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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES)
Parveen, Asma
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Efficient fault tolerant cost optimized approach for scientific workflow via optimal replication technique within cloud computing ecosystem Anjum, Asma; Parveen, Asma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp122-132

Abstract

Cloud computing is one of the dispersed and effective computing models, which offers tremendous opportunity to address scientific issues with big scale characteristics. Despite having such a dynamic computing paradigm, it faces several difficulties and falls short of meeting the necessary quality of services (QoS) standards. For sustainable cloud computing workflow, QoS is very much required and need to be addressed. Recent studies looked on quantitative fault-tolerant programming to reduce the number of copies while still achieving the reliability necessity of a process on the heterogeneous infrastructure as a service (IaaS) cloud. In this study, we create an optimal replication technique (ORT) about fault tolerance as well as cost-driven mechanism and this is known as optimal replication technique with fault tolerance and cost minimization (ORT-FTC). Here ORT-FTC employs an iterative-based method that chooses the virtual machine and its copies that have the shortest makespan in the situation of specific tasks. By creating test cases, ORT-FTC is tested while taking into account scientific workflows like CyberShake, laser interferometer gravitational-wave observatory (LIGO), montage, and sipht. Additionally, ORT-FTC is shown to be only slightly improved over the current model in all cases. 
Proximate node aware optimal and secure data aggregation in wireless sensor network based IoT environment Priyadarshini, Sushma; Parveen, Asma
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp143-150

Abstract

Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.
Abnormality-aware bone fracture detection and classification using the triple context attention model Sultana, Tabassum Nahid; Parveen, Asma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4667-4674

Abstract

In this study, a novel approach is introduced for fracture detection in bone x-ray images, introducing the triple context attention model (TCAN) that combines concentrated extensive convolutional segments with an attention mechanism to enhance positional data. The TCAN model significantly improves fracture recognition accuracy while reducing model complexity. Leveraging a diverse dataset, consistently achieving high accuracy levels across various body parts. By addressing, mislabelling issues, and employing a visual attention network (VAN), to refine the model's performance. The TCAN model emerges as a robust, computationally efficient solution, offering a remarkable average accuracy of 97.86%. This study contributes valuable advancements to medical imaging and diagnostics, providing a highly effective tool for skeletal fracture detection.
Catalysing precision in bone x-ray analysis for image detection and classification: the triple context attention model advancement Sultana, Tabassum N.; Hegde, Nagaratna P.; Parveen, Asma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4957-4970

Abstract

Accurate detection and classification of fractures in bone x-ray images are crucial for effective medical diagnosis and treatment. In this study, we propose the triple context attention model (TCAN) as a novel approach to address the challenges in this domain. TCAN offers several key contributions that significantly enhance the accuracy and efficiency of bone x-ray image recognition and classification. Firstly, TCAN introduces the coordination attention mechanism, which considers both horizontal and vertical positional data during the recognition process. Secondly, TCAN mitigates the common issue of mislabelling fractures in bone x-ray images, particularly in the you only look once (YOLO) model, due to the absence of positional data during training. Thirdly, TCAN efficiently enhances positional data by focusing on weights, and increasing feature dimension while maintaining a manageable model size. This allows for effective utilization of positional data without computational overhead. Lastly, TCAN combines the visual attention network (VAN) with its capabilities, resulting in a comprehensive system that can handle diverse image dimensions and accurately classify various types of fractures across different body regions. Overall, TCAN presents a promising advancement in medical image analysis, improving fracture detection accuracy and classification efficiency in bone x-ray images, thus aiding in more effective clinical decision-making.
Co-Authors Anjum, Asma Hegde, Nagaratna P. Priyadarshini, Sushma Sultana, Tabassum N. Sultana, Tabassum Nahid