The integration of Artificial Intelligence (AI) into the Internet of Things (IoT) ecosystem has transformed the landscape of cybersecurity. While IoT systems enable ubiquitous connectivity and automation across industries, they are also highly susceptible to cyberattacks due to their heterogeneity, limited resources, and scalability challenges. AI-based techniques, including machine learning, deep learning, and reinforcement learning, offer promising approaches to detecting anomalies, preventing intrusions, and predicting emerging threats in IoT networks. However, these opportunities are accompanied by significant challenges such as adversarial attacks, data privacy concerns, computational limitations, and the interpretability of AI models. This review article critically analyzes the dual role of AI in IoT security, highlighting its potential as both a defender and an enabler of cyber threats. Various AI-driven techniques are systematically reviewed, their applications in IoT security are discussed, and emerging risks are evaluated. The article further identifies future directions, emphasizing the importance of explainable AI, lightweight security frameworks, and robust adversarial defense mechanisms for sustainable and resilient IoT ecosystems.
This paper explores the rise and influence of chatbots in the digital age, driven by advancements in natural language processing (NLP) and artificial intelligence (AI). We examine their historical development, practical applications across diverse sectors, and the challenges faced in their deployment. Through literature review and practical case analysis, we evaluate chatbot performance in domains such as customer service, healthcare, and education. Additionally, the study discusses key performance metrics, user satisfaction, ethical considerations, and future prospects. The findings offer insights into enhancing chatbot design and effectiveness, supporting ongoing innovation in AI-powered conversational systems.
The rapid evolution of Artificial Intelligence (AI) has significantly impacted cybersecurity, offering both opportunities and challenges. While AI enhances threat detection, anomaly identification, and automated response systems, it also introduces new vulnerabilities, such as adversarial attacks and AI-powered cyber threats. This PhD thesis explores the intersection of AI and security, focusing on machine learning (ML) and deep learning (DL) techniques for cyber defense, the risks posed by malicious AI, and strategies to mitigate these threats. The research proposes novel AI-driven security frameworks, evaluates their effectiveness against emerging cyber threats, and discusses ethical considerations in AI-based security solutions.
As cloud-native architectures become more prevalent, service-based applications demand robust, scalable, and autonomous management strategies. This paper presents a decentralized self-adaptation mechanism using spectral clustering to dynamically manage services in a cloud environment. The proposed framework enables services to adapt to changing workloads and environmental conditions without centralized control, promoting resilience, scalability, and efficiency.
In an age of rapid digital communication and data exchange, the protection of multimedia information such as images and signals is of paramount importance. This paper presents a hybrid encryption approach that combines wavelet transform and permutation techniques to enhance data security. The proposed method leverages the multi-resolution capabilities of wavelet analysis to decompose data, followed by a permutation scheme to disrupt pixel or signal sample positions, ensuring high confusion and diffusion properties. The results demonstrate improved security metrics, including entropy, correlation coefficients, and histogram uniformity, compared to traditional encryption methods. This hybrid framework proves to be both efficient and robust for real-time multimedia encryption.
Virtual Machine (VM) mobility is crucial in dynamic cloud environments to achieve efficient resource utilization, high availability, and reduced operational costs. This paper investigates the advanced strategies for VM migration, focusing on optimization techniques, AI-driven scheduling, security concerns, and real-time migration scenarios. Simulation experiments demonstrate how these strategies enhance system performance, minimize downtime, and reduce energy consumption compared to traditional approaches.
The explosive growth of data across industries has challenged traditional data processing systems, necessitating scalable, efficient, and distributed solutions. This review explores the integration of the divide and conquer paradigm with parallel computing and cloud technologies to address Big Data handling. By examining current techniques, frameworks, and challenges, the article offers a comprehensive overview of how cloud-based parallel divide and conquer strategies optimize Big Data processing in terms of performance, scalability, and cost-efficiency.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the telecommunications industry through smarter networks, advanced customer engagement systems, predictive maintenance, and innovative services. This research provides an in-depth examination of AI and ML adoption in telecom, highlighting their growing role in improving operational efficiency, enhancing customer satisfaction, and shaping future telecom ecosystems. The paper also explores significant challenges including data privacy risks, high implementation costs, regulatory complexities, and workforce skill shortages. Using doctrinal research methodology, supplemented by case studies, surveys, and policy analysis, this study provides evidence-based insights. Data tables, figures, and adoption trends illustrate sectoral impacts, while international regulatory comparisons highlight contrasting approaches. Findings reveal that AI and ML are central to the future of telecom, but balanced investments in technology, expertise, and governance are vital. The study concludes with a framework for ethical, secure, and sustainable AI adoption in telecommunications.
This paper aims at building the causal relations and event structures13 to study the complex and evolving cyber – physical systems with illustrations of the reasoning based on Robotics24 and Policy Analysis25 for Communication Systems. An empirical analysis points to the realism that network security is also a geometric theory with safety and authentication tending to geometric formulae that make the larger structures. Security is very much a matter of perception too. The proposed approach also factors the perceptual aspects of the human mind. This paper includes several interesting possibilities for using linear algebra, discrete mathematics, analysis, and topology in the domains such as economics, game theory, robotics and biology to mention a few.
Wearable tracking gadget that school-age children can wear is the topic of this paper. It doesn't need any pricy technology to operate.. This technology is usable by persons of all educational levels. It has got two buttons one is alert button and another is panic button The major objective of this gadget is to ensure that the youngster may contact their parents in an emergency. The alert button allows the child to notify their parents of an emergency and provide their current location. For communication, the current technology includes Bluetooth, WI-FI, and RFID. It is hard to communicate across a great distance with these technologies because they only have a narrow range of coverage. Also they are not that accurate. This device solves the issue by utilizing GSM technology. Parents do not need to submit any special code to the device to determine the child's location's latitude and longitude. If a child is in any emergency situation and wants his/her parents to know their current exact location. If a child feels uneasy, there are two methods to let the parents know. The cell phone of the parents or guardian receives the alarm message via SMS by pressing alert button and if the child wants to communicate to parents immediately, a call can also be made via this device using the panic button and the child can talk to the parent in real time.
System security is very important, especially in the age that we live in. One of the ways to secure data is by creating a password that makes it difficult for unauthorized user to gain access to the system. However, what makes it difficult for the system to be attacked is directly dependent on approach used to create it, and how secured it is. Text based approach is the oldest authentication approach. It requires that the user supplies textual password in order to gain access to the system. However, this approach has shown a significant drawback and several vulnerabilities, one of which is the difficulty in recalling or remembering textual passwords. Several other attacks that textual passwords are vulnerable to include brute force attacks, shoulder spying, dictionary attacks etc. The introduction of graphical schemes made things a lot better. Graphical passwords make use of images. However, most graphical schemes are vulnerable to shoulder surfing attacks. In this research work, we developed two systems; A position-based multi-layer graphical user authentication system and an Image-based multi-layer graphical user authentication system. The reason behind this research work is to compare the two systems, and evaluate them based on three major performance metrics: (1) Security, (2) Reliability (3) Individual preference.
Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.
COVID-19 is an ongoing pandemic and dangerous of coronavirus disease 2019. It caused by the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 spreads most often when people are physically close. And, it spreads very easily and sustainably through the air, primarily via small droplets or aerosols, as an infected person breaths, coughs, sneezes, sings or speaks. The recommended preventive measures include hand washing, covering one’s mouth when sneezing or coughing, physical distancing, wearing a face mask in public, ventilation and air-filtering, disinfecting surfaces, and monitoring and self-isolation for people exposed or symptomatic. Hence, this paper proposed the architecture of physical distancing detection system with distance sensor which deployed on UAV System with the help of BTS (2G/3G/4G/5G) to enrich the COVID-19 preventive measures, as well as I have also developed a few algorithms for distance sensor (on UAV System) operations which sensing, tagging and disseminating the current distance, position and waiting time of resident’s information, and forwarding schemes to respective infrastructures and suspected residents including the security forces.
Cybersecurity is a combination of technologies, processes and operations that are designed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access1.The research was purposed to develop a cybersecurity culture framework which ensures that grassroot users of cyberspace are secured from cyber threats. Literature review showed that in Zimbabwe, no research had attempted to come up with a cybersecurity culture framework for grassroot users of cyberspace.The research was guided by the interpretivist paradigm and employed a qualitative methodology. A descriptive research design was used to answer the research questions and unstructured interviews were done to ascertain the cybersecurity needs and challenges of grassroot users of cyberspace. A cybersecurity culture framework was then crafted based on the research findings. The researchers recommended that Zimbabwe should have a cybersecurity vision and strategy that cascades to the grassroot users of cyberspace. Furthermore, the education curricula should be revised so that it incorporates cybersecurity courses at primary and secondary school level .This will then ensure that ICT adoption is matched with cyber hygiene and responsible use of cyberspace.
The purpose of this study is to provide new insights into the factors that influence cancellation behaviour with respect to hotel bookings. Cancellations of bookings are one of the most common concerns in the hotel sector. Before the specified arrival date, the client would cancel their reservation. The cancellation has had a major impact on hotel operations as well. The researchers developed a prototype system called Automated Hotel Booking Cancellation that can assist a hotel in better anticipating consumer cancellations and booking transactions. The researchers utilized a survey questionnaire following the ISO 25010 Software Quality Standard to assess the system's usefulness, functionality, accuracy, security, reliability, and maintainability. The results show "strongly agree" in all the domains with an overall mean = 3.46. IT Experts and respondents evaluated the proposed prototype and gained a "Strongly Agree" rating in all the domains under the survey. The domain on the usefulness of the system gained the highest mean = 3.89 with an interpretation of "strongly agree," and the lowest was the domain of security with a mean = 3.15 or "strongly agree," which ranked 6 in summary. The researchers concluded based on the findings that the developed prototype web-based system was useful and functional to the needs of the target users/beneficiaries. The user interface of the web-based system was user-friendly, representing an object-oriented user interface for the users. An improvement recommended on the cancellation to automatically predict cancellations that can be incorporated into the system using Machine Learning Algorithms to ease the process of cancellation were recommended for future researchers who want to pursue advanced studies about the topic.
Altera SDK for OpenCL allows programmers to write a simple code in OpenCL and abstracts all Field programmable gate array (FPGA) design complexity. The kernels are synthesized to equivalent circuits using the FPGA hardware recourses: Adaptive logic modules (ALMs), DSPs and Memory blocks. In this study, we developed a set of fifteen different benchmarks, each of which has its own characteristics. Benchmarks include with/without loop unrolling, have/have not atomic operations, have one/multiple kernels per single file, and in addition to one/more of these characteristics are combined. Altera OpenCL v14.0 adds more features compared with previous versions. A set of parameters chosen to compare the two OpenCL SDK versions: Logic utilization (in ALMs), total registers, RAM Blocks, total block memory bits, and clock frequency.
Quality of queuing service management is the aim of any organization providing its services to customers. The purpose of this paper is to evaluate the factors affecting the quality of service provided by the public sector depending on waiting lines. For this purpose, a questionnaire survey was conducted on a sample of 394 to collect data relating to customer satisfaction. The questionnaire consists of five factors: process, tangible, responsiveness, reliability, and empathy. The results showed that the degree of agreement of the factors; process, tangible, reliability, responsiveness, empathy, and service quality gain a neutral category, and all the factors have statistically significant effects on quality of service except the empathy factor. For the demographic information, the results showed that there are statistically significant differences for most of the demographic information. This paper extends the previous research that investigates factors affecting e-recruitment. The author extends the results of previous research related to the transparency of e-recruitment. The study recommended the service departments should make more effort in the way of providing services to improve the level of service quality management.
The flexible architecture offered by cloud computing allows for the dispersion of resources and data over numerous places, making it possible to access them from a variety of industrial settings. The use, storage, and sharing of resources such as data, services, and industrial applications have all changed as a result of cloud computing. In the past ten years, companies have quickly shifted to cloud computing in order to benefit from increased performance, lower costs, and more extensive access. Additionally, the internet of things (IoT) has significantly improved when cloud computing was incorporated. However, this quick shift to the cloud brought up a number of security concerns and challenges. Traditional security measures don't immediately apply to cloud-based systems and are occasionally inadequate. Despite the widespread use and proliferation of various cyber weapons, cloud platform issues and security concerns have been addressed over the last three years. Deep learning's (DL) quick development in the field of artificial intelligence (AI) has produced a number of advantages that can be used to cloud-based industrial security concerns. The following are some of the research's findings: We provide a detailed evaluation of the structure, services, configurations, and security fashions that enable cloud-primarily based IoT. We additionally classify cloud protection dangers in IoT into four foremost areas (records, network and carrier, programs, and gadgets). We discuss the technological issues raised in the literature before identifying key research gaps. In each class, describe the boundaries using a popular, artificial intelligence, and in-depth studying attitude. and security concerns relating to individuals), which are fully covered; we find and analyze the most recent cloud-primarily based IoT attack innovations; we identify, talk, and verify key safety challenges show the regulations from a standard, synthetic intelligence, and deep learning perspective in every class angle; we first present the technological difficulties identified in the literature before identifying IoT-based cloud infrastructure has significant research gaps which should be highlighted for future research orientations. Cloud computing and cyber security.
The SBM Robot is a manually operated robot focused on the Internet of Things and the robot operating system. The system is focused on washing and maintenance. The robot is controlled by an android app that uses the Google Real Time Firebase database to control the robot. HVAC duct is small in size and cleaning and repairing the duct is a very difficult task for humans, but it's easy to robot, robot goes inside the duct, and inspects and cleans the duct. Like similar, ships use fuel, oils, sludge, sewage, water and other fluids stored in tanks.
The term information electronic library it’s need and importance and takes a stock of some notable efforts being made to initiate information electronic library activities in different parts the world. The aim of electronic library is to explorer and to collect useful knowledge over above faster coping. Search and to distribute. So present paper discuses in electronic library services. It’s meaning, feature, feature, roll of librarians, services of e-libraries, advantages, disadvantages, and objective etc..
