: Generally, Osteosarcoma is represented as malignant bone sarcoma which is typified by an extensive genomic disruption and a proclivity for metastatic extend. Due to the early recognition, Osteosarcoma raises the human beings' survival rate. During the early phase, several Osteosarcoma recognition approaches are developed in order to recognize the Osteosarcoma, however, evaluating the slides in the microscope to identify the tumor necrosis degree and tumor outcome is an important challenge in the medical segment. Therefore, an effectual recognition approach is modeled by exploiting the adopted Hybrid Flower Pollination algorithm with the Binary Particle Swarm Optimization algorithm based Generative Adversarial Network (FPO-BPSO based GAN) to detect the osteosarcoma during the initial phase. Moreover, the adopted FPO-BPSO is modeled using the combination of FPA and BPSO, correspondingly. As a result, the classification of the important tumor, non-tumor, as well as necrotic tumor is performed using GAN by exploiting the histology image slides. GAN is exploited to carry out the osteosarcoma recognition based on features extracted from the image via the cell segmentation process. The GAN training process is performed by exploiting the adopted Hybrid FPO-BPSO approach. Nevertheless, the adopted FPO-BPSO attained superior performance by exploiting the measures, like accuracy, sensitivity, and specificity.
一般来说，骨肉瘤表现为恶性骨肉瘤，其典型特征是广泛的基因组破坏和转移扩展的倾向。由于早期的发现，骨肉瘤提高了人类的生存率。在早期阶段，为了识别骨肉瘤，开发了几种骨肉瘤识别方法，然而，在显微镜下评估载玻片以确定肿瘤坏死程度和肿瘤结局是医学领域的重要挑战。因此，将采用的杂交授粉算法与基于二元粒子群优化算法的生成对抗网络(FPO-BPSO based GAN)结合，建立了一种有效的识别方法，在骨肉瘤的初始阶段检测骨肉瘤。采用FPA和BPSO相结合的方法对所采用的FPO-BPSO进行建模。因此，通过利用组织学图像玻片，使用GAN进行重要肿瘤，非肿瘤以及坏死肿瘤的分类。基于细胞分割过程提取的图像特征，利用GAN进行骨肉瘤识别。GAN训练过程利用采用的混合FPO-BPSO方法进行。尽管如此，采用的FPO-BPSO通过利用准确性、灵敏度和特异性等措施获得了优越的性能。
{"title":"Hybrid Flower Pollination Algorithm and Binary Particle Swarm Optimization Algorithm for Osteosarcoma Detection","authors":"Manaswi Sachin Kulkarni","doi":"10.46253/j.mr.v4i4.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v4i4.a1","url":null,"abstract":": Generally, Osteosarcoma is represented as malignant bone sarcoma which is typified by an extensive genomic disruption and a proclivity for metastatic extend. Due to the early recognition, Osteosarcoma raises the human beings' survival rate. During the early phase, several Osteosarcoma recognition approaches are developed in order to recognize the Osteosarcoma, however, evaluating the slides in the microscope to identify the tumor necrosis degree and tumor outcome is an important challenge in the medical segment. Therefore, an effectual recognition approach is modeled by exploiting the adopted Hybrid Flower Pollination algorithm with the Binary Particle Swarm Optimization algorithm based Generative Adversarial Network (FPO-BPSO based GAN) to detect the osteosarcoma during the initial phase. Moreover, the adopted FPO-BPSO is modeled using the combination of FPA and BPSO, correspondingly. As a result, the classification of the important tumor, non-tumor, as well as necrotic tumor is performed using GAN by exploiting the histology image slides. GAN is exploited to carry out the osteosarcoma recognition based on features extracted from the image via the cell segmentation process. The GAN training process is performed by exploiting the adopted Hybrid FPO-BPSO approach. Nevertheless, the adopted FPO-BPSO attained superior performance by exploiting the measures, like accuracy, sensitivity, and specificity.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128082522","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 this work, a novel image compression approach is developed that is processed in several series of technologies. Here, the first process is the image segmentation and it is done using Adaptive ACM that partitions or segments the image into two regions such as ROI as well as nonROI. Here, the adaptiveness of this ACM is determined with the idea of optimization algorithm. To handle the ROI regions, the JPEG-LS technique is exploited and to handle the non-ROI region the wavelet-based lossy compression technique is utilized. The outcome of both the JPEG-LS technique, as well as a wavelet-based compression approach is integrated with respect to the bit-stream amalgamation in order to produce the compressed image. Then, the compressed image is exploited to the image decompression that will be the overturn process of compression. It will comprise the bitstream separation that is subsequently individually process in both the wavelet-based decomposition and JPEG-LS decoding for obtaining the non-ROI regions and ROI. At last, the original image is obtained accurately. Moreover, the main objective of this paper falls in the adaptiveness under optimization. The maximum iteration and weighting factor in ACM are optimally chosen and for this a novel hybrid optimization technique is proposed, which hybridizes the concept of Differential Evolution method with Monarch Butterfly Optimization Algorithm. Here, the proposed method is compared with the conventional methods in order to shows its effectiveness for image compression.
{"title":"An Efficient Hybrid Optimization Algorithm for Image Compression","authors":"Santosh Kumar","doi":"10.46253/j.mr.v2i4.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v2i4.a1","url":null,"abstract":"In this work, a novel image compression approach is developed that is processed in several series of technologies. Here, the first process is the image segmentation and it is done using Adaptive ACM that partitions or segments the image into two regions such as ROI as well as nonROI. Here, the adaptiveness of this ACM is determined with the idea of optimization algorithm. To handle the ROI regions, the JPEG-LS technique is exploited and to handle the non-ROI region the wavelet-based lossy compression technique is utilized. The outcome of both the JPEG-LS technique, as well as a wavelet-based compression approach is integrated with respect to the bit-stream amalgamation in order to produce the compressed image. Then, the compressed image is exploited to the image decompression that will be the overturn process of compression. It will comprise the bitstream separation that is subsequently individually process in both the wavelet-based decomposition and JPEG-LS decoding for obtaining the non-ROI regions and ROI. At last, the original image is obtained accurately. Moreover, the main objective of this paper falls in the adaptiveness under optimization. The maximum iteration and weighting factor in ACM are optimally chosen and for this a novel hybrid optimization technique is proposed, which hybridizes the concept of Differential Evolution method with Monarch Butterfly Optimization Algorithm. Here, the proposed method is compared with the conventional methods in order to shows its effectiveness for image compression.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809583","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}
{"title":"An Innovative Prototype for Diagnosing and Treatment of Breast Cancer: A Case Study of Specialist Hospital Gombe","authors":"","doi":"10.46253/j.mr.v5i2.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v5i2.a1","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133307307","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}
{"title":"A Novel Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis","authors":"Egba, A Fraser, R O Obikwelu, I Blessing","doi":"10.46253/j.mr.v5i4.a2","DOIUrl":"https://doi.org/10.46253/j.mr.v5i4.a2","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133268814","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}
{"title":"Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT","authors":"J Rajeshwar","doi":"10.46253/j.mr.v6i1.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v6i1.a1","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902205","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}
: Multiple Input Multiple Output (MIMO) is exploited by the current mobile telecommunication systems with the cooperative of Orthogonal Frequency Division Multiplexing (OFDM) that is renowned as MIMO-OFDM to present sturdiness as well as superior spectrum effectiveness. In this case, the main significant confront is to attain a precise channel estimation in order to recognize the information symbols, if the receiver should possess Channel State Information (CSI) in order to balance as well as the procedure the received signal. Therefore, a competent approach is developed by developing the Improved Crow Search algorithm (ICSO) to enhance the MIMO-OFDM system performance in multimedia applications. Additionally, in the MU-MIMO system user admission control is performed by exploiting the priority-based scheduling based on Cat and Mouse Optimization algorithm (CMO) approach which is combined in the STBC-MIMO-OFDM system for competent power allocation to make sure energy effectiveness. In addition, the fitness metrics like priority, power, throughput, and Proportionally Fair are calculated. The simulation is performed in diverse fading environments with three modulation strategies, such as QPSK, BPSK, and QAM with the performance measures, like BER and throughput. The proposed model outperforms the conventional models with minimum BER and maximum throughput.
{"title":"Channel Estimation in MIMO-OFDM by Improved Crow Search Algorithm","authors":"Vinay Bandaru","doi":"10.46253/j.mr.v4i4.a4","DOIUrl":"https://doi.org/10.46253/j.mr.v4i4.a4","url":null,"abstract":": Multiple Input Multiple Output (MIMO) is exploited by the current mobile telecommunication systems with the cooperative of Orthogonal Frequency Division Multiplexing (OFDM) that is renowned as MIMO-OFDM to present sturdiness as well as superior spectrum effectiveness. In this case, the main significant confront is to attain a precise channel estimation in order to recognize the information symbols, if the receiver should possess Channel State Information (CSI) in order to balance as well as the procedure the received signal. Therefore, a competent approach is developed by developing the Improved Crow Search algorithm (ICSO) to enhance the MIMO-OFDM system performance in multimedia applications. Additionally, in the MU-MIMO system user admission control is performed by exploiting the priority-based scheduling based on Cat and Mouse Optimization algorithm (CMO) approach which is combined in the STBC-MIMO-OFDM system for competent power allocation to make sure energy effectiveness. In addition, the fitness metrics like priority, power, throughput, and Proportionally Fair are calculated. The simulation is performed in diverse fading environments with three modulation strategies, such as QPSK, BPSK, and QAM with the performance measures, like BER and throughput. The proposed model outperforms the conventional models with minimum BER and maximum throughput.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129246810","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}
: A system for assessing UX issues automatically is proposed in this paper. The facial behavior of an individual performing a specific activity is tracked in real-time with software that tracks facial motion features. Evaluated with the conventional studies, this approach has several advantages: ease of deployment in the user's natural setting; avoidance of invasive devices; and severe cost minimization. An evaluation of the user experience of the system was conducted using 144 videos that showed 12 users executing three tasks on four commercial media players. To predict the presence/absence of UX issues based on the tracker's features, we used different machine learning algorithms. We show promising outcomes that open up opportunities for automated real-time UX estimation in an environmental context
{"title":"An Ecological Approach to Measuring User Experience (UX) from Facial Expressions","authors":"Zahid Hasan","doi":"10.46253/j.mr.v5i3.a4","DOIUrl":"https://doi.org/10.46253/j.mr.v5i3.a4","url":null,"abstract":": A system for assessing UX issues automatically is proposed in this paper. The facial behavior of an individual performing a specific activity is tracked in real-time with software that tracks facial motion features. Evaluated with the conventional studies, this approach has several advantages: ease of deployment in the user's natural setting; avoidance of invasive devices; and severe cost minimization. An evaluation of the user experience of the system was conducted using 144 videos that showed 12 users executing three tasks on four commercial media players. To predict the presence/absence of UX issues based on the tracker's features, we used different machine learning algorithms. We show promising outcomes that open up opportunities for automated real-time UX estimation in an environmental context","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671119","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}
{"title":"A Study on Integration of Applied Artificial Intelligence in Accounting, Finance, Insurance, and E-Commerce Sectors","authors":"Parimalendu Bandyopadhyay","doi":"10.46253/j.mr.v5i2.a3","DOIUrl":"https://doi.org/10.46253/j.mr.v5i2.a3","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124551561","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}
{"title":"Multispectral and Hyperspectral Image Fusion: A Systematic Analysis and Review with the State of Art Techniques","authors":"G. Srishailam","doi":"10.46253/j.mr.v5i4.a1","DOIUrl":"https://doi.org/10.46253/j.mr.v5i4.a1","url":null,"abstract":"","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134492354","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}
Fahad Saddique, R. Hasan, Salman Mahmood, Nauman Mushtaq
: Attention Deficit-Hyperactivity Disorder (ADHD) is a psychiatric condition that affects children’s abilities. Nowadays computational diagnosis strategies of neuropsychiatric disorders are gaining more attention. Diagnosing this disorder based on fMRI is critical to determine the brain’s Functional Connectivity (FC). Millions of children have the symptoms of this disease.The brain is notoriously unreliable for diagnosing neurological conditions. This condition is referred to as a chronic disease.A great number of youngsters exhibit signs of this disease. As a result, the study endeavored to come up with a model and design that is both reliable and accurate for diagnosing ADHD.A variety of techniques used in this present study, such as the local binary encoding method (LBEM) is utilized for future extraction, and the hierarchical extreme learning machine (HELM)is used to extract information on the connectivity functionalities of the brain.To validate our approach, the data of One hundred fifty-three children serve as a sample for the diagnosis, from which one hundred children are ultimately determined to have ADHD.These affected ADHD children are used for our experimental purpose. According to the findings of the research, the results are based on comparing various Atlases given as AAL, CC200, and CC400. Our model gainssuperior performance with CC400 when comparedwith other Atlases.
{"title":"Classification of ADHD with the Functional Connectivity by Usage of Different Atlases in Lahore, Pakistan","authors":"Fahad Saddique, R. Hasan, Salman Mahmood, Nauman Mushtaq","doi":"10.46253/j.mr.v6i3.a4","DOIUrl":"https://doi.org/10.46253/j.mr.v6i3.a4","url":null,"abstract":": Attention Deficit-Hyperactivity Disorder (ADHD) is a psychiatric condition that affects children’s abilities. Nowadays computational diagnosis strategies of neuropsychiatric disorders are gaining more attention. Diagnosing this disorder based on fMRI is critical to determine the brain’s Functional Connectivity (FC). Millions of children have the symptoms of this disease.The brain is notoriously unreliable for diagnosing neurological conditions. This condition is referred to as a chronic disease.A great number of youngsters exhibit signs of this disease. As a result, the study endeavored to come up with a model and design that is both reliable and accurate for diagnosing ADHD.A variety of techniques used in this present study, such as the local binary encoding method (LBEM) is utilized for future extraction, and the hierarchical extreme learning machine (HELM)is used to extract information on the connectivity functionalities of the brain.To validate our approach, the data of One hundred fifty-three children serve as a sample for the diagnosis, from which one hundred children are ultimately determined to have ADHD.These affected ADHD children are used for our experimental purpose. According to the findings of the research, the results are based on comparing various Atlases given as AAL, CC200, and CC400. Our model gainssuperior performance with CC400 when comparedwith other Atlases.","PeriodicalId":167187,"journal":{"name":"Multimedia Research","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132863690","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：