed datasets (iV2V and iV2I+), we demonstrate the effectiveness of AI-driven approaches in optimizing network operations. We implemented multiple machine learning models, including Decision Trees, Random Forests, and LightGBM, to predict quality of service (QoS) and optimize link selection. The Decision Tree Regressor achieved excellent performance, demonstrating its effectiveness for industrial wireless applications. We introduce a DBSCAN clusteringbased approach for identifying weak signal regions, enabling proactive network optimization and coverage enhancement. The study also evaluates location fingerprinting models, achieving high accuracy in source classification through feature importance analysis. Our results emphasize the practical benefits of AI integration in industrial wireless networks, particularly in predictive maintenance, real-time decision-making, and network security. The findings provide valuable information for implementing AIdriven solutions in Industry 4.0 applications. However, the study acknowledges current limitations and emphasizes the need for real-world validation in industrial operations.

2025, A Blockchain-Based Ecosystem for Prescription, Diagnostic, and Treatment Data Transparency in Bangladesh’s Healthcare Sector

Bangladesh faces ongoing challenges in healthcare transparency, including issues like overprescription, unnecessary diagnostics, and limited access to patient health records. This research proposes a blockchain-based healthcare data ecosystem aimed at enhancing transparency, accountability, and efficiency in the healthcare system. The proposed model integrates prescription records, diagnostic reports, and treatment histories into a secure and immutable blockchain ledger accessible by authorized stakeholders. Each patient is assigned a unique digital health ID for centralized data access. The system leverages smart contracts for automated auditing and real-time monitoring by government authorities. This paper discusses the system's architecture, benefits, implementation challenges, and policy implications in the context of Bangladesh.

2025, Mémoire ISIG 2018- NZANZU TABULYA

The evolution of GPS technology and the transmission of data in a wireless medium has given rise to several services, including the Geolocation service in an isolated area. The latter offers various possibilities in location and map positioning, including the extraordinary evolution of the Google-Map API that has provided satellite maps maps, maps, and hybrids rich enough to display the movement of different objects equipped with a GPS receiver. This facility motivated us to integrate geolocation into the transport domain.
Our job is to set up a taxi control system with an Android phone equipped with a GPS receiver on the one hand at the customer and a tablet playing the role of dashboard at the driver's other hand within from the city of Goma.
The developed application has been interconnected with the different modules of the FIREBASE database from which we have exploited: FIREBASE Authentication, FIREBASE Storage, FIREBASE DATABASE REAL TIME to store the different users, control movements, discussions performed and different real-time user coordinates

2025

The advent of 5G networks is a milestone technology in the world's communication infrastructure, supporting faster data transfer, lower latency, and greater Internet of Things (IoT) devices. Although the technology is a harbinger of immense cybersecurity risks that compromise the integrity, confidentiality, and availability of data, in spite of its potential to drive innovations across many sectors, such as healthcare, autonomous cars, and smart cities, its extremely interconnected and decentralized nature offers a large attack surface to cyber attackers. The research gap here relates to the understanding of the new cybersecurity challenges posed by 5G's new capabilities, such as network slicing, ubiquitous device interconnectivity, and virtualization. This study seeks to examine the most severe vulnerabilities that are inherent in 5G networks, with emphasis on potential threats like unauthorized access, data breaches, and denial-of-service attacks. It also seeks to examine the limitations of current security mechanisms in mitigating the challenges of next-generation technologies. The study will provide recommendations on how to secure 5G infrastructures, which may include the development of advanced encryption techniques, enhanced network monitoring, and multi-layered defense mechanisms. It will also evaluate the roles of artificial intelligence and machine learning in real-time threat detection and mitigation. This study aims to enhance the resilience of future-proof 5G communication networks by investigating the intrinsic cybersecurity risks of 5G and offering actionable recommendations. The results are anticipated to guide industry stakeholders and policymakers to create secure and resilient 5G frameworks that can adapt to the changing technological environment without sacrificing safety and privacy.

2025, Wireless Communications and Mobile Computing

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.

2025, Journal of Artificial Intelligence and System Modelling (JAISM)

Heart disease is a leading cause of mortality worldwide, necessitating effective systems for timely diagnosis and management. This study proposes an IoT-based method utilizing the Arduino platform and fuzzy logic to monitor heart health and detect individuals at risk. The system integrates sensors to measure key parameters like heart rate, blood pressure, and pulse, which are processed through a fuzzy logic algorithm. By categorizing heart rates into slow, fast, or normal states, the system enables accurate diagnosis of normal and pathological conditions. Data is efficiently processed and transmitted via IoT infrastructure to relevant medical centers, allowing healthcare professionals to act promptly, especially during critical "golden time" windows. The proposed approach emphasizes reducing data processing time and energy consumption while maintaining high accuracy. Using a dataset with 300 records from the Hungarian Heart Disease database, the model achieved a notable classification accuracy of 93.62%. The architecture includes three phases: data collection from sensors, decision-making via the fuzzy logic controller on the Arduino platform, and real-time communication of results to medical teams. This system demonstrates its potential to enhance patient outcomes through precise and swift detection of heart abnormalities, making it a valuable tool for intelligent healthcare systems in IoT-enabled smart cities.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

As the Internet continues to expand, immense people around the globe join the Internet. The Internet of Things (IoT) can be defined as the interconnection of peerless identifiable embedded computing devices within the current Internet infrastructure. This paradigm encompasses an infrastructure of software, hardware, and services that link tangible objects called things to the Internet. In Internet of Things technology, multimedia big data which is said to be the huge amount of data from multimedia devices will be generated with the swiftly rise of the multimedia gadgets and devices. The multimedia devices need higher processing and memory resources to process the obtained multimedia information. The Internet of Things systems are fiasco in realizing the multimedia devices connectivity unless they are able in processing multimedia gadgets and devices at a moment. In this paper, we are introduces a new concept of Internet of Multimedia Things (IoMT) for multimedia communications in Internet of Things (IoT). Internet of Multimedia Things (IoMT) communications play a vital role in Internet of Things (IoT) applications such as traffic control and handling, environmental monitoring, healthcare sector, observation & surveillance, event recognition and house monitoring and automation. In this paper, we present a comprehensive survey of IoMT and future research directions. The Internet of Multimedia Things (IoMT) applications such as real-time multimedia based security and monitoring in smart house, Smart Agriculture, multispecialty hospitals, metropolitan area, and smart transportation handling systems are of the most difficult systems to deploy.

2025, International Journal of Engineering Technology Research & Management

Cybersecurity is not an exception of the various fields that have been influenced by the advancement of artificial intelligence hence its evolution. Nevertheless, AI is also used by hackers for the increasing number, scale, and efficacy of their attacks. This paper therefore defines AI Cybercrime as automated phishing, AI malware, social engineering and cyber espionage, which are new form of threats that affect persons, organizations, and governments. This paper focuses on investigating AI-assisted cyber threats, knowledge and tools of an attacker, as well as defenses against threats and attacks. Applying threat detection systems with the help of artificial intelligence, ways and means of machine learning-based anomaly detection, and interaction between a human and an AI are discussed as some possible countermeasures. These small examples of AI applications in phishing, ransomware, and espionage have raised a call for robust and evolving cybersecurity measures. The results also state the fact that AI acts as a potential threat as well as having the capability to deal with threats effectively, thus requiring constant R&D in AI security.

2025, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

This research paper presents the real-world implementation of "RescueNow", an AI-integrated women's safety mobile application aimed at enhancing emergency responsiveness through smart technology. The system combines real-time SOS alerting, GPS-based location tracking, silent audio-video recording, and multi-channel emergency communication using services like Twilio. Designed with a user-centric approach, the app provides multiple trigger mechanisms including voice command, shake detection, and one-tap alerts, ensuring accessibility in high-stress situations. It also features community safety ratings and direct communication with law enforcement authorities. A notable aspect of this project is its machine learning module, which analyses patterns to predict potential crime-prone zones, allowing for proactive measures. Extensive testing in simulated and real-world scenarios validated the system's effectiveness, reliability, and scalability. This paper documents the design, implementation, and evaluation of RescueNow, demonstrating how technology can be a powerful ally in building a safer environment for women.

2025, REVISTA CIÊNCIA AGRONÔMICA

Site-specific management practices have been possible due to the wide range of solutions for data acquisition and interventions at the field level. Different approaches have to be considered for data collection, like dedicated soil and plant sensors, or even associated with the capacity of the agricultural machinery to generate valuable data that allows the farmer or the manager to infer the spatial variability of the fields. However, high computational resources are needed to convert extensive databases into useful information for site-specific management. Thus, technologies from industry, such as the Internet of Things and Artificial Intelligence, applied to agricultural production, have supported the decision-making process of precision agriculture practices. The interpretation and the integration of information from different sources of data allow enhancement of agricultural management due to its capacity to predict attributes of the crop and soil using advanced data-driven tools. Some examples are crop monitoring, local applications of inputs, and disease detection using cloud-based systems in digital platforms, previously elaborated for decision-support systems. In this review, we discuss the different approaches and technological resources, popularly named as Agriculture 4.0 or digital farming, inserted in the context of the management of spatial variability of the fields considering different sources of crop and soil data.

2025

This study introduces an IoT-enabled device-todevice communication system for smart home automation, focusing on remote control and energy efficiency. It uses two ESP8266 wireless modules to provide reliable, low-latency communication between devices. With the support from an IoT cloud platform, users can control household appliances, like lighting systems, via a mobile app powered by Arduino IoT Cloud. The system combines manual control using push buttons and relay modules, offering flexibility and convenience. By integrating direct device-to-device communication with IoT cloud features, the system ensures scalability, user-friendliness, and energy savings. It tackles key challenges such as network reliability, energy optimization, and accessibility for various users. Real-world tests demonstrate its stability and efficient device operation, showcasing the potential of IoT solutions to transform modern homes into efficient and secure living spaces.

2025

Buku Pemrograman Internet of Things (IoT) dengan Arduino dan Python Jilid 1

2025, Advances in Engineering and Intelligence Systems

The Internet of Things (IoT) improves our lives by facilitating real-time communication between people and objects. Predictive analytics could turn a reactive approach into a proactive one with the rise of artificial intelligence (AI) and machine learning in the healthcare sector. As a branch of machine learning, deep learning is able to deal with large amounts of data quickly, produce valuable insights, and solves complex problems. Accurate and timely diagnosis of diseases is essential for disease prevention and early treatment. The widespread adoption of electronic medical records necessitates the development of prediction models that are more accurate to effectively harness recurrent neural network variants of deep learning. This study investigates the integration of the Internet of Things and deep learning for disease prediction and diagnosis, which highlights key advancements in data collection, preprocessing techniques, and feature extraction strategies. The potentiality of convolutional and recurrent neural networks in increasing the accuracy of diagnosis and allowing early detection of diseases is analyzed. This review shows how IoT combined with deep learning can develop an investigative disruption in the prediction and diagnosis of diseases by expanding their validity, speed, and possibility of early detection thus opening the door to new applications and effective research.

2025, : International Journal of Innovative Research in Advanced Engineering

The early detection and management of plant diseases in short-statured crops such as beans, black gram, cowpea, groundnut, and tomato are crucial for minimizing yield losses and improving agricultural resilience in regions like the Karnataka-Tamil Nadu border. This study presents the design, development, and evaluation of a convolutional neural network (CNN)-based plant disease detection model integrated into a low-height rover system tailored for smallstatured crop fields. A dataset of 18,256 annotated images spanning 23 classes of healthy and diseased leaf samples was compiled, pre-processed, and used to train a multi-layered CNN architecture. While batch-level prediction of 18,256 test samples revealed overfitting evidenced by a uniform misclassification of all samples into a single class-the system demonstrated reliable disease identification during real-time field deployment. The rover's performance was further enhanced through the integration of soil moisture sensors, enabling preliminary analysis of environmental correlations with disease occurrence. The model and its hardware platform were evaluated qualitatively and quantitatively, with findings presented using confusion matrices, class distribution charts, and visual documentation from field trials. Limitations such as reduced effectiveness under fluctuating weather conditions, limited battery life in large-scale fields, and incompatibility with taller crops were acknowledged. Recommendations include integrating solar energy systems and improving data augmentation techniques to enhance robustness. This study contributes significantly to the underexplored domain of precision agriculture for short-statured crops, offering an accessible, scalable framework for early disease diagnosis using AI and embedded systems. Despite its constraints, the system presents a viable tool for smallholder farmers, fostering sustainable agricultural practices. Future research should address model generalization, environmental adaptability, and integration with cloud-based farm management systems. This work underscores the potential of AIdriven solutions in transforming low-input farming ecosystems by offering context-specific technological interventions rooted in local agronomic realities.

2025, World Journal of Environmental Engineering

Day after day the world stuck more and more in wars, pollution and so many other risk that threaten the environment. With a population of more than 7.3 billion, the planet suffers from continuous damage from human activity. As a result of these human distortions, climate change is one of the most fatal challenges that face the world. Climate Change won't be stopped or slowed by a single action, but with the help of too many small contributions from different fields, it will have an impressive impact. Changing to electricity generation, manufacturing, and transportation generate most headlines, but the technology field can also play a critical role. The Internet of Things (IoT) in particular, has the potential to reduce greenhouse emissions and help slow the rise of global temperatures. IoT includes more than super brilliant new gadgets and smart widgets. It also influences the Earth's condition, from its available resources to its climate. In this paper we are showing that technology itself could be the tool will save the world if we take advantage of it. Environmental monitoring is a broad application for the Internet of Things (IoT). It involves everything from watching levels of ozone in a meat packing facility to watching national forests for smoke. These solutions are the first step toward creating a numerous connected infrastructures to support innovative services, better flexibility and efficiency. We also make a spot on Industrial Internet of Things (IIoT) and its challenges as the future is for it.

2025

This study investigates the factors that influence the adoption of digital technology in rural areas of India, with a focus on the Digital India Program (DIP). By analyzing the age distribution, education levels, technology adoption rates, and utilization patterns among rural populations, this research provides insights into the effectiveness of the DIP in targeting specific demographics and promoting digital inclusion. Quantitative data were collected from 400 respondents in Srikakulam District in the Andhra Pradesh state of India. The data collected were analyzed using SPSS. The findings reveal a predominantly young population in rural India, indicating a workforce with significant economic potential and a higher likelihood of embracing digital technologies. Moreover, the study highlights the high levels of education among respondents, indicating a population well-equipped to understand and benefit from digital initiatives. Unexpectedly, the research shows a higher rate of digital technology adoption among female respondents, challenging the perception of gender disparities in technology access. This finding suggests that the DIP has played a vital role in bridging the gender gap and empowering women in rural areas. Additionally, the study uncovers a trend towards mobile-based services over computer-based services, signaling a shift in technology utilization patterns. This emphasizes the need to prioritize mobile technology and improve connectivity in rural areas to ensure wider access to digital platforms. The trend analysis of awareness and adoption rates for e-voting, e-commerce, and mobile banking over a fouryear period revealed a consistent upward trend, indicating increasing acceptance and utilization of these digital services among rural populations in India. This suggests a growing awareness and adoption of digital technologies in rural areas over time. Furthermore, the analysis of adoption rates among different demographic groups based on age, gender, and occupation demonstrated variations in adoption rates, with younger individuals and those involved in professional or entrepreneurial occupations having higher adoption rates compared to older individuals and those in retirement or farming occupations. The analysis of variance and regression analysis further supported the influence of demographic factors on the adoption rate of digital technology. Age group, gender, and occupation were found to be significant predictors of the adoption rate, suggesting that these factors play a role in influencing the likelihood of adopting digital technology among the population. The correlation analysis indicated positive relationships between perceived usefulness, ease of use, and adoption/utilization of digital technology. Individuals who perceive digital technology as useful and easy to use are more likely to adopt and utilize it. The regression analysis confirmed the importance of perceived usefulness and ease of use as predictors of the adoption/utilization of digital technology. Higher levels of perceived usefulness and ease of use were associated with increased adoption/utilization rates. Overall, these findings contribute to the existing body of knowledge and emphasize the significance of digital literacy, demographic factors, and perceived usefulness and ease of use in bridging the digital divide and fostering the adoption and utilization of digital technology in rural areas. The study highlights the importance of enhancing literacy rates, addressing demographic variations, and emphasizing user-centric design and usability in promoting the adoption and utilization of digital technology in rural communities.

2025, Asian Journal of Dairy and Food Research, Volume 44 Issue 2 (April 2025)

In the fourth industrial revolution, digital technologies are being utilized to construct efficient and sustainable dairy farms by combining various technology and resources. It is now more important than ever to embrace innovation and developing technologies in order to tackle the current global concerns by automating and modernizing the operations. However, the industry faces challenges in adopting new technologies, such as limited access to advanced technologies, inadequate infrastructure and a knowledge deficit among farmers. This article gives an overview of the evolution of dairy 4.0 from dairy 1.0. in resonance with the evolution of Industry 4.0. This is a review paper that investigated past literatures to identify the components of Dairy 4.0 that are supportive to sustainable innovation. The Indian dairy industry has transformed significantly due to technological advancements, including automation, precision farming and Industry 4.0 systems. The results show that robotics, 3D printing, artificial intelligence, Internet of Things, big data and blockchain are among the core facilitating technologies of Dairy 4.0. These innovations improve production, efficiency and milk quality, while also aiding breeding, herd management and sustainability. However, ongoing investment in technology infrastructure is necessary to ensure maximum production and productivity.

2025, International Journal of Engineering and Management Research

This paper explores the symbiotic relationship between Artificial Intelligence (AI) and the Internet of Things (IoT), highlighting the significant role that AI plays in enhancing IoT applications. The paper begins by providing an overview of both AI and IoT technologies and their individual capabilities. It then delves into the ways in which AI augments IoT systems, including data analytics, predictive modeling, anomaly detection, and autonomous decision-making.

2025

Financial technology (fintech) applications increasingly rely on continuous data streams from Internet-of-Things (IoT) devices-such as payment terminals, mobile banking platforms, and point-of-sale (POS) systemsto drive real-time analytics. This paper addresses the challenges of real-time data analytics in fintech, including strict low-latency requirements, high-volume heterogeneous data, and stringent security and compliance demands. We propose an edge-cloud collaborative architecture that distributes analytic workloads between edge devices (near data sources) and the cloud, to enable timely processing of IoT data streams without sacrificing scalability or accuracy. The proposed architecture is tailored to fintech use cases, with an emphasis on instant fraud detection, transaction monitoring, and customer experience optimization. We design and evaluate the architecture through a prototype implementation, including data flow diagrams, a layered processing pipeline, and a performance evaluation measuring latency, throughput, and scalability. Experimental results demonstrate that the edge-cloud approach significantly reduces end-to-end latency (often by an order of magnitude) and improves throughput under load, compared to a cloud-only deployment. We also discuss how the architecture supports continuous model training and adaptation, data security (keeping sensitive data at the edge when possible), and regulatory compliance. Relevant methods and technologies co-authored by Akash Vijayrao Chaudhari-including IoT data warehousing, federated learning for distributed analytics, and AI-driven fintech anomaly detection-are integrated and cited to situate our contributions in the state-of-the-art. The paper concludes that an edge-cloud collaborative paradigm is a promising foundation for next-generation fintech analytics systems, combining the agility of edge computing with the power of cloud-scale data processing.

2025, SUNDAY IKENNA AGU

Agriculture has long been a key sector in Nigeria, providing employment, raw materials for industries, and contributing significantly to GDP. However, the sector faces numerous challenges, including outdated farming techniques, inadequate infrastructure, post-harvest losses, climate change, and limited access to financing. The integration of technology and innovation has emerged as a transformative solution, offering modern techniques such as precision farming, biotechnology, mechanization, and digital platforms to enhance productivity. This paper explores the role of technology and innovation in revolutionizing agriculture in Nigeria, highlighting key components, challenges, strategies, and economic impacts. It further discusses the urgent need for government intervention, private sector involvement, and research-driven policies to fully harness the benefits of agricultural innovation.

2025, SRF Publication Jabalpur (M.P.), INDIA

Machine learning (ML) is changing cybersecurity by enabling progressed discovery, anticipation and reaction instruments. This paper gives a comprehensive survey of ML's part in cybersecurity, looking at both hypothetical systems and down to earth usage. It diagrams the rising dangers focusing on ML models, such as ill-disposed assaults, information harming and show reversal assaults and examines state-of-the-art defense procedures, counting ill-disposed preparing, vigorous models and differential protection. Furthermore, the paper investigates different ML applications in cybersecurity from interruption location to malware classification, highlighting their affect on improving security measures. An peculiarity induction calculation is proposed for the early discovery of cyber-intrusions at the substations. Cybersecurity has ended up a imperative investigate range. The paper concludes with a discourse on the key inquire about bearings and best hones for making secure and versatile ML frameworks in a data-driven world. This paper dives into how Machine Learning (ML) revolutionizes cybersecurity, enabling progressed discovery, avoidance, and reaction components. It offers a exhaustive investigation of ML's urgent part in cybersecurity, enveloping hypothetical systems and viable applications. It addresses rising dangers like ill-disposed assaults and information harming, nearby cutting-edge defense techniques such as antagonistic preparing and strong models.

2025

As individuals increasingly interact with algorithms in a work context, it is important to understand these new types of ‘human-algorithm’ relationships. We investigate the human-algorithm interaction between Uber drivers and the Uber driver app in managing customers, routes and fares. This research-in-progress paper reports on initial findings from an ongoing study, from interviews with ten Uber drivers in the United States. Our findings illustrate that Uber drivers experience role ambiguity and role conflict as they attribute different roles to the algorithms embedded in their app. The literature shows that ambiguity and conflict create workplace uncertainty. We expand on it by identifying several new sources of role ambiguity and role conflict that emerge between the driver and the algorithm. Our initial results are positioned within the literature that studies the emerging role of algorithms at work.

2025, Proceedings of the 10th World Congress on Civil, Structural, and Environmental Engineering (CSEE 2025) Barcelona, Spain - April, 2025

In this present paper, four point loading configuration was used to assess the effect of steel fibre volume (Vf), type of steel fibre, presence/absence of stirrup and shear span to depth ratio (a/d) on the ductility and strain behaviour of the UHPFRC-CA beam. Findings from the study revealed that a/d has the most influence on the ductility of UHPFRC-CA beam as it favours the development of new cracks, propagation of existing cracks and the realization of high shear capacity. Vf of up to 2% improves UHPFRC-CA beam's ductility beyond which leads to ductility reduction. Shear reinforcement in form of stirrups does not have significant impact on the ductility of UHPFRC-CA beam. UHPFRC-CA beam has lower compressive strain and higher tensile strain than the compressive strain and tensile strain of its cube specimen and dogbone specimen respectively. The use of hooked-end steel fibre and increase in Vf from 2% to 3% in the beam resulted in strain reduction in the compression zone of the UHPFRC-CA beam. The strain in the tension zone of UHPFRC-CA beam increased with the use of hooked-end steel fibre, increase in Vf from 2% to 3%, exclusion of stirrups and decrease in a/d from 2.82 to 2.08. The shear zone of UHPFRC-CA beam is characterized with low strain growth rate from the appearance of diagonal crack to failure; and the shear zone of UHPFRC-CA beam experienced strain reduction with the use of hooked-end steel fibre and increase in Vf from 2% to 3%. Finally, findings from the ductility and strain behaviour of this UHPFRC-CA beams can be utilized during research design and experimental stages to forecast the crack pattern and failure mode of UHPFRC-CA beam in terms of concrete crushing.

2025, IEEE

This research is a thorough study to meet the traditional method of cultivating oyster mushrooms using solar-powered Internet of Things systems. In this study, different research methods were used, developmental, experimental, and correlational research. The developmental research was used to develop the solarpowered IoT that uses sensors, data transmission protocols, and uses energy to be more sustainable, energyefficient, and able to operate in remote locations, particularly in areas where the traditional source is often unavailable. The experimental design was used to test if the solar-powered IoT system effectively cultivates oyster mushrooms. The experiment was done by designing two groups, experimental that used solar-powered IoT, and controlled that used traditional methods of growing oyster mushrooms. The last design is correlational, it was used to find out what environmental factors most significantly impact the growth of oyster mushrooms. The result shows that the group that used solar-powered IoT technology has an improvement in cap diameter, height, and stem count with p-values below 0.001, indicating significant enhancements. Although there were no differences in weight, mushrooms from the IoT group tended to be heavier. It was discovered in this study that it can help to improve the cultivation of mushrooms, increase the yield, and above all the use of renewable energy, a long-term operation that no longer depends on traditional power sources.

2025, International Journal of Science and Research (IJSR)

In today's healthcare environment, improving patient outcomes while maintaining operational efficiency is paramount. This paper explores the strategic use of client group health information insights to achieve these goals. By analyzing aggregated data from specific groups, such as community populations or corporate health programs, healthcare providers can gain valuable insights into prevalent health trends and risk factors. These insights facilitate the development of targeted interventions, optimized resource allocation, and enhanced treatment protocols, ultimately leading to improved clinical outcomes and reduced costs. The paper discusses strategies for effective data collection, integration, and analysis, as well as the importance of collaboration with stakeholders. It also addresses challenges such as data privacy and interoperability, offering solutions to mitigate these issues. By harnessing group health information, healthcare organizations can drive operational excellence and deliver higher-quality, patient-centered care.

2025, preprint.org

The rapid expansion of the Internet of Things (IoT) has introduced transformative benefits across various sectors, but it has also exposed significant security vulnerabilities. These vulnerabilities are increasingly being exploited by cybercriminals, posing a major threat to the integrity of IoT systems. This paper, titled "Enhancing IoT Systems: Cybercrime Prevention through Security Vulnerability Management", aims to identify key security weaknesses in IoT devices and networks and proposes effective strategies to mitigate associated cybercrime risks. The study adopts a multifaceted approach, combining a thorough review of existing literature with real-world case studies to explore prevalent IoT vulnerabilities. Additionally, it evaluates current prevention techniques, such as encryption, AI-based intrusion detection, and Blockchain, comparing their effectiveness in securing IoT systems. The proposed solutions are tested in simulated IoT environments, demonstrating their capacity to significantly reduce the risk of cyberattacks. Key findings indicate that industrial IoT and healthcare IoT systems are especially vulnerable, highlighting the need for advanced risk management frameworks tailored to these sectors. The paper concludes that integrating innovative technologies such as AI and Blockchain, alongside regular security updates, can enhance IoT security and play a critical role in preventing cybercrime. The implications of this research extend to the development of safer IoT infrastructures, with a focus on strengthening global cybercrime defense mechanisms.

2025

The Detection and Prevention system against several attacks has been developed in wireless sensor networks to secure the information and to provide the uninterrupted service to the reliable clients. The formulation of opinion of nodes of different neighbors or Trust value plays important role in the detection system to avoid attacks. The attack detection system always follows the behaviors of nodes to identify the attack formats and prediction of future attacks. In this paper, the Genetic Algorithm theorem is designed that gives better result by employing different parameters. This proposed work is more reliable and efficient than the previous and the results are proved and verified in MATLAB. By using this algorithm truth values of nodes will be maintained better and the accuracy of system will be enhanced through this methodology.

2025, Journal IJETRM

The increasing concern over health monitoring and safety has led to the development of intelligent systems that assist in real-time health assessments. This paper presents a Smart Breath Analyzer system utilizing an Arduino Uno controller, an I2C LCD display, a DHT11 temperature and humidity sensor, an APR33A3 voice module, and a heartbeat sensor to monitor an individual's health status effectively. The system continuously measures heart rate using a heartbeat sensor. If the heart rate exceeds or falls below the normal threshold, the voice module (APR33A3) triggers an alert instructing the user to perform a breath analysis. The breath analyzer then evaluates the breath sample and provides an output that can indicate intoxication, respiratory issues, or abnormal breath patterns. The I2C LCD displays real-time readings of heart rate, temperature, and breath analysis results. This system is beneficial for health monitoring, law enforcement, and personal safety applications, ensuring timely detection of abnormal conditions and providing immediate alerts. The proposed solution is low-cost, portable, and easy to implement, making it suitable for both medical and non-medical applications.

2025

The historical role of phone numbers as primary communication identifiers is undergoing a transformative shift, driven by technological advancements and evolving user demands. While phone numbers have traditionally facilitated personal and professional interactions, their limitations, including privacy vulnerabilities and declining utility in digital ecosystems, are increasingly apparent. Emerging alternatives such as unified digital identities, app-based communication platforms, and biometric authentication methods offer enhanced privacy, security, and user experience. Technologies like IoT, AI, and blockchain are further accelerating this transition by enabling seamless device interactions, personalized communication, and secure identity verification without reliance on static identifiers. However, the shift faces significant challenges, including infrastructure dependency, cultural resistance, and the need for cohesive regulatory frameworks. Despite these barriers, the decline of traditional phone numbers is anticipated by 2030, as digital identifiers gain prominence across industries, fostering global connectivity and inclusivity. This transition heralds a profound redefinition of societal communication practices, emphasizing innovation while ensuring equitable access to emerging technologies.

2025

Substation automation has become very necessary to improve the monitoring process and the workflow of the station, and it is necessary to detect the type of restriction that is taking place, therefore, it needs a monitoring system that is able to automatically detect, monitor and classify the existing restrictions. The purpose of this project is to remotely obtain electrical parameters such as voltage, current, temperature, humidity and flame and send the values in real time over the network. In addition, this system is designed to send alerts whenever the voltage or current exceeds pre-set limits. This project uses the IoT platform (Ubidots), in addition to the ESP32 microcontroller. The controller can communicate efficiently with the different sensors used.

2025, Independent Research Manuscript

As artificial intelligence (AI) continues to advance, so do the threats it enables in the cybersecurity landscape. This paper explores the dual-edged nature of AI in both defending against and facilitating cyberattacks. We analyze current trends in AI-powered threats such as phishing, malware generation, and social engineering, as well as the increasing use of deep learning for evading traditional security systems. In response, we propose a multi-layered defense framework combining AI-driven detection, explainable machine learning, and threat intelligence sharing. Our approach highlights the importance of transparency, adaptability, and proactive threat modeling to protect organizations from rapidly evolving AI-based attacks.

2025, International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

Efficient task scheduling in multi-cloud environments is crucial for optimizing resource utilization, reducing execution time, minimizing costs, and enhancing overall system performance. Traditional scheduling approaches, including heuristic and rule-based methods, often struggle with dynamic workload fluctuations and resource heterogeneity, leading to inefficiencies. This study proposes a reinforcement learning-based scheduling framework that dynamically adapts to real-time cloud conditions to optimize task allocation. The problem is formulated as a Markov Decision Process (MDP), and a deep reinforcement learning model is trained to learn optimal scheduling policies. Experimental results demonstrate that the proposed approach significantly reduces makespan, improves resource utilization, lowers energy consumption, and decreases operational costs compared to traditional scheduling methods. The model successfully adapts to varying workload intensities and different cloud configurations, proving its scalability and robustness. Comparative analysis with heuristic-based and rule-based scheduling techniques further validates the superiority of reinforcement learning in optimizing multi-cloud task scheduling. Despite initial computational overhead during training, the proposed model offers long-term performance benefits, making it a viable solution for real-world cloud computing applications. The study highlights the potential of reinforcement learning to revolutionize cloud resource management and lays the foundation for future advancements in autonomous, intelligent cloud scheduling frameworks.

2025, International Journal of Science and Research

In today's digital era, where data holds immense value and the internet spans globally, the proliferation of digital transactions and assets has imposed greater security responsibilities on technology companies and financial institutions. Simultaneously, the exponential advancement of artificial intelligence (AI) has introduced a dual-use technology that, if exploited by cybercriminals, can significantly undermine cybersecurity. Traditional signature-based defense mechanisms are increasingly inadequate against evolving threats, positioning User and Entity Behavior Analytics (UEBA) as a vital component in modern cybersecurity frameworks. By identifying deviations from baseline user and device behavior, UEBA solutions enhance the detection of anomalous and malicious activities that often bypass conventional defenses. This paper presents a comprehensive analysis of UEBA's role in fortifying enterprise cybersecurity, detailing its architectural design, deployment strategies, and integration within contemporary environments. Furthermore, the paper addresses the challenges of large-scale UEBA implementation and outlines prospective avenues for future research.

2025

Concerns about resilience and cybersecurity have grown as next-generation networks, including 5G and beyond, get bigger and more complicated. The complexity of these more complex and dynamic attacks is surpassing the capabilities of conventional network security methods. The security and resilience of next-generation networks can now be enhanced with AI-driven frameworks. These frameworks use AI techniques like machine learning, deep learning, and others to dynamically detect, stop, and lessen cyberattacks, providing real-time defence against emerging attack vectors. These frameworks combine automatic reaction mechanisms, predictive analytics, and AIdriven anomaly detection to lower the risk of breaches. This allows them to adjust to new threats more quickly than with conventional techniques. Additionally, by identifying possible failures or disruptions, optimising traffic flow, and enhancing resource allocation, AI can improve network resilience and enable proactive measures to guarantee service continuity. This study examines the use of AI-powered frameworks to improve nextgeneration networks' resilience and cybersecurity, going into the underlying technology, advantages, difficulties, and possible effects. Additionally, it looks at how AI may enhance current security procedures, speed up incident response times, and make stronger network architectures possible. In the end, AI-powered frameworks are well-positioned to be crucial in safeguarding communications infrastructure in the future by providing intelligent, scalable, and adaptable defences against more complex cyberthreats for next-generation networks.

2025, IEEE International Conference 2025 AI-Driven Smart Healthcare for Society 5.0 (AdSoc 5.0)At: Kolkata, India

Health monitoring has become a major concern in current society especially due to the continuously rising incidences of chronic ailments. Some of the primary monitoring techniques face a severe disadvantage in that they are incapable of identifying even the subtlest of health changes in real-time. Other systems also have difficulties handling large scales, nonlinear, dynamic data, for instance, from wearables' sensors. To deal with such problems, this work developed a new approach based on Autoencoder for learning the features without requiring the label and GAN for the online detection of the anomalous data in real-time health information. It involves wearables with IoT for perpetually monitoring the patients and using sophisticated detection algorithms for feasible cloud analysis. The initial performance shows a better accuracy/precision, and recall compared to the conventional models and serves as an efficient means to provide timely basic health intelligence.

2025, Transactions on Emerging Telecommunications Technologies

2025

This study introduces an advanced smart walking stick engineered to assist visually impaired individuals by merging multiple sensor technologies with an intelligent voice guidance system and robust emergency communication capabilities. The design capitalizes on sensor fusion by integrating IR and ultrasonic sensors for multi-range obstacle detection, supplemented by a moisture sensor to alert users to hazardous conditions, and an accelerometer to monitor orientation. An ATmega328-based Arduino Nano processes these inputs in real time, triggering a dedicated voice playback module to deliver prompt, context-aware auditory feedback. In parallel, integrated GPS and GSM modules continuously track the user's position and dispatch automated SMS alerts during emergencies. Extensive laboratory tests and field trials validate that the system reliably detects obstacles, provides adaptive voice prompts, and ensures timely emergency interventionultimately enhancing independent navigation and user safety. The proposed hybrid design combines the strengths of existing technologies into a singular, cohesive device, representing a significant advancement in assistive tools for the visually impaired.

2025, Journal of Informatics Education and Research

The business is not just confined to selling products and services. Customization of products and services according to customer satisfaction has become an integral part of business. Consumer sentiment analysis has become an important part of business models. Businesses are also striving towards transform consumer experience, loyalty, and satisfaction using artificial intelligence (AI) concept specially machine learning (ML) and deep learning (DL) which are further used in sentiment analysis. Businesses can efficiently comprehend and respond to clients' comments and emotions by using sentiment analysis. Companies can now monitor vast amounts of customer interactions on a variety of platforms, including social platform, opinion, and support queries in real time. In addition to making it easier to understand consumer sentiment, AI-driven sentiment analysis technologies also make it possible to forecast trends and identify problems early on. Businesses may enhance the whole customer experience by improving products by features, personalizing client interactions, and refining strategy by using predictive capability. To provide a complete picture of customer interactions and preferences, current developments highlight the significance of combining sentiment analysis driven by AI with customer relationship management (CRM) systems. With an emphasis on sentiment analysis in commercial contexts, this study provides a comprehensive overview of the most recent developments in AI and ML and their ramifications in business performance optimization.

2025

تواجه الزراعة الحديثة العديد من التحديات، مثل تغير المناخ، وزيادة عدد السكان، ونقص الموارد الطبيعية. وفي هذا السياق، تقدم تكنولوجيا أجهزة الاستشعار اللاسلكية والابتكارات الهندسية حلولًا فعالة لجعل الزراعة أكثر استدامة. تتيح هذه التقنيات جمع وتحليل البيانات في الوقت الفعلي، مما يساعد المزارعين على اتخاذ قرارات دقيقة لتحسين إنتاجية المحاصيل وتقليل التأثير البيئي.

2025, Düzce Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

ABSTRACT: Increasing consumer interaction thanks to the development of communication facilities has great importance on company decisions. Digital transformation, one of these decisions, necessitated the transfer of actual business processes to digital. The central dynamic that reveals this necessity is the digitalization of the consumer. With the digitalization of the consumer, the attitudes of the companies towards the consumers and the expectations of the consumers from the companies have changed. In this context, it has been aimed to compile and examine the studies with the keywords "customer" and "consumer" in the publications on digital transformation in the WOS (Web of Science) database. Therefore, this study examines academically qualified studies on both digital transformation and consumers through a conceptual framework. On digital transformation, a current concept, some remarkable consumer-oriented studies have been carried out in Germany, Russia, Italy, the USA, Spain, and England. It has been observed that the keywords of digitalization, big data, e-commerce, artificial intelligence, machine learning, and digital entrepreneurship are the subject matters studied in this context. These are the most frequently used keywords in digital transformation and digital consumer. It also reveals possible literature gaps on which future studies in this context can focus. The research results extracted from 432 studies provide an overview of the studies on the digital transformation between 2010-2021. It also contributes to the literature by visualizing the role of the consumer in digital transformation.
KEYWORDS: Digital Transformation, Digital Marketing, Consumer, Bibliometric Analysis /////
ÖZET: İletişim olanaklarının gelişmesi nedeniyle artan tüketici etkileşimi, şirket kararlarında büyük öneme sahip olmuştur. Bu kararlardan biri olan dijital dönüşüm, gerçek iş süreçlerinin dijitale aktarılmasını zorunlu kılmıştır. Bu gerekliliği ortaya çıkaran temel dinamik ise tüketicinin dijitalleşmesidir. Tüketicilerin dijitalleşmesi sayesinde firmaların tüketicilere yönelik tutumu ve tüketicilerin onlardan beklentileri değişime uğramıştır. Bu bağlamda çalışmada, WOS (Web of Science) veri tabanında yer alan dijital dönüşüm üzerine yazılmış çalışmalardan "müşteri" ve "tüketici" anahtar kelimelerini içerenlerin toplanması ve incelenmesi amaçlanmıştır. Dolayısıyla çalışma, dijital dönüşüm üzerine yazılmış akademik açıdan nitelikli çalışmaları kavramsal bir çerçeve üzerinden incelenmektedir. Güncel bir kavram olan dijital dönüşüm konusunda Almanya, Rusya, İtalya, ABD, İspanya ve İngiltere'de tüketici yönelimli dikkat çekici çalışmalar yapılmıştır. Dijitalleşme, büyük veri, e-ticaret, yapay zekâ, makine öğrenimi ve dijital girişimcilik anahtar kelimelerinin bu bağlamda çalışıldığı gözlemlenmiştir. Dijital dönüşüm ve dijital tüketici bağlamında en çok kullanılan kelimeler olmuştur. Ayrıca çalışma, ileride bu alanda yapılacak çalışmaların odaklanması muhtemel literatür boşluklarını açığa çıkarmayı hedeflemektedir. 432 çalışmanın incelenmesiyle ortaya çıkarılan sonuçlar, dijital dönüşüm alanında 2010-2021 yılında dijital dönüşüm alında yapılan çalışmalara yönelik bir genel bakış sağlamaktadır. Çalışma ayrıca literatüre dijital dönüşümün rolünün literatür destekli olarak görselleştirilmesi ile de katkı sağlamaktadır.
ANAHTAR KELİMELER: Dijital Dönüşüm, Dijital Pazarlama, Tüketici, Bibliyometrik Analiz

2025, iaeme publication

The healthcare industry faces increasing pressure to deliver high-quality patient care while managing limited resources efficiently. Predictive analytics, enabled by critical and emerging technologies (CETs) such as artificial intelligence (AI), machine learning (ML), cloud computing, and the Internet of Things (IoT), is transforming healthcare operations. This paper reviews the application of CETs in predictive analytics to improve patient outcomes and optimize resource allocation. By synthesizing data from academic research, healthcare case studies, and industry reports, we examine the potential of these technologies in predicting patient outcomes, preventing adverse events, and managing healthcare resources. Furthermore, the paper discusses the challenges and limitations of adopting predictive analytics in healthcare and provides recommendations for future research and implementation.

2025, International Research Journal of Modernization in Engineering Technology and Science

The digital revolution has transformed how individuals live, work, and interact, but it has also introduced significant risks, particularly for women in cyberspace. Cyberbullying, online harassment, identity theft, data privacy violations, and electronic surveillance not only threaten personal security but also undermine women's confidence and participation in the digital sphere. At the same time, emerging technological advancements offer promising solutions to enhance online safety. Innovations such as IoT-based safety devices, AI-driven threat detection systems, and mobile safety applications demonstrate the potential for technology to counteract these threats. This paper explores the dual impact of technological advancements-how they create vulnerabilities while serving as tools for empowerment and protection. It highlights the importance of ethical design, robust regulatory frameworks, and multidisciplinary collaboration in developing sustainable and inclusive safety solutions. Additionally, the innovation potential of Generative AI (Gen AI) is examined, demonstrating how it can drive proactive safety measures and support digital security initiatives. The findings emphasize the critical need for ongoing research and policymaking to ensure that technological innovation does not exacerbate existing inequalities but instead safeguards and empowers women in the digital age.

2025, International Journal of Research Publication and Reviews

The study explores the significance of the Internet of Things (IoT) in enhancing the efficiency and effectiveness of business operations, particularly in retail stores. It highlights how IoT applications streamline various processes such as inventory management, order processing, and billing, thereby reducing time consumption and improving overall performance. The research examines the factors influencing the adoption of IoT, including technical aspects, organizational support, complexity, security concerns, regulatory frameworks, and cost considerations. The study also analyzes the demographic profile of respondents, revealing that most participants are male, belong to the 20-30 age group, hold undergraduate degrees, and have less than one year of work experience, though many possess prior work exposure. The findings suggest that implementing IoT significantly reduces the time required for key business processes, with most activities being completed in under 5 minutes. The research concludes that while IoT adoption positively impacts operational efficiency, proper training and awareness about IoT applications are essential for maximizing its benefits. Recommendations include hiring a diverse workforce, providing training on IoT, and ensuring a balanced gender mix to further enhance business performance.