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, 2024
Accurate localization is crucial for numerous applications, spanning from navigation systems to i... more Accurate localization is crucial for numerous applications, spanning from navigation systems to indoor positioning and asset tracking. However, achieving precise localization remains challenging, especially in environments where traditional positioning technologies face limitations. To address this challenge, this paper proposes a novel approach: the fusion of multiple positioning technologies. By integrating data from GPS, Wi-Fi, Bluetooth, RFID, and other sensors, our framework aims to enhance localization accuracy, robustness, and adaptability across diverse environments. We present a comprehensive fusion algorithm that combines geometric, probabilistic, and machine learning techniques, while incorporating context-awareness mechanisms for adaptive localization. Through simulations and real-world experiments, we demonstrate the effectiveness of our fusion framework in improving localization accuracy and resilience to environmental factors. This research contributes to advancing the state-of-the-art in localization technologies and opens avenues for innovative applications in transportation, healthcare, retail, and beyond.
International Journal of Innovative Research in Computer Science and Technology, 2024
The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabl... more The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabling a new era of connectivity, data collection, and automation across various domains. IoT-based sensor networks comprise interconnected sensors and actuators that collect, transmit, and process data to provide valuable insights and enable intelligent decision-making. This paper explores the architecture, applications, and challenges of IoT-based sensor networks. The architecture section delves into the components, layers, and communication protocols that constitute these networks, highlighting the roles and interactions of sensors, microcontrollers, gateways, and cloud services. The applications section showcases the diverse use cases of IoT-based sensor networks in smart cities, industrial automation, healthcare, agriculture, and environmental monitoring, illustrating their transformative impact. The challenges section identifies the key issues such as scalability, interoperability, security, reliability, energy efficiency, and data management that need to be addressed to realize the full potential of IoT networks. Finally, the paper discusses future directions, emphasizing the potential of edge computing, 5G, artificial intelligence, and blockchain technology to advance IoT-based sensor networks and unlock new opportunities. Through continued research, innovation, and collaboration, IoT sensor networks are poised to drive significant advancements in technology and society, creating a more connected and intelligent world.
International Journal of Innovative Research in Computer Science and Technology, 2024
In recent times, there has been a swift advancement in the creation of smart cities. A smart city... more In recent times, there has been a swift advancement in the creation of smart cities. A smart city is a metropolitan area that makes use of cutting-edge technology for effective resource management and monitoring. The goal is to maximize energy and cost savings while simultaneously enhancing the quality of life for its citizens. With an emphasis on energy-related monitoring, a smart energy meter is the fundamental element that forms the basis for all data gathering, analysis, and automation. This paper describes how to turn a regular energy meter into a smart energy meter by adding an Arduino and a GSM module. With the help of the Internet of Things, this smart energy meter is further improved using IoT. An embedded controller (Arduino) and a GSM modem will be integrated by the smart energy meter system to transfer data over a GSM mobile network, such as generated invoices, spent energy in kWh, etc. IoT can be used to enable energy providers and power companies to monitor and control their customers' services without the need for human labour by providing them with the user -generated data. By connecting the smart meter to the Internet of Things, the user can access the data using a password and ID that has been issued from any location in the globe. Major energy companies will benefit from our project's user-friendliness, reduced labour costs, and decreased error and loss rates.
International Journal of Innovative Research in Computer Science and Technology, 2024
To address the inefficiencies and inaccuracies in analyzing large-scale medical diagnostic datase... more To address the inefficiencies and inaccuracies in analyzing large-scale medical diagnostic datasets, this paper introduces a deep learning-based method for processing auxiliary medical diagnostic data. The proposed approach involves preprocessing the medical diagnostic data through normalization and principal component analysis to extract relevant features. Subsequently, a neural network utilizing a multilayer perceptron is employed to analyze the preprocessed data, facilitating diagnostic classification. It also provides intelligent support for medical professionals. The method was implemented and tested using the Python programming environment. Results indicate that the proposed approach achieves better performance than other comparative methods and demonstrates significant practical application potential.
International Journal of Innovative Research in Computer Science and Technology, 2024
Emotional health plays a crucial role in the holistic development of school children, impacting t... more Emotional health plays a crucial role in the holistic development of school children, impacting their academic performance, social interactions, and overall well-being. This research article explores the potential of integrating Monistic Theory, Neuro-Linguistic Programming (NLP), and Artificial Intelligence (AI) to foster emotional health among school children, both globally and within the Indian context. Drawing upon existing literature and empirical evidence, this paper highlights the theoretical underpinnings of Monistic Theory, the principles of NLP, and the capabilities of AI in supporting emotional development. Furthermore, it discusses specific strategies and interventions that can be implemented in school settings to promote emotional well-being among students.
International Journal of Innovative Research in Computer Science and Technology, 2024
Image-based defogging technology can significantly enhance intraoperative image quality and shows... more Image-based defogging technology can significantly enhance intraoperative image quality and shows great promise in various medical fields. A new image removal algorithm based on conditional generative adversarial networks (cGAN) has been developed. This algorithm employs the Tiramisu model instead of the conventional U-Net, thereby improving its computational accuracy. Additionally, the quality of the resulting images is enhanced by incorporating more textual data. A novel visual perception method is proposed, utilizing a contrast-based approach to improve the similarity between images with the same semantic content. Experiments demonstrate that this method not only excels at fog removal but also better preserves the key visual features of the images. Compared to existing image defogging technologies, this method offers superior qualitative analysis capabilities. This advancement can aid doctors in better visualizing intraoperative images. The effectiveness and robustness of the proposed method are validated through comparative analysis with several existing image noise reduction techniques.
International Journal of Innovative Research in Computer Science and Technology, 2024
This research investigates the multifaceted impact of international personnel on local communitie... more This research investigates the multifaceted impact of international personnel on local communities in Greater Noida, India, exploring socio-economic, cultural, and integrative dynamics through empirical data collected from local and international residents. The study reveals that international personnel contribute significantly to local economies through job creation and business investments. Additionally, they bring about cultural exchanges that, while enhancing cultural understanding, also pose challenges in integration and affect local traditions. The findings indicate that while the presence of international individuals fosters economic growth and multicultural interactions, it requires balanced strategies to ensure harmonious community integration and mutual cultural respect. This work underscores the importance of tailored community engagement and policy measures to enhance the benefits and mitigate the challenges of international presence in local settings.
International Journal of Innovative Research in Computer Science and Technology, 2024
This study focuses on enhancing power quality using a Current Source Converter (CSC) based Dynami... more This study focuses on enhancing power quality using a Current Source Converter (CSC) based Dynamic Voltage Restorer (D-STATCOM) controlled by a Fuzzy Logic-PID (Fuzzy-PID) controller. Power quality improvement is a vital aspect of maintaining reliable and efficient electrical power systems. The integration of fuzzy logic with traditional PID control enables adaptive and precise regulation of the D-STATCOM, addressing voltage sags, swells, and harmonic distortions effectively. The Fuzzy-PID controller dynamically adjusts the control parameters, offering superior performance in compensating for power quality disturbances compared to conventional methods. Simulation and experimental results demonstrate that the Fuzzy-PID controlled CSC-based D-STATCOM significantly improves voltage stability, reduces harmonic distortion, and enhances overall power quality. This approach is particularly effective in managing the nonlinear and time-varying nature of electrical loads, making it highly suitable for industrial power systems, renewable energy integration, and smart grid applications. The proposed system ensures robust and reliable power quality improvement, presenting a promising solution for modern electrical infrastructure challenges.
International Journal of Innovative Research in Computer Science and Technology, 2024
The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and ... more The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and Bidirectional Long Short-Term (BLSTM) to create a robust model for temporal expression recognition in clinical texts. The CNN component effectively captures morphological and orthographic features at the character level, which enriches the semantic understanding of complex medical terminologies that are often abbreviated or have unique suffixes and prefixes. The BLSTM component excels in capturing long-range dependencies in text, which is crucial for understanding the context in which temporal expressions occur. By integrating these models with a CRF layer, the system not only predicts discrete labels but also ensures that the sequence of predicted labels is coherent and contextually appropriate, addressing the limitations of models that predict labels independently. The integration of pre-trained biomedical word vectors provides significant contextual grounding tailored to the medical domain, enhancing the model's ability to discern and interpret the nuances of medical language. This is crucial in clinical contexts where accurate interpretation of temporal phrases can be critical for patient management and treatment timelines. Further, experiments conducted on the dataset validate the effectiveness of the proposed model, demonstrating a notable improvement over traditional methods that rely heavily on hand-crafted features and rule-based approaches. Future work could explore the adaptability of this model to other subdomains of the medical field and its efficacy in processing multilingual texts, potentially increasing its applicability in global healthcare settings, with further refinement of the neural architecture and optimization of training strategies potentially yielding even better performance and faster processing times essential for real-time clinical decision support systems.
International Journal of Innovative Research in Computer Science and Technology, 2024
Practical subjects have grown in importance in student lives over time. Certain components may ma... more Practical subjects have grown in importance in student lives over time. Certain components may malfunction or not be suitable for the experiment being conducted. The RFID system facilitates the tracking of both student attendance and experiment details, which are saved on a MySQL server and shown inside a PHP environment. After being collected by means of an RFID reader, RFID tags, and a nodeMCU, the data has been applied to artificial understanding that identifies usage and then notifies the service. Based on mean square error (MSE) value, we have constructed three models: gradient boosting (1.00), random forest (0.5), and linear regression (0.14).
International Journal of Innovative Research in Computer Science and Technology, 2024
Knowledge distillation is a model compression technique that enhances the performance and efficie... more Knowledge distillation is a model compression technique that enhances the performance and efficiency of a smaller model (student model) by transferring knowledge from a larger model (teacher model). This technique utilizes the outputs of the teacher model, such as soft labels, intermediate features, or attention weights, as additional supervisory signals to guide the learning process of the student model. By doing so, knowledge distillation reduces computational resources and storage space requirements while maintaining or surpassing the accuracy of the teacher model. Research on knowledge distillation has evolved significantly since its inception in the 1980s, especially with the introduction of soft labels by Hinton and colleagues in 2015. Various advancements have been made, including methods to extract richer knowledge, knowledge sharing among models, integration with other compression techniques, and application in diverse domains like natural language processing and reinforcement learning. This article provides a comprehensive review of knowledge distillation, covering its concepts, methods, applications, challenges, and future directions.
International Journal of Innovative Research in Computer Science and Technology, 2024
Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their dete... more Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their detection and classification essential. The prevalent use of transfer learning in recognition algorithms often encounters a significant disparity between source and target datasets, which hampers effective feature extraction from pulmonary nodules and degrades performance. An enhanced neural network model leveraging convolutional neural networks is introduced to address this issue. This model integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer, improving the network’s ability to extract features. To ascertain the optimal configuration, the models were evaluated based on accuracy in various combinations. The experiments conducted on the LUNA16 pulmonary nodule dataset revealed that the refined network model achieved an accuracy of 88.78% and a sensitivity of 87.18%. This represents an increase of 2.7 and 2.22 percentage points in accuracy and sensitivity, respectively, compared to the GoogLeNet Inception V3 algorithm. Further tests across different dataset proportions also yielded superior outcomes, demonstrating enhanced generalization capabilities. These findings can offer objective benchmarks for clinical diagnosis.
International Journal of Innovative Research in Computer Science and Technology, 2024
Every city in the world suffers from traffic congestion, which greatly disturbs the citizens. Thi... more Every city in the world suffers from traffic congestion, which greatly disturbs the citizens. This problem is made worse by signal timing delays because modern traffic lights are not traffic condition-adaptive. An increasing number of people are in need of effective automatic traffic control technologies to address this. In this study, a density-based dynamic traffic signal control scheme is proposed. The system uses infrared sensors fixed on road poles and a Node MCU micro-controller to automatically modify signal timing according to traffic density at intersections. It is essential to go from fixed-time signaling to automated decision-making. Current fixed-timing systems become ineffective when imbalances in traffic flow arise. This study proposes a solution to a common urban problem by introducing an adaptive traffic light system to reduce congestion.
International Journal of Innovative Research in Computer Science and Technology, 2024
Cloud computing offers storage, infrastructure, computing, networking, databases, platform, softw... more Cloud computing offers storage, infrastructure, computing, networking, databases, platform, software, and analytics services over the Internet. It provides numerous benefits including scalability, cost management, broad access to resources, elasticity, resource pooling. Though cloud computing is mature and widely adopted computing model in software as well as non-software industries, it has several issues regarding security as it provides most of the cloud services over the public infrastructure. Denial of service (DOS), malware injection, insecure APIs, data loss, data breaches, hypervisor vulnerabilities, VM escape are a few major issues in cloud computing. In this paper, authors tried to provide a comprehensive analysis of critical security issues in cloud computing. Furthermore, this paper critically analyses the existing solutions to the various security issues in the cloud computing model.
International Journal of Innovative Research in Computer Science and Technology, 2024
In an effort to conserve energy and optimize the use of resources, this research explores the app... more In an effort to conserve energy and optimize the use of resources, this research explores the application of fuzzy logic control techniques to improve energy efficiency in intelligent lighting and air conditioning (AC) management systems. This research aims to investigate how fuzzy logic control strategies can be incorporated into intelligent systems to regulate lighting and air conditioning operations with greater precision, adaptability, and energy efficiency. By utilizing a fuzzy logic algorithm, this research develops a model that is able to dynamically adjust lighting levels and AC settings based on environmental conditions, housing patterns, and user preferences. Fuzzy logic control has proven useful for maintaining the desired level of comfort and optimizing electrical energy consumption through trials. The research results show that the integration of fuzzy logic control methodology offers significant potential to improve energy efficiency in lighting and air conditioning management systems, leading to reduced energy consumption and operational costs. These findings highlight the importance of intelligent control in the sustainable management of electrical equipment and provide valuable insights for the design and implementation of energy-efficient systems in a variety of contexts.
International Journal of Innovative Research in Computer Science and Technology, 2024
YOLOv5 represents a significant advancement in the field of real-time object detection, building ... more YOLOv5 represents a significant advancement in the field of real-time object detection, building upon the YOLO (You Only Look Once) series' legacy. This paper provides a comprehensive review of YOLOv5, examining its architecture, innovations, performance benchmarks, and applications. We also compare YOLOv5 with previous YOLO versions and other state-of-the-art object detection models, highlighting its strengths and limitations. Through this review, we aim to offer insights into the evolution of YOLOv5 and its impact on the field of computer vision.
International Journal of Innovative Research in Computer Science and Technology, 2024
Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. Thi... more Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.
International Journal of Innovative Research in Computer Science and Technology, 2024
No matter how we look at it, there are no chances to live without the flora surrounding us. One c... more No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process.
The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.
International Journal of Innovative Research in Computer Science and Technology, 2024
The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transport... more The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transportation, particularly in the context of India's burgeoning population and rapid urbanization. Despite significant potential, the widespread adoption of EVs in India is hampered by various barriers, including infrastructure limitations, policy gaps, high upfront costs, and consumer perceptions. This research paper aims to analyze these key barriers and propose strategies to accelerate the market penetration of EVs in India. Through a comprehensive literature review, policy analysis, and examination of case studies, this paper identifies the challenges hindering EV adoption and offers recommendations for addressing them. Proposed strategies include infrastructure development, policy interventions, consumer awareness initiatives, and technological innovations. By implementing these recommendations, India can overcome barriers to EV adoption and realize the environmental and economic benefits associated with clean mobility.
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, 2024
Accurate localization is crucial for numerous applications, spanning from navigation systems to i... more Accurate localization is crucial for numerous applications, spanning from navigation systems to indoor positioning and asset tracking. However, achieving precise localization remains challenging, especially in environments where traditional positioning technologies face limitations. To address this challenge, this paper proposes a novel approach: the fusion of multiple positioning technologies. By integrating data from GPS, Wi-Fi, Bluetooth, RFID, and other sensors, our framework aims to enhance localization accuracy, robustness, and adaptability across diverse environments. We present a comprehensive fusion algorithm that combines geometric, probabilistic, and machine learning techniques, while incorporating context-awareness mechanisms for adaptive localization. Through simulations and real-world experiments, we demonstrate the effectiveness of our fusion framework in improving localization accuracy and resilience to environmental factors. This research contributes to advancing the state-of-the-art in localization technologies and opens avenues for innovative applications in transportation, healthcare, retail, and beyond.
International Journal of Innovative Research in Computer Science and Technology, 2024
The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabl... more The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabling a new era of connectivity, data collection, and automation across various domains. IoT-based sensor networks comprise interconnected sensors and actuators that collect, transmit, and process data to provide valuable insights and enable intelligent decision-making. This paper explores the architecture, applications, and challenges of IoT-based sensor networks. The architecture section delves into the components, layers, and communication protocols that constitute these networks, highlighting the roles and interactions of sensors, microcontrollers, gateways, and cloud services. The applications section showcases the diverse use cases of IoT-based sensor networks in smart cities, industrial automation, healthcare, agriculture, and environmental monitoring, illustrating their transformative impact. The challenges section identifies the key issues such as scalability, interoperability, security, reliability, energy efficiency, and data management that need to be addressed to realize the full potential of IoT networks. Finally, the paper discusses future directions, emphasizing the potential of edge computing, 5G, artificial intelligence, and blockchain technology to advance IoT-based sensor networks and unlock new opportunities. Through continued research, innovation, and collaboration, IoT sensor networks are poised to drive significant advancements in technology and society, creating a more connected and intelligent world.
International Journal of Innovative Research in Computer Science and Technology, 2024
In recent times, there has been a swift advancement in the creation of smart cities. A smart city... more In recent times, there has been a swift advancement in the creation of smart cities. A smart city is a metropolitan area that makes use of cutting-edge technology for effective resource management and monitoring. The goal is to maximize energy and cost savings while simultaneously enhancing the quality of life for its citizens. With an emphasis on energy-related monitoring, a smart energy meter is the fundamental element that forms the basis for all data gathering, analysis, and automation. This paper describes how to turn a regular energy meter into a smart energy meter by adding an Arduino and a GSM module. With the help of the Internet of Things, this smart energy meter is further improved using IoT. An embedded controller (Arduino) and a GSM modem will be integrated by the smart energy meter system to transfer data over a GSM mobile network, such as generated invoices, spent energy in kWh, etc. IoT can be used to enable energy providers and power companies to monitor and control their customers' services without the need for human labour by providing them with the user -generated data. By connecting the smart meter to the Internet of Things, the user can access the data using a password and ID that has been issued from any location in the globe. Major energy companies will benefit from our project's user-friendliness, reduced labour costs, and decreased error and loss rates.
International Journal of Innovative Research in Computer Science and Technology, 2024
To address the inefficiencies and inaccuracies in analyzing large-scale medical diagnostic datase... more To address the inefficiencies and inaccuracies in analyzing large-scale medical diagnostic datasets, this paper introduces a deep learning-based method for processing auxiliary medical diagnostic data. The proposed approach involves preprocessing the medical diagnostic data through normalization and principal component analysis to extract relevant features. Subsequently, a neural network utilizing a multilayer perceptron is employed to analyze the preprocessed data, facilitating diagnostic classification. It also provides intelligent support for medical professionals. The method was implemented and tested using the Python programming environment. Results indicate that the proposed approach achieves better performance than other comparative methods and demonstrates significant practical application potential.
International Journal of Innovative Research in Computer Science and Technology, 2024
Emotional health plays a crucial role in the holistic development of school children, impacting t... more Emotional health plays a crucial role in the holistic development of school children, impacting their academic performance, social interactions, and overall well-being. This research article explores the potential of integrating Monistic Theory, Neuro-Linguistic Programming (NLP), and Artificial Intelligence (AI) to foster emotional health among school children, both globally and within the Indian context. Drawing upon existing literature and empirical evidence, this paper highlights the theoretical underpinnings of Monistic Theory, the principles of NLP, and the capabilities of AI in supporting emotional development. Furthermore, it discusses specific strategies and interventions that can be implemented in school settings to promote emotional well-being among students.
International Journal of Innovative Research in Computer Science and Technology, 2024
Image-based defogging technology can significantly enhance intraoperative image quality and shows... more Image-based defogging technology can significantly enhance intraoperative image quality and shows great promise in various medical fields. A new image removal algorithm based on conditional generative adversarial networks (cGAN) has been developed. This algorithm employs the Tiramisu model instead of the conventional U-Net, thereby improving its computational accuracy. Additionally, the quality of the resulting images is enhanced by incorporating more textual data. A novel visual perception method is proposed, utilizing a contrast-based approach to improve the similarity between images with the same semantic content. Experiments demonstrate that this method not only excels at fog removal but also better preserves the key visual features of the images. Compared to existing image defogging technologies, this method offers superior qualitative analysis capabilities. This advancement can aid doctors in better visualizing intraoperative images. The effectiveness and robustness of the proposed method are validated through comparative analysis with several existing image noise reduction techniques.
International Journal of Innovative Research in Computer Science and Technology, 2024
This research investigates the multifaceted impact of international personnel on local communitie... more This research investigates the multifaceted impact of international personnel on local communities in Greater Noida, India, exploring socio-economic, cultural, and integrative dynamics through empirical data collected from local and international residents. The study reveals that international personnel contribute significantly to local economies through job creation and business investments. Additionally, they bring about cultural exchanges that, while enhancing cultural understanding, also pose challenges in integration and affect local traditions. The findings indicate that while the presence of international individuals fosters economic growth and multicultural interactions, it requires balanced strategies to ensure harmonious community integration and mutual cultural respect. This work underscores the importance of tailored community engagement and policy measures to enhance the benefits and mitigate the challenges of international presence in local settings.
International Journal of Innovative Research in Computer Science and Technology, 2024
This study focuses on enhancing power quality using a Current Source Converter (CSC) based Dynami... more This study focuses on enhancing power quality using a Current Source Converter (CSC) based Dynamic Voltage Restorer (D-STATCOM) controlled by a Fuzzy Logic-PID (Fuzzy-PID) controller. Power quality improvement is a vital aspect of maintaining reliable and efficient electrical power systems. The integration of fuzzy logic with traditional PID control enables adaptive and precise regulation of the D-STATCOM, addressing voltage sags, swells, and harmonic distortions effectively. The Fuzzy-PID controller dynamically adjusts the control parameters, offering superior performance in compensating for power quality disturbances compared to conventional methods. Simulation and experimental results demonstrate that the Fuzzy-PID controlled CSC-based D-STATCOM significantly improves voltage stability, reduces harmonic distortion, and enhances overall power quality. This approach is particularly effective in managing the nonlinear and time-varying nature of electrical loads, making it highly suitable for industrial power systems, renewable energy integration, and smart grid applications. The proposed system ensures robust and reliable power quality improvement, presenting a promising solution for modern electrical infrastructure challenges.
International Journal of Innovative Research in Computer Science and Technology, 2024
The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and ... more The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and Bidirectional Long Short-Term (BLSTM) to create a robust model for temporal expression recognition in clinical texts. The CNN component effectively captures morphological and orthographic features at the character level, which enriches the semantic understanding of complex medical terminologies that are often abbreviated or have unique suffixes and prefixes. The BLSTM component excels in capturing long-range dependencies in text, which is crucial for understanding the context in which temporal expressions occur. By integrating these models with a CRF layer, the system not only predicts discrete labels but also ensures that the sequence of predicted labels is coherent and contextually appropriate, addressing the limitations of models that predict labels independently. The integration of pre-trained biomedical word vectors provides significant contextual grounding tailored to the medical domain, enhancing the model's ability to discern and interpret the nuances of medical language. This is crucial in clinical contexts where accurate interpretation of temporal phrases can be critical for patient management and treatment timelines. Further, experiments conducted on the dataset validate the effectiveness of the proposed model, demonstrating a notable improvement over traditional methods that rely heavily on hand-crafted features and rule-based approaches. Future work could explore the adaptability of this model to other subdomains of the medical field and its efficacy in processing multilingual texts, potentially increasing its applicability in global healthcare settings, with further refinement of the neural architecture and optimization of training strategies potentially yielding even better performance and faster processing times essential for real-time clinical decision support systems.
International Journal of Innovative Research in Computer Science and Technology, 2024
Practical subjects have grown in importance in student lives over time. Certain components may ma... more Practical subjects have grown in importance in student lives over time. Certain components may malfunction or not be suitable for the experiment being conducted. The RFID system facilitates the tracking of both student attendance and experiment details, which are saved on a MySQL server and shown inside a PHP environment. After being collected by means of an RFID reader, RFID tags, and a nodeMCU, the data has been applied to artificial understanding that identifies usage and then notifies the service. Based on mean square error (MSE) value, we have constructed three models: gradient boosting (1.00), random forest (0.5), and linear regression (0.14).
International Journal of Innovative Research in Computer Science and Technology, 2024
Knowledge distillation is a model compression technique that enhances the performance and efficie... more Knowledge distillation is a model compression technique that enhances the performance and efficiency of a smaller model (student model) by transferring knowledge from a larger model (teacher model). This technique utilizes the outputs of the teacher model, such as soft labels, intermediate features, or attention weights, as additional supervisory signals to guide the learning process of the student model. By doing so, knowledge distillation reduces computational resources and storage space requirements while maintaining or surpassing the accuracy of the teacher model. Research on knowledge distillation has evolved significantly since its inception in the 1980s, especially with the introduction of soft labels by Hinton and colleagues in 2015. Various advancements have been made, including methods to extract richer knowledge, knowledge sharing among models, integration with other compression techniques, and application in diverse domains like natural language processing and reinforcement learning. This article provides a comprehensive review of knowledge distillation, covering its concepts, methods, applications, challenges, and future directions.
International Journal of Innovative Research in Computer Science and Technology, 2024
Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their dete... more Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their detection and classification essential. The prevalent use of transfer learning in recognition algorithms often encounters a significant disparity between source and target datasets, which hampers effective feature extraction from pulmonary nodules and degrades performance. An enhanced neural network model leveraging convolutional neural networks is introduced to address this issue. This model integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer, improving the network’s ability to extract features. To ascertain the optimal configuration, the models were evaluated based on accuracy in various combinations. The experiments conducted on the LUNA16 pulmonary nodule dataset revealed that the refined network model achieved an accuracy of 88.78% and a sensitivity of 87.18%. This represents an increase of 2.7 and 2.22 percentage points in accuracy and sensitivity, respectively, compared to the GoogLeNet Inception V3 algorithm. Further tests across different dataset proportions also yielded superior outcomes, demonstrating enhanced generalization capabilities. These findings can offer objective benchmarks for clinical diagnosis.
International Journal of Innovative Research in Computer Science and Technology, 2024
Every city in the world suffers from traffic congestion, which greatly disturbs the citizens. Thi... more Every city in the world suffers from traffic congestion, which greatly disturbs the citizens. This problem is made worse by signal timing delays because modern traffic lights are not traffic condition-adaptive. An increasing number of people are in need of effective automatic traffic control technologies to address this. In this study, a density-based dynamic traffic signal control scheme is proposed. The system uses infrared sensors fixed on road poles and a Node MCU micro-controller to automatically modify signal timing according to traffic density at intersections. It is essential to go from fixed-time signaling to automated decision-making. Current fixed-timing systems become ineffective when imbalances in traffic flow arise. This study proposes a solution to a common urban problem by introducing an adaptive traffic light system to reduce congestion.
International Journal of Innovative Research in Computer Science and Technology, 2024
Cloud computing offers storage, infrastructure, computing, networking, databases, platform, softw... more Cloud computing offers storage, infrastructure, computing, networking, databases, platform, software, and analytics services over the Internet. It provides numerous benefits including scalability, cost management, broad access to resources, elasticity, resource pooling. Though cloud computing is mature and widely adopted computing model in software as well as non-software industries, it has several issues regarding security as it provides most of the cloud services over the public infrastructure. Denial of service (DOS), malware injection, insecure APIs, data loss, data breaches, hypervisor vulnerabilities, VM escape are a few major issues in cloud computing. In this paper, authors tried to provide a comprehensive analysis of critical security issues in cloud computing. Furthermore, this paper critically analyses the existing solutions to the various security issues in the cloud computing model.
International Journal of Innovative Research in Computer Science and Technology, 2024
In an effort to conserve energy and optimize the use of resources, this research explores the app... more In an effort to conserve energy and optimize the use of resources, this research explores the application of fuzzy logic control techniques to improve energy efficiency in intelligent lighting and air conditioning (AC) management systems. This research aims to investigate how fuzzy logic control strategies can be incorporated into intelligent systems to regulate lighting and air conditioning operations with greater precision, adaptability, and energy efficiency. By utilizing a fuzzy logic algorithm, this research develops a model that is able to dynamically adjust lighting levels and AC settings based on environmental conditions, housing patterns, and user preferences. Fuzzy logic control has proven useful for maintaining the desired level of comfort and optimizing electrical energy consumption through trials. The research results show that the integration of fuzzy logic control methodology offers significant potential to improve energy efficiency in lighting and air conditioning management systems, leading to reduced energy consumption and operational costs. These findings highlight the importance of intelligent control in the sustainable management of electrical equipment and provide valuable insights for the design and implementation of energy-efficient systems in a variety of contexts.
International Journal of Innovative Research in Computer Science and Technology, 2024
YOLOv5 represents a significant advancement in the field of real-time object detection, building ... more YOLOv5 represents a significant advancement in the field of real-time object detection, building upon the YOLO (You Only Look Once) series' legacy. This paper provides a comprehensive review of YOLOv5, examining its architecture, innovations, performance benchmarks, and applications. We also compare YOLOv5 with previous YOLO versions and other state-of-the-art object detection models, highlighting its strengths and limitations. Through this review, we aim to offer insights into the evolution of YOLOv5 and its impact on the field of computer vision.
International Journal of Innovative Research in Computer Science and Technology, 2024
Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. Thi... more Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.
International Journal of Innovative Research in Computer Science and Technology, 2024
No matter how we look at it, there are no chances to live without the flora surrounding us. One c... more No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process.
The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.
International Journal of Innovative Research in Computer Science and Technology, 2024
The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transport... more The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transportation, particularly in the context of India's burgeoning population and rapid urbanization. Despite significant potential, the widespread adoption of EVs in India is hampered by various barriers, including infrastructure limitations, policy gaps, high upfront costs, and consumer perceptions. This research paper aims to analyze these key barriers and propose strategies to accelerate the market penetration of EVs in India. Through a comprehensive literature review, policy analysis, and examination of case studies, this paper identifies the challenges hindering EV adoption and offers recommendations for addressing them. Proposed strategies include infrastructure development, policy interventions, consumer awareness initiatives, and technological innovations. By implementing these recommendations, India can overcome barriers to EV adoption and realize the environmental and economic benefits associated with clean mobility.
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Papers by IJIRCST I
The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.
The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.