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3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Hu, Wu-Chih Wu, Hsin-Te Zhan, Jun-We Zhang, Jing-Mi Tseng, Fan-Hsun Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Magong880011 Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Yilan26047 Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taipei10610 Taiwan

ISBN: (纸本)9783030639402

The research develops an intelligent aquaculture system to detect the water quality of a culture pond. Additionally, using Fuzzy Logic to evaluate water quality that influences the aquaculture livability. Each species requires a different environment of water quality;therefore, the study utilizes an intelligent aquaculture system to detect the water quality of white shrimp ponds. After using Fuzzy Logic to analyze water quality, the result is delivered as equally divided into five levels of signals sections. The purpose of the research is to understand whether the aquaculture environment is suitable for white shrimps by detecting the water quality;consequently, through studying the livability to understand the importance of water quality. From the experimental results, the water quality of targeted aquaculture ponds are all within the livability range of white shrimp;the result has shown a livability rate of 33%, which is considered high livability in marine white shrimp farming. Hence, it is concluded that water quality has a high correlation with livability. Moreover, the study demonstrates that water monitoring and water quality analysis are beneficial to monitor the aquaculture environment, which can further increase the livability of white shrimp and boost income. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Water quality

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Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

arXiv

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arXiv 2022年

作者： Azhir, Elham Hosseinzadeh, Mehdi Khan, Faheem Mosavi, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran*** Iran Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran*** Iran Computer Science University of Human Development Sulaymaniyah0778-6 Iraq Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of *** Codes 68T05 © 2022, CC BY.

关键词： MapReduce

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

arXiv

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Enhancing Pathogen Identification in Cheese with High Background Microflora Using an Artificial Neural Network-Enabled Paper Chromogenic Array Sensor Approach

SSRN

引用

SSRN 2024年

作者： Jia, Zhen Lin, Zhuangsheng Luo, Yaguang Cardoso, Zachary Wang, Dayang Flock, Genevieve Thompson-Witrick, Katherine A. Yu, Hengyong Zhang, Boce Food Science and Human Nutrition Department University of Florida GainesvilleFL32611 United States Department of Biomedical & Nutritional Sciences University of Massachusetts LowellMA01854 United States Environmental Microbial and Food Safety Lab and Food Quality Lab U.S. Department of Agriculture Agricultural Research Service BeltsvilleMD20705 United States Department of Chemical Engineering University of Massachusetts LowellMA01854 United States Department of Electrical and Computer Engineering University of Massachusetts LowellMA01854 United States US Army Natick Soldier Research Development and Engineering Center NatickMA01760 United States

Biohazards, which may occur at all stages of the supply chain, pose significant threats to food safety and public health. Addressing these concerns and enhancing food safety necessitates a nondestructive pathogen surveillance approach capable of continuously and simultaneously detecting multiple pathogens. Detecting and differentiating low concentrations of pathogenic bacteria amid high background microflora levels in foods is challenging, requiring technology with high sensitivity and robust discriminatory capability. This study introduces an artificial neural network -driven paper chromogenic array sensor (ANN-PCA) technique developed for the nondestructive, continuous, and simultaneous detection of Salmonella Enteritidis (SE) and Escherichia coli O157:H7 (Ec) from a high background microflora in shredded cheddar cheese. This method enables accurate detection of SE and Ec in monoculture and cocktail culture, while distinguishing them from a high level of background microflora (~7.5 log CFU/g), with accuracies ranging from 72 ± 11% to 92 ± 3%. In addition, SE and Ec were successfully identified at concentrations as low as 1 log CFU/g within one day, with an accuracy of 72 ± 11%. This approach exhibits promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in foods throughout the supply chain without enrichment, incubation, or other sample preparation steps. © 2024, The Authors. All rights reserved.

关键词： Health risks

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Enhanced Identification of Pheochromocytoma and Paragangliomas’ Genetic Clusters from CT

Enhanced Identification of Pheochromocytoma and Paragangliom...

引用

Medical Imaging 2025: computer-Aided Diagnosis

作者： Makroo, Owais Santra, Bikash Mukherjee, Pritam Mathai, Tejas Sudharshan Jha, Abhishek Patel, Mayank Pacak, Karel Summers, Ronald M. Department of Mathematics Indian Institute of Technology West Bengal Kharagpur India School of AI and Data Science Indian Institute of Technology Rajasthan Jodhpur India Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Radiology and Imaging Sciences Clinical Center National Institutes of Health BethesdaMD United States National Institute of Child Health and Human Development National Institutes of Health BethesdaMD United States

ISBN: (纸本)9781510685925

Pheochromocytoma and Paraganglioma (PPGL) tumors are uncommon neuroendocrine tumors that exhibit substantial genetic and phenotypic variability, making their classification challenging. We describe a new method that performs supervised contrastive learning leveraging DinoV2 feature representations to categorize genetic clusters of PPGL tumors into four distinct subtypes: SDHx, VHL-EPAS1, kinase signaling, and sporadic. The proposed supervised contrastive learning framework teaches a shallow linear network using feature representations taken from a DinoV2 model that has already been pre-trained with ImageNet-21K. This training process optimizes both contrastive and classification losses together, taking into account the genetic groups and four anatomical placements of PPGLs: head and neck, chest, adrenal, and extra-adrenal abdomen and pelvis. The proposal was tested on a dataset consisting of 650 contrast-enhanced computed tomography (CE-CT) axial images of 287 PPGL patients. The results showed a balanced accuracy of 0.582±0.079, an AUC score of 0.805±0.055, and an F1 score of 0.551±0.063 using five-fold cross-validation. Our methodology demonstrated superior performance compared to other methods by balancing the performance across all four genetic types, achieving a 2% higher balanced accuracy and a 7–14% higher F1 score, while using the entire axial slice as a single sample in contrast to using the extracted tumor patches. Moreover, the sporadic cluster achieved the highest balanced accuracy (0.652±0.037), while the SDHx cluster had the highest F1 score (0.738±0.0565). Experimental studies have verified that incorporating predictions that are aware of specific regions during training, as well as utilizing supervised contrastive learning, are both essential for attaining these outcomes. © 2025 SPIE.

关键词： Support vector machines

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A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems

Research Square

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Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Department of Computer Science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted. © 2021, CC BY.

关键词： Recurrent neural networks

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A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNs)

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Scientific Reports 2023年第1期13卷 1-20页

作者： Hosseinzadeh, Mehdi Yoo, Joon Ali, Saqib Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Ahmed, Omed Hassan Rahmani, Amir Masoud Tightiz, Lilia Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam School of Computing Gachon University 1342 Seongnamdaero Seongnam 13120 South Korea Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughp

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A greedy perimeter stateless routing method based on a position prediction mechanism for flying ad hoc networks

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Journal of King Saud University - computer and Information sciences 2023年第8期35卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Ionescu-Feleaga, Liliana Ionescu, Bogdan-Stefan Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Ahmed, Omed Hassan Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Accounting and Audit Bucharest University of Economic Studies Bucharest 010374 Romania Department of Management Information System Bucharest University of Economic Studies Bucharest 010374 Romania Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si 13120 South Korea

Today, unmanned aerial vehicles (UAVs) are very popular in military and commercial applications as well as academic research. Flying ad hoc network (FANET) is a new type of ad hoc network that organizes small drones in an ad hoc form. The movement in 3D space, high mobility, frequent topological changes, restricted resources, and low density are the features of these networks. These features cause serious challenges in designing an appropriate routing approach for these networks. In this paper, a greedy perimeter stateless routing method based on a position prediction mechanism called GPSR+ is proposed for FANETs. This scheme employs a position forecast strategy to approximate the future position of UAVs and modifies the propagation period of hello messages based on this strategy to achieve better adaptability to the dynamic network. In addition, GPSR+ chooses a set of candidate UAVs using a new method called the spherical removal technique to decide on the next-hop UAVs in the routing process. Finally, the most stable UAV toward the destination will be chosen from the candidate set to act as the next-hop. The simulation results show that GPSR+ grows the packet delivery ratio in FANET and extends network longevity because it increases path stability and improves energy consumption. However, the delay in GPSR+ is high. © 2023 The Author(s)

关键词： Data transmission Drone Flying ad hoc network (FANET) Reliability Routing

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A fuzzy logic-based secure hierarchical routing scheme using firefly algorithm in Internet of Things for healthcare

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Scientific Reports 2023年第1期13卷 1-23页

The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EE

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