Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women’s health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time.
乳腺癌是全球关注的重大健康问题，凸显了早期检测对有效治疗妇女健康的极端重要性。虽然卷积网络（CNN）一直是分析医学图像的最佳工具，但最近人们对利用视觉变换器（ViT）进行医学数据分析产生了兴趣。本研究旨在对自注意变换器（VIT）、紧凑型卷积变换器（CCT）和标记学习器（TVIT）这三种系统进行综合比较，以便将乳腺 X 射线图像分为良性组织和癌组织。实验使用了 DDSM 数据集，其中包括 5970 张良性图像和 7158 张恶性图像。对所提模型的性能准确性进行了评估，结果显示 VIT 为 99.81%，CCT 为 99.92%，TVIT 为 99.05%。此外，研究还将这些结果与当前最先进的性能指标进行了比较。研究结果表明了卷积-注意力机制如何有效地帮助开发用于诊断乳腺癌的强大计算机辅助系统。值得注意的是，所提出的方法在实现高性能结果的同时，还最大限度地减少了所需的计算资源，缩短了决策时间。
{"title":"Vision transformer-convolution for breast cancer classification using mammography images: A comparative study","authors":"Mouhamed Laid Abimouloud, Khaled Bensid, Mohamed Elleuch, Oussama Aiadi, Monji Kherallah","doi":"10.3233/his-240002","DOIUrl":"https://doi.org/10.3233/his-240002","url":null,"abstract":"Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women’s health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127674","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}
Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.
{"title":"Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model","authors":"Rakesh Sengupta, Anuj Shukla, Ravichander Janapati, Bhavesh Verma","doi":"10.3233/his-240007","DOIUrl":"https://doi.org/10.3233/his-240007","url":null,"abstract":"Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128470","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}
Luka Jovanovic, M. Zivkovic, Nebojša Bačanin, Aleksandra Bozovic, Peter Bisevac, Milos Antonijevic
This study explores the realm of time series forecasting, focusing on the utilization of Recurrent Neural Networks (RNN) to detect abnormal cardiovascular rhythms in Electrocardiogram (ECG) signals. The principal objective is to optimize RNN performance by finely tuning hyperparameters, a complex task with known NP-hard complexity. To address this challenge, the study employs metaheuristic algorithms, specialized problem-solving techniques crafted for navigating intricate and non-deterministic optimization landscapes. Additionally, a refined algorithm is introduced to overcome limitations inherent in the original approach. This modified algorithm exhibits significant improvements, surpassing its predecessor in identifying anomalous cardiovascular rhythms within ECG signals. The most successful optimized model achieves an accuracy of 99.26%, outperforming models optimized by other contemporary metaheuristics assessed in the study. Further experimentation extends the initial inquiry by exploring the capabilities of Long Short-Term Memory (LSTM) models augmented by attention layers. In this extension, the best models demonstrate an accuracy of 99.83%, surpassing the original RNN models. These findings underscore the crucial importance of refining machine learning models and emphasize the potential for substantial advancements in healthcare through innovative algorithmic approaches.
{"title":"Metaheuristic optimized electrocardiography time-series anomaly classification with recurrent and long-short term neural networks","authors":"Luka Jovanovic, M. Zivkovic, Nebojša Bačanin, Aleksandra Bozovic, Peter Bisevac, Milos Antonijevic","doi":"10.3233/his-240005","DOIUrl":"https://doi.org/10.3233/his-240005","url":null,"abstract":"This study explores the realm of time series forecasting, focusing on the utilization of Recurrent Neural Networks (RNN) to detect abnormal cardiovascular rhythms in Electrocardiogram (ECG) signals. The principal objective is to optimize RNN performance by finely tuning hyperparameters, a complex task with known NP-hard complexity. To address this challenge, the study employs metaheuristic algorithms, specialized problem-solving techniques crafted for navigating intricate and non-deterministic optimization landscapes. Additionally, a refined algorithm is introduced to overcome limitations inherent in the original approach. This modified algorithm exhibits significant improvements, surpassing its predecessor in identifying anomalous cardiovascular rhythms within ECG signals. The most successful optimized model achieves an accuracy of 99.26%, outperforming models optimized by other contemporary metaheuristics assessed in the study. Further experimentation extends the initial inquiry by exploring the capabilities of Long Short-Term Memory (LSTM) models augmented by attention layers. In this extension, the best models demonstrate an accuracy of 99.83%, surpassing the original RNN models. These findings underscore the crucial importance of refining machine learning models and emphasize the potential for substantial advancements in healthcare through innovative algorithmic approaches.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128478","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}
Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability.
{"title":"Classifications, evaluation metrics, datasets, and domains in recommendation services: A survey","authors":"Luong Vuong Nguyen","doi":"10.3233/his-240003","DOIUrl":"https://doi.org/10.3233/his-240003","url":null,"abstract":"Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128721","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}
Amjad Pendhari, Nazneen A. Pendhari, Santosh Singh
The use of social media is becoming increasingly important in our day-to-day activities. Platforms for social media are utilised on a daily basis, and it has been seen that young adults make regular use of social media, even while they are in the midst of an emergency scenario. For the purpose of communication, individuals, businesses, and governments all make use of various social media platforms. Through their efforts to establish communication with their loved ones who are residing in regions that have been impacted by disasters, a great number of people are demonstrating their profound concern for the well-being of those individuals. The individuals are looking for a variety of necessities, including food, help, pharmaceuticals, lodging, transportation, and other necessities. It is possible for telecommunication networks to experience a breakdown or become incapable of adequately accommodating a sudden spike in the number of users attempting to connect to the network during times of crisis. There is a widespread use of short messaging service (SMS) mobile text messages in the modern communication landscape. Platforms for social media websites that are accessible online have the potential to effectively regulate the flow of communication. The existence of social media networks that are technologically scalable makes it possible for this to be a feasible option. The usage of a platform that enables communication in both directions has the potential to outperform the efficiency of conventional channels that only transmit information in one direction, such as radio and television, when it comes to crisis situations. The proliferation of network technologies has resulted in an increased emphasis on the examination of the features of network components, the mitigation of the affects of these components, and the rapid restoration of operations in the event of disasters. It is possible to improve the efficiency, dependability, and participatory nature of emergency communication by making use of various social media platforms. Consequently, it is possible to make the observation that crises have become an integral part of the ecosystem of social media in the modern day.
{"title":"A hybrid approach of machine learning algorithms for improving accuracy of social media crisis detection","authors":"Amjad Pendhari, Nazneen A. Pendhari, Santosh Singh","doi":"10.3233/his-240011","DOIUrl":"https://doi.org/10.3233/his-240011","url":null,"abstract":"The use of social media is becoming increasingly important in our day-to-day activities. Platforms for social media are utilised on a daily basis, and it has been seen that young adults make regular use of social media, even while they are in the midst of an emergency scenario. For the purpose of communication, individuals, businesses, and governments all make use of various social media platforms. Through their efforts to establish communication with their loved ones who are residing in regions that have been impacted by disasters, a great number of people are demonstrating their profound concern for the well-being of those individuals. The individuals are looking for a variety of necessities, including food, help, pharmaceuticals, lodging, transportation, and other necessities. It is possible for telecommunication networks to experience a breakdown or become incapable of adequately accommodating a sudden spike in the number of users attempting to connect to the network during times of crisis. There is a widespread use of short messaging service (SMS) mobile text messages in the modern communication landscape. Platforms for social media websites that are accessible online have the potential to effectively regulate the flow of communication. The existence of social media networks that are technologically scalable makes it possible for this to be a feasible option. The usage of a platform that enables communication in both directions has the potential to outperform the efficiency of conventional channels that only transmit information in one direction, such as radio and television, when it comes to crisis situations. The proliferation of network technologies has resulted in an increased emphasis on the examination of the features of network components, the mitigation of the affects of these components, and the rapid restoration of operations in the event of disasters. It is possible to improve the efficiency, dependability, and participatory nature of emergency communication by making use of various social media platforms. Consequently, it is possible to make the observation that crises have become an integral part of the ecosystem of social media in the modern day.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129607","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}
Daniel E. Asuquo, Uduak A. Umoh, Samuel A. Robinson, Emmanuel A. Dan, Samuel S. Udoh, K. Attai
The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.
{"title":"Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks","authors":"Daniel E. Asuquo, Uduak A. Umoh, Samuel A. Robinson, Emmanuel A. Dan, Samuel S. Udoh, K. Attai","doi":"10.3233/his-240009","DOIUrl":"https://doi.org/10.3233/his-240009","url":null,"abstract":"The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129658","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}
The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor can execute its program part concurrently with the other processors. This study compares OMP and MATLAB, two important parallel computing simulation tools, through the use of a dense matrix multiplication technique. The results showed that OMP outperformed the MATLAB parallel environment by over 8 times in sequential execution and 6 times in parallel execution. From this proposed method, it was also observed that OMP with an even slower processor performs much better than MATLAB with a higher processor. Thus, the present analysis indicates that OMP is a superior environment for parallel computing and should be preferred over parallel MATLAB.
{"title":"A comparative assessment of OMP and MATLAB for parallel computation","authors":"Yajnaseni Dash, Ajith Abraham","doi":"10.3233/his-240001","DOIUrl":"https://doi.org/10.3233/his-240001","url":null,"abstract":"The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor can execute its program part concurrently with the other processors. This study compares OMP and MATLAB, two important parallel computing simulation tools, through the use of a dense matrix multiplication technique. The results showed that OMP outperformed the MATLAB parallel environment by over 8 times in sequential execution and 6 times in parallel execution. From this proposed method, it was also observed that OMP with an even slower processor performs much better than MATLAB with a higher processor. Thus, the present analysis indicates that OMP is a superior environment for parallel computing and should be preferred over parallel MATLAB.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357004","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}
The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.
{"title":"Novel odor sensing intelligence and surveillance capabilities in controller-responder robots","authors":"Serena Gandhi, Ajith Abraham","doi":"10.3233/his-230017","DOIUrl":"https://doi.org/10.3233/his-230017","url":null,"abstract":"The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"49 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139185353","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}
In [1] Karanwal et al. introduced the novel color descriptor in Face Recognition (FR) called as Fused Local Color Pattern (FLCP). In FLCP, the RGB color format is utilized for extracting features. From R, G and B channels, the MRELBP-NI, 6 × 6 MB-LBP and RD-LBP are imposed for feature extraction and then all are integrated to form the FLCP size. FLCP beats the accuracy of various methods. The one major shortcoming observed in [1] is that the basic format RGB is used for extracting features. Literature suggests that other hybrid formats achieves better recognition rates than RGB. Motivated from literature, the proposed work uses the hybrid color space format RCrQ for feature extraction. In this format R channel is taken from RGB, Cr channel is taken from YCbCr and Q channel is taken from YIQ. On R channel, MRELBP-NI is imposed for extracting features, On Cr channel 6 × 6 MB-LBP is imposed and on Q channel RD-LBP is imposed for extracting features. Then all channel features are joined to build the robust and discriminant feature called as Robust And Discriminant Local Color Pattern (RADLCP). Compression and matching is assisted from PCA and SVMs. For evaluating results GT face dataset is used. Results proves the potency of RADLCP in contrast to gray scale based implemented descriptors. RADLCP also beats the results of FLCP. Several literature techniques are also outclassed by RADLCP. For evaluating all the results MATLAB R2021a is used.
{"title":"Robust And Discriminant Local Color Pattern (RADLCP): A novel color descriptor for face recognition","authors":"Shekhar Karanwal","doi":"10.3233/his-230016","DOIUrl":"https://doi.org/10.3233/his-230016","url":null,"abstract":"In [1] Karanwal et al. introduced the novel color descriptor in Face Recognition (FR) called as Fused Local Color Pattern (FLCP). In FLCP, the RGB color format is utilized for extracting features. From R, G and B channels, the MRELBP-NI, 6 × 6 MB-LBP and RD-LBP are imposed for feature extraction and then all are integrated to form the FLCP size. FLCP beats the accuracy of various methods. The one major shortcoming observed in [1] is that the basic format RGB is used for extracting features. Literature suggests that other hybrid formats achieves better recognition rates than RGB. Motivated from literature, the proposed work uses the hybrid color space format RCrQ for feature extraction. In this format R channel is taken from RGB, Cr channel is taken from YCbCr and Q channel is taken from YIQ. On R channel, MRELBP-NI is imposed for extracting features, On Cr channel 6 × 6 MB-LBP is imposed and on Q channel RD-LBP is imposed for extracting features. Then all channel features are joined to build the robust and discriminant feature called as Robust And Discriminant Local Color Pattern (RADLCP). Compression and matching is assisted from PCA and SVMs. For evaluating results GT face dataset is used. Results proves the potency of RADLCP in contrast to gray scale based implemented descriptors. RADLCP also beats the results of FLCP. Several literature techniques are also outclassed by RADLCP. For evaluating all the results MATLAB R2021a is used.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42427362","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}
Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the “wet” season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test.
{"title":"Rainfall data classification using Mann-Kendall test statistics associated with Neuro Fuzzy technique: A case study of Chennai district","authors":"A. Raj, H. Henrietta, J. P. Angelena","doi":"10.3233/his-230010","DOIUrl":"https://doi.org/10.3233/his-230010","url":null,"abstract":"Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the “wet” season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"11 1","pages":"95-109"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79291672","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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