Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features

Sahy, Seba Aziz; Mahdi, Sura Hammed; Gheni, Hassan Muwafaq; Al_Barazanchi, Israa

doi:10.11591/eei.v11i5.4355

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The suggested scheme's efficacy is evaluated using these metrics. The outcomes were compared to those obtained using conventional methods are offFog (OF) [17], context-federated deep reinforcement learning (C-fDRL) [18], hierarchical multi-agent deep reinforcement learning (H-MADRL) [19], and graph optimized algorithm (GOA) [20]. All the energy needed to carry out an activity, create something, or just occupy a structure is referred to as energy consumption.…”

Section: Resultsmentioning

confidence: 99%

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

et al. 2023

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The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of data transmission at the original targeted location. Initially, healthcare data was collected. Preprocessing is achieved by the normalization method. An EEEF data offloading scheme is proposed. A fruit fly optimization (FFO) technique is utilized. The performance metrics such as energy consumption, delay, resource utilization, scalability, and packet loss are analyzed and compared with existing techniques. The future scope will make use of a revolutionary optimization approach for IoMT.

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Section: Resultsmentioning

confidence: 99%

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

et al. 2023

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“…Recognizing these challenges, this is crucial for applications like autonomous vehicles, surveillance, and robotics, where accurate identification is paramount in complex real-world scenarios. The goal was to leverage recent advances in object detection technology to overcome the limitations posed by the HOG-based detection method, especially in scenarios involving moving objects, occlusions, and varying lighting conditions [25].…”

Section: Methodsmentioning

confidence: 99%

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Khalaf,

M. Abdulrahman,

Al_Barazanchi

et al. 2024

TELKOMNIKA

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Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes.

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“…However, they do convert an arbitrary input to a 32-bit output and thus can function as heuristic functions. With Adler32 [27], [28] we chose a better performing checksum algorithm for comparison. Similarly, to the previous ones, it generates 32 bits of output.…”

Section: Checksums As Heuristic Functionsmentioning

confidence: 99%

A powerful heuristic method for generating efficient database systems

Abbas,

JosephNg,

Khalaf

et al. 2023

Bulletin EEI

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Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we present the corresponding runtime results. For the coding part, the records counting part is hasty and can only work for local Hadoop part, it can be debugged and optimized for general purpose implement on Hadoop and Spark and turn into an effective performance monitor tool. As mentioned before, there are strange issue, also the performance of BLAKE2s is unexpectedly slow in that it’s widely accepted the performance of BLAKE2s is much better than MD5 and SHA256, we would like to figure out why the common-sense performance of heuristics is deferent from what we got in distributed frameworks.

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Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features

Cited by 6 publications

References 11 publications

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

A powerful heuristic method for generating efficient database systems

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