Article
Computer Science, Information Systems
Aria Bisma Wahyutama, Mintae Hwang
Summary: This study develops a smart trash bin using a webcam and Raspberry Pi with YOLO real-time object detection to separate and collect recyclables, and guides users to throw trash in the correct compartment. The system also includes ultrasonic sensors and GPS for capacity monitoring and location tracking, and uploads information to Firebase database via Wi-Fi module for display in a mobile application.
Article
Engineering, Biomedical
Chi Qin, Xiaofei Zhu, Lin Ye, Li Peng, Long Li, Jue Wang, Jin Ma, Tian Liu
Summary: This study extracts multiple time scale brain features of fMRI for objective detection of autism, achieving better results compared with established models. The research provides a new framework and research ideas for subsequent fMRI analysis.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Engineering, Environmental
Nattapon Leeabai, Chinnathan Areeprasert, Chanoknunt Khaobang, Niti Viriyapanitchakij, Bundit Bussa, Dilixiati Dilinazi, Fumitake Takahashi
Summary: The study found that trash bins with the least preferred colors had the highest waste separation efficiencies. Color preference towards trash bins had no significant impact on waste collection, while lower color preference contributed to lower noticeability of trash bins. Emphasizing the location of less noticeable trash bins might help participants practice correct waste disposal.
Article
Engineering, Environmental
Jia Yang, Fengming Tao, Yanni Zhong
Summary: This study focuses on the chance-constrained collection and transportation problem for sorted waste with multiple separated compartments EVs, proposing a Chance-Constrained Multi-Compartment Electric Vehicle Routing Problem (CCMCEVRP) and verifying the effectiveness of the proposed algorithms through extensive numerical experiments. The research shows that using EVs can save costs compared to fuel vehicles, and the saving rate increases with the number of compartments.
WASTE MANAGEMENT & RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Manuel Lopes, Tania R. P. Ramos
Summary: A combination of better sensing, prediction, and planning can enhance the efficiency of completing spatially distributed tasks. The compromise between travel cost and quality of service is crucial when an agent needs to serve different locations regularly. This complex problem arises in waste collection, maintenance, and surveillance and cannot be optimally solved due to uncertainty and combinatorial decision making. We propose an efficient algorithm that considers scheduling, route planning, and the impact on travel efficiency and quality of service for sensor placement in this work, demonstrating examples in a waste collection environment.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Agricultural Engineering
Xuan Zhang, Dachao Ma, Jiahao Lv, Qingge Feng, Zhengwu Liang, Hongcheng Chen, Jinghang Feng
Summary: Using mature compost as a bulking agent for food waste rapid composting can reduce CO2 and N2O emissions, but it results in higher N2O emissions compared to rice bran. The abundance of denitrifying bacteria and the expression of narG and nirK genes were higher in mature compost, contributing to N2O emissions.
BIORESOURCE TECHNOLOGY
(2022)
Article
Management
Claudio Arbib, Fabrizio Marinelli, Andrea Pizzuti
Summary: In this study, an orthogonal non-oriented two-dimensional bin packing problem with due-dates for items is addressed, aiming to minimize the number of bins and the maximum lateness of items. A sequential value correction heuristic is proposed that outperforms two benchmark algorithms, and the benchmark dataset for this problem is extended with new and larger industrial instances. Insights into the structure of Pareto-optimal sets in the considered classes of instances are also provided.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Information Systems
Chen Dong, Hao Wu, Qingyuan Li
Summary: As vehicles become more intelligent and connected, the increase in the number of ECUs and communication interfaces raises network security risks. The lack of security mechanisms for the CAN bus network, the main bus in the vehicle, leaves it vulnerable to cyber attacks. To address this, we propose a CAN bus intrusion detection system based on multiple observation HMM for in-vehicle networks. Our algorithm builds HMMs based on normal CAN bus traffic and calculates the existence probability of frames to detect abnormalities. Experimental results demonstrate better detection performance compared to other methods in four attack scenarios.
Article
Green & Sustainable Science & Technology
Josef Matusinec, Dusan Hrabec, Radovan Somplak, Vlastimir Nevrly, Yury Redutskiy
Summary: One of the current trends in waste management and circular economy is the recycling of fats and cooking oils, which can contribute to energy supply and material recovery. A mathematical model is proposed for optimizing the location of fat waste bins and containers, aiming to minimize the total number of collection points and infrastructure cost.
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY
(2022)
Article
Chemistry, Analytical
Rui Ge, Hanjie Dai, Shumin Zhang, Jie Wei, Tianhui Jiao, Qingmin Chen, Quansheng Chen, Xiaomei Chen
Summary: In this study, a photoelectrochemical (PEC) biosensor combined with recombinase polymerase amplification (RPA) technology (RPA-PEC) was proposed for rapid detection of multiple foodborne pathogens. The RPA was initiated on homemade three-dimensional screen-printed paper-based electrodes, forming a lab-on-paper platform. Using the DNA-PEC signaler, photocurrents were achieved after only 20 minutes of RPA, with superior detection performance compared to conventional agarose gel electrophoresis.
ANALYTICAL CHEMISTRY
(2023)
Article
Computer Science, Information Systems
Ouzhu Han, Tao Ding, Chenggang Mu, Wenhao Jia, Zhoujun Ma
Summary: Data centers have become increasingly important in demand response programs due to their considerable capability. To maximize welfare and relieve transmission pressure, a scheduling model is proposed for the optimization of data center operators and system operators. A distributed scheduling algorithm is designed for privacy protection and autonomy. Simulation results show that the algorithm effectively maximizes social welfare while protecting data privacy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Shimei Ma, Xiaolong Li, Mengyao Xiao, Bin Ma, Yao Zhao
Summary: A fast expansion-bins-determination method for multiple histograms modification (MHM) is proposed in this paper, which considers a general form of the problem with differentiable objective function and real variables, making use of advanced analysis tools such as Lagrange multiplier, in order to quickly determine the optimal expansion bins and improve the practicality of MHM.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Mathematics
Ming Wan, Ting Qu, Manna Huang, Xiaohua Qiu, George Q. Huang, Jinfu Zhu, Junrong Chen
Summary: This study proposes a cloud-edge-terminal-based synchronization decision-making and control system for MSWCT, with smart terminals and edge computing devices deployed at key nodes for real-time data collection and analysis, as well as a three-level and two-stage synchronization decision-making mechanism established to enable synchronization operation between various subsystems.
Article
Computer Science, Information Systems
Xiang Yu, Shui-Hua Wang, Yu-Dong Zhang
Summary: To facilitate faster breast cancer detection, a novel and efficient patch-based breast mass detection system was developed. The system consists of three modules: pre-processing, multiple-level breast tissue segmentation, and final breast mass detection. An improved Deeplabv3+ model is used for pre-processing and a multiple-level thresholding segmentation method is proposed for breast mass segmentation. Deep learning models are trained to classify image patches into breast mass or background, reducing the false positive rate. The proposed method achieves comparable performance with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Nitin Goyal, Ashok Kumar, Renu Popli, Lalit Kumar Awasthi, Nonita Sharma, Gaurav Sharma
Summary: In order to efficiently gather data in resource constrained sparse Underwater Wireless Sensor Networks (UWSNs), a priority-based data gathering scheme using multiple Mobile Sinks (MSs) is proposed for detecting pipeline leakage under the water. Each MS can move in both directions under the water, and when a Cluster Head receives critical data, it sends an emergency notification to the nearest MS via other Cluster Heads.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Md Abdul Momin, Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Mohamad Sofian Abu Talip
Summary: Efficient vehicle detection is crucial in intelligent transportation systems. This study aims to improve the conventional CNN model to achieve real-time detection on low-cost embedded hardware. A lightweight CNN model based on YOLOv4 Tiny is proposed, which adds an additional scale feature map to enhance detection accuracy. Experimental results show that the proposed model outperforms the conventional YOLOv4 Tiny and previous works in terms of mean average precision (mAP).
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Review
Computer Science, Information Systems
Ahmad A. M. Abushariah, Hua-Nong Ting, Mumtaz Begum Peer Mustafa, Anis Salwa Mohd Khairuddin, Mohammad A. M. Abushariah, Tien-Ping Tan
Summary: In this technological era, smart and intelligent systems with artificial intelligence techniques have impacted various aspects of daily life. Speech communication and interaction between humans and machines have become increasingly important, and numerous technologies, such as Automatic Speech Recognition (ASR), utilize speech as a means of interaction. However, research on ASR systems combining multiple languages is limited. This paper aims to provide a comprehensive background and fundamentals of bilingual ASR, discussing related works, research taxonomy, and open challenges. The study suggests that bilingual ASR with a deep learning approach is highly demanded and can open new research opportunities for language combinations.
Article
Mathematics
Ali Najem Alkawaz, Jeevan Kanesan, Anis Salwa Mohd Khairuddin, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Hussien, Maughal Ahmed Ali Baig, N. Ameer Ahammad
Summary: This work proposes an improved method of training neural networks based on optimal control theory. By adapting the learning rate, the proposed algorithm overcomes the local minima problem of backpropagation and achieves better accuracy and shorter training time compared to the conventional method. The effectiveness of the algorithm is verified on various logic gate models and a full adder model.
Article
Mechanics
Ahmed Elhanafy, Yasser Abuouf, Samir Elsagheer, Shinichi Ookawara, Mahmoud Ahmed
Summary: Diagnostic technology based on magnetic fields is commonly used in medicine, but exposure to strong electromagnetic fields has adverse effects on patients. This study investigates the effects of external uniform magnetic fields on blood flow in healthy and diseased cases, and determines safe values for field strength. A three-dimensional non-Newtonian flow model is developed to investigate the effects of the magnetic field on shear rate and hematocrit. Numerical simulations are conducted at different field strengths and orientations, and results demonstrate the dominant effect of the magnetic field in the Y-direction.
Article
Clinical Neurology
Hani Chanbour, Jeffrey W. Chen, Lakshmi S. Gangavarapu, Gabriel A. Bendfeldt, Matthew E. LaBarge, Mahmoud Ahmed, Steven G. Roth, Silky Chotai, Leo Y. Luo, Amir M. Abtahi, Byron F. Stephens, Scott L. Zuckerman
Summary: This is a retrospective case-control study that aimed to identify risk factors associated with unplanned readmission after metastatic spine surgery and determine its impact on long-term outcomes. The results showed that postoperative complications were associated with unplanned readmission, and 3-month unplanned readmission was associated with a shorter time to local recurrence and decreased overall survival.
Editorial Material
Pharmacology & Pharmacy
Mahmoud Ahmed, Deok Ryong Kim
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Genetics & Heredity
Mahmoud Ahmed, Hyun Joon Kim, Deok Ryong Kim
Summary: The human genome project ignited scientific interest in an ambitious goal, leading to significant discoveries and a new era of research. The project's impact extended beyond its results, as novel technologies and analysis methods emerged, making high-throughput datasets more accessible to labs and serving as a model for future collaborations. To effectively utilize these datasets for research and public benefit, re-analysis, curation, and integration with other data forms are crucial.
FRONTIERS IN GENETICS
(2023)
Article
Medicine, General & Internal
Mahmoud Ahmed, Yamini Krishna, Petya Popova, Rose Herbert, Gediminas Sidaras, Anshoo Choudhary, Stephen B. B. Kaye
Summary: This study examined the impact of low-dose propofol sedation on patient reported outcome measures (PROMS) during cataract surgery. The study found that the use of low-dose propofol was associated with reduced pain, needle recall, and blood pressure. The results suggest that propofol can be an effective sedation method for cataract surgery.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Medicine, General & Internal
Alfredo Borgia, Vito Romano, Davide Romano, Luca Pagano, Aldo Vagge, Giuseppe Giannaccare, Mahmoud Ahmed, Kunal Gadhvi, Nardine Menassa, Mohammad Ahmad, Stephen Kaye, Giulia Coco
Summary: Astigmatism is a common issue after keratoplasty, and it is important to accurately identify and characterize its type, amount, and direction. Various techniques, including corneal tomography and topo-aberrometry, can be used for evaluation, but alternative methods are available if these instruments are not accessible. This article describes both low-tech and high-tech approaches for detecting post-keratoplasty astigmatism, as well as the management strategies involving suture manipulation.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Genetics & Heredity
Dalal Al-Sharshani, Dinesh Velayutham, Muthanna Samara, Reham Gazal, Ayman Al Haj Zen, Mohamed A. Ismail, Mahmoud Ahmed, Gheyath Nasrallah, Salma Younes, Nasser Rizk, Sara Hammuda, M. Walid Qoronfleh, Thomas Farrell, Hatem Zayed, Palli Valapila Abdulrouf, Manar AlDweik, John Paul Ben Silang, Alaa Rahhal, Rana Al-Jurf, Ahmed Mahfouz, Amar Salam, Hilal Al Rifai, Nader I. Al-Dewik
Summary: This study aimed to investigate the association between selected single nucleotide polymorphisms (SNPs) and dyslipidemia, as well as the increased susceptibility risks of cardiovascular diseases, type 2 diabetes mellitus, and non-alcoholic fatty liver disease. The results showed significant differences in genotypic frequencies of six SNPs between dyslipidemia patients and control individuals. These differences were found to be associated with gender, obesity, hypertension, and diabetes.
MOLECULAR GENETICS & GENOMIC MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Nur Athirah Zailan, Anis Salwa Mohd Khairuddin, Khairunnisa Hasikin, Mohamad Haniff Junos, Uswah Khairuddin
Summary: Garbage pollution is a growing global concern, and innovative solutions are necessary for its control. Obtaining visual information of floating garbage in rivers is crucial for the development of an efficient cleaner robot. Deep learning, which can learn high-level semantic features based on visual information, is essential for the detection and classification of different types of floating garbage. In this paper, an optimized You Only Look Once v4 Tiny model is proposed, which improves the spatial pyramid pooling, activation function, neural network, and hyperparameters to achieve better results. The proposed model shows a mean average precision of 74.89% with a size of 16.4 MB, making it the best trade-off among other models. It has promising results in terms of model size, detection time, and memory space, and is feasible to be embedded in low-cost devices.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Oncology
Trang Huyen Lai, Mahmoud Ahmed, Jin Seok Hwang, Md Entaz Bahar, Trang Minh Pham, Jinsung Yang, Wanil Kim, Rizi Firman Maulidi, Dong-Kun Lee, Dong-Hee Kim, Hyun Joon Kim, Deok Ryong Kim
Summary: Breast cancer is a common tumor type in women with a high fatality rate due to metastasis. Raf kinase inhibitory protein (RKIP) is a potential metastasis suppressor candidate, but its expression is reduced or lost in aggressive cancer variants. This study explores the role of RKIP as an anti-metastatic gene using two breast cancer cell lines with different levels of RKIP expression. The findings suggest a potential strategy to reverse the metastatic capability of breast cancer cells by chemically manipulating RKIP expression.
FRONTIERS IN ONCOLOGY
(2023)
Article
Computer Science, Information Systems
Hai Chuan Liu, Joon Huang Chuah, Anis Salwa Mohd Khairuddin, Xian Min Zhao, Xiao Dan Wang
Summary: The intelligent campus surveillance system improves safety in school by recognizing abnormal behaviors. This study explores the challenges of video-based abnormal behavior recognition on campus and proposes a novel framework that models long-range temporal video structures and uses a global sparse uniform sampling strategy. The proposed method achieves competitive results in terms of recognition accuracy compared to other peer video recognition methods.
Article
Mathematics, Interdisciplinary Applications
Amin Sharafian, Jeevan Kanesan, Anis Salwa Mohd Khairuddin, Anand Ramanathan, Alireza Sharifi, Xiaoshan Bai
Summary: This paper presents a novel approach to designing a fixed-time fractional order observer for estimating the states of the dynamic model of HIV infection. The proposed approach combines output injection terminal sliding mode and RBF neural network strategies to achieve robust and efficient estimation. The results show accurate and efficient estimation of the states of the HIV model.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Information Systems
Wen Yee Wong, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Sarah Abdul Razak, Hanee Farzana Hizaddin, Mohd Istajib Mokhtar, Muhammad Mokhzaini Azizan
Summary: A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes. This study evaluates the capability of machine learning algorithms in handling imbalanced water quality data and compares the performance of 10 algorithms. The results show that high-accuracy models are not always good in recall and sensitivity. The proposed stacked ensemble deep learning model performs well in the F1 score, achieving a balance between accuracy and completeness.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
