International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The escalating complexity and sophistication of cyber threats necessitate a paradigm shift in ope... more The escalating complexity and sophistication of cyber threats necessitate a paradigm shift in operating system (OS) security. Traditional security measures, while effective against known vulnerabilities, struggle to adapt to dynamic attack vectors. This paper introduces Intelligent Defense, an innovative approach to OS security powered by Artificial Intelligence (AI). By integrating advanced machine learning algorithms and neural network architectures, Intelligent Defense transforms an operating system into an adaptive, self-learning security platform capable of identifying, predicting, and mitigating cyber threats in real-time. The study explores the architecture and functionality of an AI-powered OS, highlighting its ability to detect zero-day exploits, thwart malware intrusions, and provide a proactive response to emerging threats. Furthermore, the paper discusses the ethical considerations, computational challenges, and scalability of implementing AI in system-level security. Through case studies and simulations, we demonstrate the superior resilience and efficacy of Intelligent Defense compared to conventional OS security frameworks. This pioneering approach underscores the potential of AI to redefine the cybersecurity landscape, setting a new benchmark for OS security in the age of intelligent systems.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The significance of digital marketing has grown throughout time in the current digital era as a w... more The significance of digital marketing has grown throughout time in the current digital era as a way to provide clients with innovative properties for education, engagement, and the sale of goods and services. The purpose of the study is to evaluate research and development dynamics in the field of digital marketing (DM) from a multidisciplinary perspective by utilising bibliometric analysis to analyse the corpus of important research publications in the last twelve years (from 2012 to 2024). The Scopus database provided a total of 2403 published articles for the investigation. To provide more context, we evaluate patterns in the literature on digital marketing research in this study, considering the publication's year, author, keyword, and country. The findings showed that digital marketing research progressively grew during the study period. This bibliometric study generally provides the whole image of the field. It suggests that researchers focus on novel areas to add new findings and knowledge to the literature if they conduct digital marketing research.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The rapid evolution of cyber threats has created unprecedented challenges, particularly in techno... more The rapid evolution of cyber threats has created unprecedented challenges, particularly in technologically advanced regions like California, where digital infrastructure supports critical industries and millions of residents. This study investigates the application of artificial intelligence (AI) in predicting and mitigating cybersecurity risks, focusing on its transformative role in safeguarding California's digital landscape. By leveraging AI-driven technologies such as machine learning algorithms, neural networks, and natural language processing (NLP), the research demonstrates how these tools identify and neutralize threats before they escalate into breaches. For example, supervised machine learning models are used to detect anomalies in network traffic, while NLP-based tools analyze phishing emails to prevent social engineering attacks. AI-powered solutions like predictive analytics and real-time threat intelligence platforms showcase their ability to enhance cybersecurity frameworks through faster detection, improved accuracy, and reduced response times. The study also examines AI's integration with security systems like firewalls and intrusion detection systems, which are now bolstered by adaptive learning capabilities. Our findings illustrate that AI not only mitigates immediate risks but also fortifies long-term resilience against emerging cyber threats, making it a cornerstone of future cybersecurity strategies.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
This study introduces an innovative methodology for the efficient conceptual design of complex, m... more This study introduces an innovative methodology for the efficient conceptual design of complex, multidisciplinary systems that involve computationally intensive analyses and a vast array of design variables. A novel nearly-orthogonal sampling strategy with superior space-filling characteristics is employed to extract maximal insights into system behaviour using a significantly reduced number of trial designs. The sampled dataset serves as input for a metamodel constructed using advanced artificial neural networks, augmented by Transformer Networks to enhance the metamodel’s capacity for capturing intricate dependencies and complex interactions within the data. Furthermore, a stage-wise interconnection of discrete neural networks is proposed for trajectory metamodeling, effectively mitigating the dimensionality challenges inherent in traditional neural architectures. The optimization process integrates a hybrid approach, leveraging a Genetic Algorithm for global optimization in tandem with Sequential Quadratic Programming for localized refinement utilizing exact disciplinary analyses. The efficacy of the proposed methodology is demonstrated through its application to the conceptual design optimization of a multistage solid-fuelled space launch vehicle. The results reveal exceptional accuracy in approximating highly nonlinear functions, a substantial reduction in overall computational time, and significant minimization of the reliance on exhaustive disciplinary analyses, underscoring the transformative potential of this approach.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
In the rapidly evolving digital landscape, cyberattacks are becoming increasingly sophisticated, ... more In the rapidly evolving digital landscape, cyberattacks are becoming increasingly sophisticated, posing significant threats to organizations and individuals worldwide. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, offering advanced capabilities to detect, prevent, and mitigate these attacks. This article explores the integration of AI into cybersecurity frameworks, emphasizing its role in identifying anomalies, predicting potential threats, and automating response mechanisms. By leveraging machine learning algorithms and data-driven insights, AI enhances the speed and accuracy of threat detection, effectively combating challenges posed by modern cyberattacks. However, the adoption of AI also introduces new complexities, such as adversarial AI and ethical considerations. This paper delves into the dual nature of AI in cybersecurity, providing a comprehensive overview of its benefits, challenges, and future potential in safeguarding digital ecosystems against ever-evolving cyber threats.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
Microservices architecture, described with characteristics of being distributed and loosely coupl... more Microservices architecture, described with characteristics of being distributed and loosely coupled, has become popular in recent times for software development. It offers flexibility, scalability, and a fault tolerance that accompanies a different set of security challenges. The introduction of microservices architecture shifted the application development pattern as well as deployment pattern because the monolithic systems were broken down into smaller, independent, and scalable services, but its nature of being distributed generated certain specific security issues. This research paper explores security vulnerabilities related to microservices, analyzes specific problems they raise, and seeks to know the methods and best practices for reducing these threats. Discussed subjects include authentication and authorization, secure communication, data protection, service segregation, monitoring, and incident response. This paper discusses the critical security threats arising with microservices applications and those that include increased attack surface, API security, data protection, and IAM. We discuss the root cause of these weaknesses and then present a feasible approach to combating them. Then we proceed further and involve discussions about security as code and DevSecOps practices and new technologies like blockchain and zero-trust architecture for protecting microservices environments. Organizations can enjoy the benefits of microservices and still keep their applications safe from any kind of threat by identifying these challenges and applying suitable security strategies.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Heart disease is a global health concern because of eating patterns, office work cultures, and li... more Heart disease is a global health concern because of eating patterns, office work cultures, and lifestyle changes. A machine learning-based heart attack prediction system is like having a vigilant watchdog in the medical field. To estimate the danger of a heart attack, it all boils down to analyzing data and complex algorithms. Four primary categories were established at the outset of this study: age, gender, BMI, and blood pressure. The data on heart illness was then classified using a variety of machine learning approaches, including XGBoost Model, Gradient Boosting Model, Random Forest, Logistic Regression, and Decision Trees. The results in terms of accuracy, false positive rate, precision, sensitivity, and specificity were then compared. Results in terms of accuracy, precision, recall, and f1_score were found to be greatest when using Logistic Regression (LR). It is therefore strongly recommended that data on cardiac disease can be classified using the logistic regression technique.
This paper explores the potential for optimizing the Internet of Things by integrating machine le... more This paper explores the potential for optimizing the Internet of Things by integrating machine learning and computer vision technologies and its implications for U.S. national security and economic competitiveness. First, the application of machine learning in IoT device optimization is introduced, emphasizing its ability to improve system intelligence and efficiency. Secondly, the critical role of computer vision technology in monitoring and reacting to changes in the physical environment is discussed, especially in security applications, such as the protection of national infrastructure and border security. Finally, the strategic significance of integrating these technologies in national security strategy and economic development is analyzed, and the direction and challenge of future research are put forward. .
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The main goal of CT-based triage is to shorten the time it takes to reach the expert opinion for ... more The main goal of CT-based triage is to shorten the time it takes to reach the expert opinion for patients in emergency situations, especially in cases of cranial fractures and intracranial hemorrhage. Increasing the performance in this regard may be possible with the use of artificial intelligence-supported software that may pre-scan the images and put them in order of urgency before the human triage officer is able to evaluate them. The project involves the development software that quickly classifies cases into one of two groups, urgent or non-urgent, by analysing the computerized tomography (CT) image of the brain taken without the administration of intravenous contrast material. In this way, more effective triage is aimed. The software developed for this purpose was observed to have high performances in two separate machine learning models. Additionally, a visual interface that allows viewing DICOM files was developed within the scope of the project.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The rapid advancement of digital health technologies and sensor innovations has transformed weara... more The rapid advancement of digital health technologies and sensor innovations has transformed wearable health monitoring systems, enabling unprecedented levels of personalized care, real-time health tracking, and early disease detection. This paper explores the pivotal role of these technologies in revolutionizing the healthcare landscape. We examine the integration of cutting-edge sensors, including biosensors, motion sensors, and environmental sensors, within wearable devices, which allow for continuous monitoring of physiological parameters such as heart rate, blood pressure, glucose levels, and physical activity. The paper also highlights the growing impact of artificial intelligence (AI) and machine learning (ML) in enhancing the accuracy, predictive capabilities, and decision-making processes of these systems. Furthermore, we discuss the challenges of data privacy, system interoperability, and the need for robust regulatory frameworks to ensure the safe and effective implementation of wearable health devices. In conclusion, we propose that the continued evolution of digital health technologies and sensors will play a crucial role in the future of preventive healthcare, offering new opportunities for improving health outcomes and reducing the burden on traditional healthcare infrastructures.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
India is having best weather system to apply the solar power system to individual home for people... more India is having best weather system to apply the solar power system to individual home for people’s regular day to day working devices. Sunlight The local weather has a significant impact on photovoltaic systems, with dust being the most significant factor. Dust accumulation on the surface of photovoltaic (PV) panels prevents sunlight from reaching the cells, which ultimately lowers the system's power output. Depending on the location, PV integration strategy, and size of the PV power plant, regular cleaning is necessary to prevent dust-based power losses. This publication examines the effects of dust buildup on solar systems, including radiation loss and output power operation, and establishes the ideal cleaning interval. In the current sector recharging and battery maintenance is one of the most difficult in our day to day life. This paper explains the novel technology help to analyse the battery level. The systems find the level of battery as well as automatically recharge the device using solar power system. So for this devices can recharge automatically and stop recharging after completing the battery level is full. Communication is one of the most preferable and should not avoidable. This method help to recharge the mobile devices without socketing and plugin device for our regular power supply. The proposed ARTSPS helps to easy recharging is possible while we are going to apply this novel revolution. ARTSRS Solar panels are designed to work in all weather conditions. The only factor that can affect a solar installation is snow accumulation, which can reduce production due to shading.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Medical report generation demands accurate abnormality detection and precise description generati... more Medical report generation demands accurate abnormality detection and precise description generation from CT images. While large language models have shown promising results in natural language processing tasks, their application in medical imaging analysis faces challenges due to the complexity of fine-grained feature detection and the requirement for domain-specific knowledge. This paper presents a novel framework integrating large language models with specialized medical image processing techniques for fine-grained abnormality detection and natural language description generation. Our approach incorporates a multi-modal knowledge enhancement module and a hierarchical attention mechanism to bridge the gap between visual understanding and textual description. The framework employs an adapter-based architecture for efficient domain adaptation and introduces a medical knowledge-enhanced loss function to improve description accuracy. Experimental results on three public datasets demonstrate the effectiveness of our approach, achieving 94.6% detection accuracy and a BLEU-4 score of 0.421 for description generation, surpassing current state-of-the-art methods. The system shows particular strength in handling subtle abnormalities, with a 91.2% average precision in fine-grained detection tasks. Comprehensive ablation studies validate the contribution of each component, while qualitative analysis demonstrates the clinical relevance of generated descriptions. The proposed framework represents a significant advancement in automated medical image analysis, offering potential benefits for clinical workflow optimization and diagnostic support.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This research aims at examining the strategies of performance tuning in cloud computing with emph... more This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments....
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The rapid evolution of drone technology has expanded its applications across variodus domains, in... more The rapid evolution of drone technology has expanded its applications across variodus domains, including delivery services, environmental monitoring, and search and rescue operations. However, many of these applications face significant challenges in GPS-denied environments, such as dense urban areas and heavily forested regions, where traditional navigation methods falter. This paper presents a novel multi-sensor fusion algorithm designed to enhance the localization accuracy of autonomous drones without reliance on GPS. By integrating data from an Inertial Measurement Unit (IMU), LiDAR, and visual sensors, the proposed approach effectively compensates for the limitations of individual sensors, enabling robust navigation in complex environments. Experimental results demonstrate that the algorithm achieves an average localization accuracy of 1.2 meters in urban areas and 1.5 meters in forested settings, showcasing its resilience against sensor noise and environmental challenges. The implementation of loop closure techniques further improves long-term navigation accuracy, making it suitable for prolonged missions. This research contributes to the growing body of knowledge in autonomous drone navigation and offers significant implications for enhancing the operational capabilities of drones in real-world scenarios. Future work will focus on integrating additional sensors, exploring machine learning techniques for adaptive fusion, and conducting extensive field trials to validate the system's performance in dynamic environments..
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Digital Technology has been a boon, especially in the field of education. Being one of the mot ef... more Digital Technology has been a boon, especially in the field of education. Being one of the mot efficient ways of supplementing the traditional teaching-learning process, it has rightfully become one of the most important facets in the classroom and its application has risen in both quantity and quality. Mathematics as a subject finds constant usage of Digital tools in its pedagogy as well as its application. This study attempts to find the prevalence of Digital tool usage in the mathematics classrooms of the Undergraduate levels. Using a survey questionnaire, the investigators collected data regarding the various aspects of Digital tool integration in a mathematics classroom, reaching the conclusion that despite students perceiving the use and integration of digital technology in their mathematics classroom as net beneficial to themselves, there is not much scope for its development within the classrooms.
Digital Technology has been a boon, especially in the field of education. Being one of the mot ef... more Digital Technology has been a boon, especially in the field of education. Being one of the mot efficient ways of supplementing the traditional teachinglearning process, it has rightfully become one of the most important facets in the classroom and its application has risen in both quantity and quality. Mathematics as a subject finds constant usage of Digital tools in its pedagogy as well as its application. This study attempts to find the prevalence of Digital tool usage in the mathematics classrooms of the Undergraduate levels. Using a survey questionnaire, the investigators collected data regarding the various aspects of Digital tool integration in a mathematics classroom, reaching the conclusion that despite students perceiving the use and integration of digital technology in their mathematics classroom as net beneficial to themselves, there is not much scope for its development within the classrooms.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This research work aims at evaluating the possibilities of using artificial intelligence in dynam... more This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (H... more This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management....
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remai... more Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. The method evaluates node trust using Dempster-Shafer Evidence Theoryto secure localization against rogue nodes and optimizes the localization process through trilateral and multilateration systems. The SABWP-NL approach demonstrates superior performance in terms of localized nodes and localization error compared to existing techniques like BWP, ROA, and AO. Results show that SABWP-NL achieves the highest number of localized nodes and the lowest localization error, making it a promising solution for efficient and secure node localization in WSNs.
International Journal of Innovative Research in Computer Science and Technology, 2024
The study of deep beams with openings in their webs and how to reinforce them is crucial for ensu... more The study of deep beams with openings in their webs and how to reinforce them is crucial for ensuring strong and effective structures. This collection of research examines various aspects of the topic, including the use of externally bonded composites and fiber-reinforced polymers (FRP). The studies carefully investigate the failure mechanisms of these beams, their response to loads and deflections, improvements in their shear capacity, and the effects of the size and placement of the openings. Collectively, these studies provide valuable insights into the reinforcement of deep beams, contributing to the overall knowledge in structural engineering.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The escalating complexity and sophistication of cyber threats necessitate a paradigm shift in ope... more The escalating complexity and sophistication of cyber threats necessitate a paradigm shift in operating system (OS) security. Traditional security measures, while effective against known vulnerabilities, struggle to adapt to dynamic attack vectors. This paper introduces Intelligent Defense, an innovative approach to OS security powered by Artificial Intelligence (AI). By integrating advanced machine learning algorithms and neural network architectures, Intelligent Defense transforms an operating system into an adaptive, self-learning security platform capable of identifying, predicting, and mitigating cyber threats in real-time. The study explores the architecture and functionality of an AI-powered OS, highlighting its ability to detect zero-day exploits, thwart malware intrusions, and provide a proactive response to emerging threats. Furthermore, the paper discusses the ethical considerations, computational challenges, and scalability of implementing AI in system-level security. Through case studies and simulations, we demonstrate the superior resilience and efficacy of Intelligent Defense compared to conventional OS security frameworks. This pioneering approach underscores the potential of AI to redefine the cybersecurity landscape, setting a new benchmark for OS security in the age of intelligent systems.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The significance of digital marketing has grown throughout time in the current digital era as a w... more The significance of digital marketing has grown throughout time in the current digital era as a way to provide clients with innovative properties for education, engagement, and the sale of goods and services. The purpose of the study is to evaluate research and development dynamics in the field of digital marketing (DM) from a multidisciplinary perspective by utilising bibliometric analysis to analyse the corpus of important research publications in the last twelve years (from 2012 to 2024). The Scopus database provided a total of 2403 published articles for the investigation. To provide more context, we evaluate patterns in the literature on digital marketing research in this study, considering the publication's year, author, keyword, and country. The findings showed that digital marketing research progressively grew during the study period. This bibliometric study generally provides the whole image of the field. It suggests that researchers focus on novel areas to add new findings and knowledge to the literature if they conduct digital marketing research.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
The rapid evolution of cyber threats has created unprecedented challenges, particularly in techno... more The rapid evolution of cyber threats has created unprecedented challenges, particularly in technologically advanced regions like California, where digital infrastructure supports critical industries and millions of residents. This study investigates the application of artificial intelligence (AI) in predicting and mitigating cybersecurity risks, focusing on its transformative role in safeguarding California's digital landscape. By leveraging AI-driven technologies such as machine learning algorithms, neural networks, and natural language processing (NLP), the research demonstrates how these tools identify and neutralize threats before they escalate into breaches. For example, supervised machine learning models are used to detect anomalies in network traffic, while NLP-based tools analyze phishing emails to prevent social engineering attacks. AI-powered solutions like predictive analytics and real-time threat intelligence platforms showcase their ability to enhance cybersecurity frameworks through faster detection, improved accuracy, and reduced response times. The study also examines AI's integration with security systems like firewalls and intrusion detection systems, which are now bolstered by adaptive learning capabilities. Our findings illustrate that AI not only mitigates immediate risks but also fortifies long-term resilience against emerging cyber threats, making it a cornerstone of future cybersecurity strategies.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
This study introduces an innovative methodology for the efficient conceptual design of complex, m... more This study introduces an innovative methodology for the efficient conceptual design of complex, multidisciplinary systems that involve computationally intensive analyses and a vast array of design variables. A novel nearly-orthogonal sampling strategy with superior space-filling characteristics is employed to extract maximal insights into system behaviour using a significantly reduced number of trial designs. The sampled dataset serves as input for a metamodel constructed using advanced artificial neural networks, augmented by Transformer Networks to enhance the metamodel’s capacity for capturing intricate dependencies and complex interactions within the data. Furthermore, a stage-wise interconnection of discrete neural networks is proposed for trajectory metamodeling, effectively mitigating the dimensionality challenges inherent in traditional neural architectures. The optimization process integrates a hybrid approach, leveraging a Genetic Algorithm for global optimization in tandem with Sequential Quadratic Programming for localized refinement utilizing exact disciplinary analyses. The efficacy of the proposed methodology is demonstrated through its application to the conceptual design optimization of a multistage solid-fuelled space launch vehicle. The results reveal exceptional accuracy in approximating highly nonlinear functions, a substantial reduction in overall computational time, and significant minimization of the reliance on exhaustive disciplinary analyses, underscoring the transformative potential of this approach.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
In the rapidly evolving digital landscape, cyberattacks are becoming increasingly sophisticated, ... more In the rapidly evolving digital landscape, cyberattacks are becoming increasingly sophisticated, posing significant threats to organizations and individuals worldwide. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, offering advanced capabilities to detect, prevent, and mitigate these attacks. This article explores the integration of AI into cybersecurity frameworks, emphasizing its role in identifying anomalies, predicting potential threats, and automating response mechanisms. By leveraging machine learning algorithms and data-driven insights, AI enhances the speed and accuracy of threat detection, effectively combating challenges posed by modern cyberattacks. However, the adoption of AI also introduces new complexities, such as adversarial AI and ethical considerations. This paper delves into the dual nature of AI in cybersecurity, providing a comprehensive overview of its benefits, challenges, and future potential in safeguarding digital ecosystems against ever-evolving cyber threats.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST) , 2025
Microservices architecture, described with characteristics of being distributed and loosely coupl... more Microservices architecture, described with characteristics of being distributed and loosely coupled, has become popular in recent times for software development. It offers flexibility, scalability, and a fault tolerance that accompanies a different set of security challenges. The introduction of microservices architecture shifted the application development pattern as well as deployment pattern because the monolithic systems were broken down into smaller, independent, and scalable services, but its nature of being distributed generated certain specific security issues. This research paper explores security vulnerabilities related to microservices, analyzes specific problems they raise, and seeks to know the methods and best practices for reducing these threats. Discussed subjects include authentication and authorization, secure communication, data protection, service segregation, monitoring, and incident response. This paper discusses the critical security threats arising with microservices applications and those that include increased attack surface, API security, data protection, and IAM. We discuss the root cause of these weaknesses and then present a feasible approach to combating them. Then we proceed further and involve discussions about security as code and DevSecOps practices and new technologies like blockchain and zero-trust architecture for protecting microservices environments. Organizations can enjoy the benefits of microservices and still keep their applications safe from any kind of threat by identifying these challenges and applying suitable security strategies.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Heart disease is a global health concern because of eating patterns, office work cultures, and li... more Heart disease is a global health concern because of eating patterns, office work cultures, and lifestyle changes. A machine learning-based heart attack prediction system is like having a vigilant watchdog in the medical field. To estimate the danger of a heart attack, it all boils down to analyzing data and complex algorithms. Four primary categories were established at the outset of this study: age, gender, BMI, and blood pressure. The data on heart illness was then classified using a variety of machine learning approaches, including XGBoost Model, Gradient Boosting Model, Random Forest, Logistic Regression, and Decision Trees. The results in terms of accuracy, false positive rate, precision, sensitivity, and specificity were then compared. Results in terms of accuracy, precision, recall, and f1_score were found to be greatest when using Logistic Regression (LR). It is therefore strongly recommended that data on cardiac disease can be classified using the logistic regression technique.
This paper explores the potential for optimizing the Internet of Things by integrating machine le... more This paper explores the potential for optimizing the Internet of Things by integrating machine learning and computer vision technologies and its implications for U.S. national security and economic competitiveness. First, the application of machine learning in IoT device optimization is introduced, emphasizing its ability to improve system intelligence and efficiency. Secondly, the critical role of computer vision technology in monitoring and reacting to changes in the physical environment is discussed, especially in security applications, such as the protection of national infrastructure and border security. Finally, the strategic significance of integrating these technologies in national security strategy and economic development is analyzed, and the direction and challenge of future research are put forward. .
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The main goal of CT-based triage is to shorten the time it takes to reach the expert opinion for ... more The main goal of CT-based triage is to shorten the time it takes to reach the expert opinion for patients in emergency situations, especially in cases of cranial fractures and intracranial hemorrhage. Increasing the performance in this regard may be possible with the use of artificial intelligence-supported software that may pre-scan the images and put them in order of urgency before the human triage officer is able to evaluate them. The project involves the development software that quickly classifies cases into one of two groups, urgent or non-urgent, by analysing the computerized tomography (CT) image of the brain taken without the administration of intravenous contrast material. In this way, more effective triage is aimed. The software developed for this purpose was observed to have high performances in two separate machine learning models. Additionally, a visual interface that allows viewing DICOM files was developed within the scope of the project.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The rapid advancement of digital health technologies and sensor innovations has transformed weara... more The rapid advancement of digital health technologies and sensor innovations has transformed wearable health monitoring systems, enabling unprecedented levels of personalized care, real-time health tracking, and early disease detection. This paper explores the pivotal role of these technologies in revolutionizing the healthcare landscape. We examine the integration of cutting-edge sensors, including biosensors, motion sensors, and environmental sensors, within wearable devices, which allow for continuous monitoring of physiological parameters such as heart rate, blood pressure, glucose levels, and physical activity. The paper also highlights the growing impact of artificial intelligence (AI) and machine learning (ML) in enhancing the accuracy, predictive capabilities, and decision-making processes of these systems. Furthermore, we discuss the challenges of data privacy, system interoperability, and the need for robust regulatory frameworks to ensure the safe and effective implementation of wearable health devices. In conclusion, we propose that the continued evolution of digital health technologies and sensors will play a crucial role in the future of preventive healthcare, offering new opportunities for improving health outcomes and reducing the burden on traditional healthcare infrastructures.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
India is having best weather system to apply the solar power system to individual home for people... more India is having best weather system to apply the solar power system to individual home for people’s regular day to day working devices. Sunlight The local weather has a significant impact on photovoltaic systems, with dust being the most significant factor. Dust accumulation on the surface of photovoltaic (PV) panels prevents sunlight from reaching the cells, which ultimately lowers the system's power output. Depending on the location, PV integration strategy, and size of the PV power plant, regular cleaning is necessary to prevent dust-based power losses. This publication examines the effects of dust buildup on solar systems, including radiation loss and output power operation, and establishes the ideal cleaning interval. In the current sector recharging and battery maintenance is one of the most difficult in our day to day life. This paper explains the novel technology help to analyse the battery level. The systems find the level of battery as well as automatically recharge the device using solar power system. So for this devices can recharge automatically and stop recharging after completing the battery level is full. Communication is one of the most preferable and should not avoidable. This method help to recharge the mobile devices without socketing and plugin device for our regular power supply. The proposed ARTSPS helps to easy recharging is possible while we are going to apply this novel revolution. ARTSRS Solar panels are designed to work in all weather conditions. The only factor that can affect a solar installation is snow accumulation, which can reduce production due to shading.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Medical report generation demands accurate abnormality detection and precise description generati... more Medical report generation demands accurate abnormality detection and precise description generation from CT images. While large language models have shown promising results in natural language processing tasks, their application in medical imaging analysis faces challenges due to the complexity of fine-grained feature detection and the requirement for domain-specific knowledge. This paper presents a novel framework integrating large language models with specialized medical image processing techniques for fine-grained abnormality detection and natural language description generation. Our approach incorporates a multi-modal knowledge enhancement module and a hierarchical attention mechanism to bridge the gap between visual understanding and textual description. The framework employs an adapter-based architecture for efficient domain adaptation and introduces a medical knowledge-enhanced loss function to improve description accuracy. Experimental results on three public datasets demonstrate the effectiveness of our approach, achieving 94.6% detection accuracy and a BLEU-4 score of 0.421 for description generation, surpassing current state-of-the-art methods. The system shows particular strength in handling subtle abnormalities, with a 91.2% average precision in fine-grained detection tasks. Comprehensive ablation studies validate the contribution of each component, while qualitative analysis demonstrates the clinical relevance of generated descriptions. The proposed framework represents a significant advancement in automated medical image analysis, offering potential benefits for clinical workflow optimization and diagnostic support.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This research aims at examining the strategies of performance tuning in cloud computing with emph... more This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments....
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
The rapid evolution of drone technology has expanded its applications across variodus domains, in... more The rapid evolution of drone technology has expanded its applications across variodus domains, including delivery services, environmental monitoring, and search and rescue operations. However, many of these applications face significant challenges in GPS-denied environments, such as dense urban areas and heavily forested regions, where traditional navigation methods falter. This paper presents a novel multi-sensor fusion algorithm designed to enhance the localization accuracy of autonomous drones without reliance on GPS. By integrating data from an Inertial Measurement Unit (IMU), LiDAR, and visual sensors, the proposed approach effectively compensates for the limitations of individual sensors, enabling robust navigation in complex environments. Experimental results demonstrate that the algorithm achieves an average localization accuracy of 1.2 meters in urban areas and 1.5 meters in forested settings, showcasing its resilience against sensor noise and environmental challenges. The implementation of loop closure techniques further improves long-term navigation accuracy, making it suitable for prolonged missions. This research contributes to the growing body of knowledge in autonomous drone navigation and offers significant implications for enhancing the operational capabilities of drones in real-world scenarios. Future work will focus on integrating additional sensors, exploring machine learning techniques for adaptive fusion, and conducting extensive field trials to validate the system's performance in dynamic environments..
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Digital Technology has been a boon, especially in the field of education. Being one of the mot ef... more Digital Technology has been a boon, especially in the field of education. Being one of the mot efficient ways of supplementing the traditional teaching-learning process, it has rightfully become one of the most important facets in the classroom and its application has risen in both quantity and quality. Mathematics as a subject finds constant usage of Digital tools in its pedagogy as well as its application. This study attempts to find the prevalence of Digital tool usage in the mathematics classrooms of the Undergraduate levels. Using a survey questionnaire, the investigators collected data regarding the various aspects of Digital tool integration in a mathematics classroom, reaching the conclusion that despite students perceiving the use and integration of digital technology in their mathematics classroom as net beneficial to themselves, there is not much scope for its development within the classrooms.
Digital Technology has been a boon, especially in the field of education. Being one of the mot ef... more Digital Technology has been a boon, especially in the field of education. Being one of the mot efficient ways of supplementing the traditional teachinglearning process, it has rightfully become one of the most important facets in the classroom and its application has risen in both quantity and quality. Mathematics as a subject finds constant usage of Digital tools in its pedagogy as well as its application. This study attempts to find the prevalence of Digital tool usage in the mathematics classrooms of the Undergraduate levels. Using a survey questionnaire, the investigators collected data regarding the various aspects of Digital tool integration in a mathematics classroom, reaching the conclusion that despite students perceiving the use and integration of digital technology in their mathematics classroom as net beneficial to themselves, there is not much scope for its development within the classrooms.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This research work aims at evaluating the possibilities of using artificial intelligence in dynam... more This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (H... more This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management....
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remai... more Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. The method evaluates node trust using Dempster-Shafer Evidence Theoryto secure localization against rogue nodes and optimizes the localization process through trilateral and multilateration systems. The SABWP-NL approach demonstrates superior performance in terms of localized nodes and localization error compared to existing techniques like BWP, ROA, and AO. Results show that SABWP-NL achieves the highest number of localized nodes and the lowest localization error, making it a promising solution for efficient and secure node localization in WSNs.
International Journal of Innovative Research in Computer Science and Technology, 2024
The study of deep beams with openings in their webs and how to reinforce them is crucial for ensu... more The study of deep beams with openings in their webs and how to reinforce them is crucial for ensuring strong and effective structures. This collection of research examines various aspects of the topic, including the use of externally bonded composites and fiber-reinforced polymers (FRP). The studies carefully investigate the failure mechanisms of these beams, their response to loads and deflections, improvements in their shear capacity, and the effects of the size and placement of the openings. Collectively, these studies provide valuable insights into the reinforcement of deep beams, contributing to the overall knowledge in structural engineering.
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