Pub Date : 2024-07-02DOI: 10.18535/ijecs/v13i07.4849
Nandu Kumar, Amar Nayak
In the fields of data analytics and industrial automation, the Internet of Things (IoT) has become a game-changer. In IoT contexts, there is a growing demand for effective use of resources due to the interconnection of devices and systems. The constantly changing character of IoT systems, where resource availability and demand alter continually, presents one of the main obstacles in resource allocation management. The capacity of machine learning approaches to manage intricate and changing structures has garnered substantial interest in recent times. In the framework of the Industrial Internet of Things, this work gives a detailed comparison of various resource allocation in IoT network using machine learning algorithms.
{"title":"A Review on Resource Allocation in IoT Network using Machine Learning","authors":"Nandu Kumar, Amar Nayak","doi":"10.18535/ijecs/v13i07.4849","DOIUrl":"https://doi.org/10.18535/ijecs/v13i07.4849","url":null,"abstract":"In the fields of data analytics and industrial automation, the Internet of Things (IoT) has become a game-changer. In IoT contexts, there is a growing demand for effective use of resources due to the interconnection of devices and systems. The constantly changing character of IoT systems, where resource availability and demand alter continually, presents one of the main obstacles in resource allocation management. The capacity of machine learning approaches to manage intricate and changing structures has garnered substantial interest in recent times. In the framework of the Industrial Internet of Things, this work gives a detailed comparison of various resource allocation in IoT network using machine learning algorithms.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.18535/ijecs/v13i07.4843
Williams, J., B. E. O., Anireh V.I.E
In the era of digital communication, ensuring the confidentiality and integrity of sensitive information is paramount. This dissertation introduces a robust text encryption system that combines the strengths of Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms to create a hybrid encryption approach. Object Oriented Design (OOD) was used for the design methodology. The proposed system leverages the efficiency of AES for symmetric key encryption and the security benefits of RSA for key exchange and digital signatures. The encryption process begins with the generation of a random symmetric key for each communication session, which is then used for the AES encryption of the plaintext. The symmetric key is subsequently encrypted using the recipient's RSA public key, ensuring secure key exchange. This hybrid approach harnesses the speed of AES for bulk data encryption while utilizing RSA's asymmetric encryption for the secure sharing of secret keys. The system incorporates digital signatures generated using RSA to authenticate the sender and verify the integrity of the encrypted message. This dual-layered encryption strategy not only safeguards the confidentiality of the message but also provides assurance of the message origin and integrity. The implementation of this hybrid AES-RSA encryption system using Python programming language offers a versatile solution suitable for diverse communication channels, including email, messaging platforms, and file transfers. Its robustness against common cryptographic attacks makes it an ideal choice for securing sensitive information in various applications, such as financial transactions, healthcare communication, and government data exchange. The experimental results demonstrate the efficacy of the proposed system, with significantly reduced encryption and decryption times—0.5005 seconds and 0.5003 seconds, respectively—when compared to existing systems. This noteworthy improvement in processing speed enhances the system's practical applicability for real-time communication scenarios.
{"title":"An efficient algorithm for text encryption on android devices","authors":"Williams, J., B. E. O., Anireh V.I.E","doi":"10.18535/ijecs/v13i07.4843","DOIUrl":"https://doi.org/10.18535/ijecs/v13i07.4843","url":null,"abstract":"In the era of digital communication, ensuring the confidentiality and integrity of sensitive information is paramount. This dissertation introduces a robust text encryption system that combines the strengths of Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms to create a hybrid encryption approach. Object Oriented Design (OOD) was used for the design methodology. The proposed system leverages the efficiency of AES for symmetric key encryption and the security benefits of RSA for key exchange and digital signatures. The encryption process begins with the generation of a random symmetric key for each communication session, which is then used for the AES encryption of the plaintext. The symmetric key is subsequently encrypted using the recipient's RSA public key, ensuring secure key exchange. This hybrid approach harnesses the speed of AES for bulk data encryption while utilizing RSA's asymmetric encryption for the secure sharing of secret keys. The system incorporates digital signatures generated using RSA to authenticate the sender and verify the integrity of the encrypted message. This dual-layered encryption strategy not only safeguards the confidentiality of the message but also provides assurance of the message origin and integrity. The implementation of this hybrid AES-RSA encryption system using Python programming language offers a versatile solution suitable for diverse communication channels, including email, messaging platforms, and file transfers. Its robustness against common cryptographic attacks makes it an ideal choice for securing sensitive information in various applications, such as financial transactions, healthcare communication, and government data exchange. The experimental results demonstrate the efficacy of the proposed system, with significantly reduced encryption and decryption times—0.5005 seconds and 0.5003 seconds, respectively—when compared to existing systems. This noteworthy improvement in processing speed enhances the system's practical applicability for real-time communication scenarios.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"39 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-09DOI: 10.18535/ijecs/v11i08.4692
Goli Mallesham
Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported. The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.
{"title":"Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques","authors":"Goli Mallesham","doi":"10.18535/ijecs/v11i08.4692","DOIUrl":"https://doi.org/10.18535/ijecs/v11i08.4692","url":null,"abstract":"Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported.\u0000The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":" 72","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-09DOI: 10.18535/ijecs/v11i08.4691
Goli Mallesham
Supply chain management is an approach used by firms to ensure that their business can be highly effective, and profitable and that operations run smoothly. This involves managing the movement of raw materials inwards and finished goods outwards. Logistics is a key component of this. It also involves managing the flow of products between companies, which can involve the movement of products between a manufacturer, a wholesaler, and a retailer. Supply chain management is therefore the integration of these flows between companies. Several innovative techniques can be used to optimize supply chain operations, particularly given recent advances in information technology. The function of logistics is known as activities that are related to the flow of products between companies, such as the transportation and warehousing of goods. Activities that take place within companies, such as inventory management and materials handling, are not considered to be logistical activities but part of the supply chain. The level of interest in supply chain management has risen quite dramatically over the last few years. This is partly due to advances in information technology, which have enabled closer integration of the supply chain. As well as increasing competition between companies, on both a national and an international level, has led to an increasing emphasis on the need for companies to concentrate on their core competencies, and to look to outside suppliers to provide other goods and services. This has led to the increased use of external suppliers.
{"title":"Innovative Techniques for Optimizing Supply Chain Operations","authors":"Goli Mallesham","doi":"10.18535/ijecs/v11i08.4691","DOIUrl":"https://doi.org/10.18535/ijecs/v11i08.4691","url":null,"abstract":"Supply chain management is an approach used by firms to ensure that their business can be highly effective, and profitable and that operations run smoothly. This involves managing the movement of raw materials inwards and finished goods outwards. Logistics is a key component of this. It also involves managing the flow of products between companies, which can involve the movement of products between a manufacturer, a wholesaler, and a retailer. Supply chain management is therefore the integration of these flows between companies. Several innovative techniques can be used to optimize supply chain operations, particularly given recent advances in information technology. The function of logistics is known as activities that are related to the flow of products between companies, such as the transportation and warehousing of goods. Activities that take place within companies, such as inventory management and materials handling, are not considered to be logistical activities but part of the supply chain. The level of interest in supply chain management has risen quite dramatically over the last few years. This is partly due to advances in information technology, which have enabled closer integration of the supply chain. As well as increasing competition between companies, on both a national and an international level, has led to an increasing emphasis on the need for companies to concentrate on their core competencies, and to look to outside suppliers to provide other goods and services. This has led to the increased use of external suppliers.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.18535/ijecs/v13i06.4827
Garvit Sharma, Karthik Pragada, Poushali Deb Purkayastha, Yukta Vajpayee
The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniques such as Hybrid Filtering, Content-Based Filtering, Demographic Filtering, and Collaborative Filtering catering to personalized information needs across industries like entertainment, education, and healthcare. Moreover, emerging types of recommendation systems, including Knowledge-Based, RiskAware, Social-Networking, and Context-Aware, further broaden their applicability, addressing specific user needs and preferences. Leveraging machine learning and AI algorithms on big data, recommendation systems have become a quintessential application of big data analytics, enhancing user experience and engagement in domains like e-learning, tourism, and news dissemination. However, scaling recommendation systems present challenges due to the exponential growth of input data, necessitating strategies like Dimensionality Reduction and cluster-based methods. Integrating multiple recommendation algorithms enhances system complexity, requiring careful consideration of algorithm selection, performance monitoring, and maintenance. Transparency and explanation mechanisms become crucial in complex systems to foster user trust and understanding. Despite challenges, recommendation systems continue to drive innovation, delivering personalized recommendations and enriching user experiences across various domains.
{"title":"Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches","authors":"Garvit Sharma, Karthik Pragada, Poushali Deb Purkayastha, Yukta Vajpayee","doi":"10.18535/ijecs/v13i06.4827","DOIUrl":"https://doi.org/10.18535/ijecs/v13i06.4827","url":null,"abstract":"The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniques such as Hybrid Filtering, Content-Based Filtering, Demographic Filtering, and Collaborative Filtering catering to personalized information needs across industries like entertainment, education, and healthcare. Moreover, emerging types of recommendation systems, including Knowledge-Based, RiskAware, Social-Networking, and Context-Aware, further broaden their applicability, addressing specific user needs and preferences. Leveraging machine learning and AI algorithms on big data, recommendation systems have become a quintessential application of big data analytics, enhancing user experience and engagement in domains like e-learning, tourism, and news dissemination. However, scaling recommendation systems present challenges due to the exponential growth of input data, necessitating strategies like Dimensionality Reduction and cluster-based methods. Integrating multiple recommendation algorithms enhances system complexity, requiring careful consideration of algorithm selection, performance monitoring, and maintenance. Transparency and explanation mechanisms become crucial in complex systems to foster user trust and understanding. Despite challenges, recommendation systems continue to drive innovation, delivering personalized recommendations and enriching user experiences across various domains.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pneumococcal disease, caused by Streptococcus pneumoniae, poses a significant health challenge, particularly in resource-limited settings like Bonny Island, Nigeria. This study employs neural networks and artificial intelligence to predict pneumococcal disease, addressing the critical need for early diagnosis and intervention. Methodologically, the research encompasses data collection, cleaning, correlation analysis, and model development, ensuring a robust system for early disease prediction. By analyzing demographic, clinical, and environmental factors, the study identifies significant predictors of pneumococcal disease risk. In comparison with Random Forest and Support Vector Machines trained on the same data, the neural network achieved 100 percent accuracy, recall, precision, and f1 scores. The integration of the neural network model into a web application facilitates real-time predictions, enabling healthcare providers to input symptoms and receive immediate diagnostic insights. This approach enhances timely interventions, potentially reducing morbidity and mortality associated with pneumococcal disease. Despite challenges like data quality and integration, the findings demonstrate the efficacy of AI-driven models in improving public health outcomes. The deployment of such models in Bonny Island underscores their practicality and scalability, paving the way for broader applications in similar contexts. Ultimately, this study not only advances understanding of pneumococcal disease epidemiology in Bonny Island but also contributes to global efforts in enhancing healthcare delivery through innovative technological solutions. Future research should focus on continuous model refinement and validation with larger datasets to further improve accuracy and reliability.
由肺炎链球菌引起的肺炎球菌疾病是一项重大的健康挑战，尤其是在尼日利亚邦尼岛等资源有限的地区。本研究利用神经网络和人工智能预测肺炎球菌疾病，以满足早期诊断和干预的迫切需要。在方法上，研究包括数据收集、清理、相关性分析和模型开发，以确保建立一个强大的早期疾病预测系统。通过分析人口、临床和环境因素，该研究确定了肺炎球菌疾病风险的重要预测因素。与在相同数据上训练的随机森林和支持向量机相比，神经网络的准确率、召回率、精确度和 f1 分数均达到了 100%。将神经网络模型集成到网络应用程序中有助于进行实时预测，使医疗服务提供者能够输入症状并立即获得诊断意见。这种方法可以加强及时干预，从而降低与肺炎球菌疾病相关的发病率和死亡率。尽管存在数据质量和整合等挑战，但研究结果证明了人工智能驱动的模型在改善公共卫生成果方面的功效。在邦尼岛部署此类模型凸显了其实用性和可扩展性，为在类似环境中更广泛的应用铺平了道路。最终，这项研究不仅加深了人们对邦尼岛肺炎球菌疾病流行病学的了解，还有助于全球通过创新技术解决方案来改善医疗保健服务的努力。未来的研究应侧重于利用更大的数据集不断完善和验证模型，以进一步提高准确性和可靠性。
{"title":"Utilizing Neural Networks for Early Prediction of Pneumococcal Disease: A Case Study in Bonny Island, Nigeria","authors":"Fiberesima Alalibo Ralph, Ogunnusi, Samuel.O, Pronen Innocent","doi":"10.18535/ijecs/v13i06.4828","DOIUrl":"https://doi.org/10.18535/ijecs/v13i06.4828","url":null,"abstract":"Pneumococcal disease, caused by Streptococcus pneumoniae, poses a significant health challenge, particularly in resource-limited settings like Bonny Island, Nigeria. This study employs neural networks and artificial intelligence to predict pneumococcal disease, addressing the critical need for early diagnosis and intervention. Methodologically, the research encompasses data collection, cleaning, correlation analysis, and model development, ensuring a robust system for early disease prediction. By analyzing demographic, clinical, and environmental factors, the study identifies significant predictors of pneumococcal disease risk. In comparison with Random Forest and Support Vector Machines trained on the same data, the neural network achieved 100 percent accuracy, recall, precision, and f1 scores. The integration of the neural network model into a web application facilitates real-time predictions, enabling healthcare providers to input symptoms and receive immediate diagnostic insights. This approach enhances timely interventions, potentially reducing morbidity and mortality associated with pneumococcal disease. Despite challenges like data quality and integration, the findings demonstrate the efficacy of AI-driven models in improving public health outcomes. The deployment of such models in Bonny Island underscores their practicality and scalability, paving the way for broader applications in similar contexts. Ultimately, this study not only advances understanding of pneumococcal disease epidemiology in Bonny Island but also contributes to global efforts in enhancing healthcare delivery through innovative technological solutions. Future research should focus on continuous model refinement and validation with larger datasets to further improve accuracy and reliability.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"30 2‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-12DOI: 10.18535/ijecs/v12i07.4744
Dr Mansoor Farooq
The Internet of Things (IoT) presents a wide range of issues and challenges. Security is a major concern for IoT technologies, applications, and networks. IoT's research progress is discussed in this paper, which focuses on this primary feature of IoT and describes various security issues and concerns. The Internet of Things and the concept of edge computing have enabled many new IoT applications, including smart homes, intelligent transportation, pioneering health, smart grids, and smart energy. It also introduces a slew of unanticipated challenges to data security. Cybersecurity, edge computing, the Internet of Things, and artificial intelligence all present exciting new research and development prospects. There are many new threats and opportunities and this paper will focus on them.
{"title":"Artificial Intelligence-Based Approach on Cybersecurity Challenges and Opportunities in The Internet of Things & Edge Computing Devices","authors":"Dr Mansoor Farooq","doi":"10.18535/ijecs/v12i07.4744","DOIUrl":"https://doi.org/10.18535/ijecs/v12i07.4744","url":null,"abstract":"The Internet of Things (IoT) presents a wide range of issues and challenges. Security is a major concern for IoT technologies, applications, and networks. IoT's research progress is discussed in this paper, which focuses on this primary feature of IoT and describes various security issues and concerns. The Internet of Things and the concept of edge computing have enabled many new IoT applications, including smart homes, intelligent transportation, pioneering health, smart grids, and smart energy. It also introduces a slew of unanticipated challenges to data security. Cybersecurity, edge computing, the Internet of Things, and artificial intelligence all present exciting new research and development prospects. There are many new threats and opportunities and this paper will focus on them.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134301106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.18535/ijecs/v12i07.4741
MR. Bukola
Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.
{"title":"A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System","authors":"MR. Bukola","doi":"10.18535/ijecs/v12i07.4741","DOIUrl":"https://doi.org/10.18535/ijecs/v12i07.4741","url":null,"abstract":"Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125080843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research paper focuses on the application of computer vision techniques using Python and OpenCV for image analysis and interpretation. The main objective is to develop a system capable of performing various tasks such as object detection, recognition, and image processing. The project employs a combination of traditional computer vision algorithms and deep learning models to achieve accurate and efficient results. The research paper begins with essential preprocessing steps, including image acquisition, resizing, and noise reduction. Feature extraction techniques are utilized to capture relevant information from images, followed by object detection using methods like Haar cascades or deep learning-based approaches such as YOLO. Object recognition is achieved through feature matching or deep learning-based classification models. Furthermore, image processing techniques, including image enhancement, segmentation, and filtering, are applied to improve image quality and extract meaningful information. The system is implemented using Python programming language, leveraging the powerful OpenCV library for various computer vision tasks.
{"title":"Real-Time Object Detection Using SSD MobileNet Model of Machine Learning","authors":"Anurag Gupta, Darshan Yadav, Akash Raj, Ayushman Pathak","doi":"10.18535/ijecs/v12i05.4735","DOIUrl":"https://doi.org/10.18535/ijecs/v12i05.4735","url":null,"abstract":"This research paper focuses on the application of computer vision techniques using Python and OpenCV for image analysis and interpretation. The main objective is to develop a system capable of performing various tasks such as object detection, recognition, and image processing. The project employs a combination of traditional computer vision algorithms and deep learning models to achieve accurate and efficient results. The research paper begins with essential preprocessing steps, including image acquisition, resizing, and noise reduction. Feature extraction techniques are utilized to capture relevant information from images, followed by object detection using methods like Haar cascades or deep learning-based approaches such as YOLO. Object recognition is achieved through feature matching or deep learning-based classification models. Furthermore, image processing techniques, including image enhancement, segmentation, and filtering, are applied to improve image quality and extract meaningful information. The system is implemented using Python programming language, leveraging the powerful OpenCV library for various computer vision tasks.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.18535/ijecs/v12i05.4734
Manju Sharma
Transaction volumes throughout the world are growing very fastly and that result in the complexities, vulnerabilities, inefficiencies, and higher costs of current transaction systems. The growth of ecommerce, online banking, and in-app purchases, is increasing and is more popular among the people around the world. And transaction volumes are increasing with the advent of the Internet of Things (IoT). Objects, such as laptop, washing machines and groceries are running low and cars that deliver themselves to your door. To meet these challenges and others we need faster payment methods that are trustworthy and require no specialized equipment with no chargebacks or monthly fees and offer a good bookkeeping solution for ensuring transparency.
{"title":"Implementation of Blockchain technology","authors":"Manju Sharma","doi":"10.18535/ijecs/v12i05.4734","DOIUrl":"https://doi.org/10.18535/ijecs/v12i05.4734","url":null,"abstract":"Transaction volumes throughout the world are growing very fastly and that result in the complexities, vulnerabilities, inefficiencies, and higher costs of current transaction systems. The growth of ecommerce, online banking, and in-app purchases, is increasing and is more popular among the people around the world. And transaction volumes are increasing with the advent of the Internet of Things (IoT). Objects, such as laptop, washing machines and groceries are running low and cars that deliver themselves to your door. To meet these challenges and others we need faster payment methods that are trustworthy and require no specialized equipment with no chargebacks or monthly fees and offer a good bookkeeping solution for ensuring transparency. \u0000 ","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129690926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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