bluuebunny/crossref_metadata_2025 huggingface dataset:
Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis
Authors: Haldorai Anandakumar , Sivaraj Jeevanandham , Nagabushanam Munivenkatappa , Kingston Roberts Michaelraj
Published: October 2022

Because of numerous dormant application fields, wireless sensor networks (WSNs) have emerged as an important and novel area in radio and mobile computing research. These applications range from enclosed system configurations in the home and office to alfresco enlistment in an opponent’s landmass in a strategic flashpoint. Cognitive radio networks (CRNs) can be created by integrating radio link capabilities with network layer operations utilizing cognitive radios. The goal of CRN design is to optimize the general system operations to meet customer requirements at any location worldwide by much more efficiently addressing CRNs instead of simply connecting spectrum utilization. When compared to conventional radio networks, CRNs are more versatile and susceptible to wireless connections. Recent advancements in wireless communication have resulted in increasing spectrum scarcity. As a modern innovation, cognitive radio aims to tackle this challenge by proactively utilizing the spectrum. Because cognitive radio (CR) technology gives assailants additional possibilities than a normal wireless network, privacy in a CRN becomes a difficult challenge. We concentrate on examining the surveillance system at a societal level, in which both defense and monitoring are critical components in assuring the channel’s privacy. The current state of investigation into spectrum sensing and potential risks in cognitive radios is reviewed in this study.
Buffer-Aided Cooperative Millimeter Waves for IOT Networks
Authors: Alkhawatrah Mohammad
Published: October 2022

Millimeter-wave (mm-Wave) communications is a promising technology for the next wireless generations and applications such as IoT networks because of its massive bandwidth which increases the system capacity. However, mm-wave is susceptible to huge path loss and obstacle blocking, and this is largely narrowing the coverage of the mm-wave signals. Therefore, several techniques are used to enhance mm-wave performance such as multiple-input multiple-output (MIMO) and cooperative solutions such as relays. For instance, traditional relaying techniques (without buffers) are utilized to mitigate mm-wave path loss and blocking, as it provides an alternative path that could have lower path loss and it could help in avoiding obstacles. Nonetheless, traditional relaying techniques are outperformed by buffer-aided relaying techniques which achieve higher throughput and lower outage in the microwave frequencies. In this paper, we propose utilizing buffer-aided relays with a finite buffer-size instead of conventional relays in mm-wave networks motivated by the positive impact of the buffer-aided relay in lower frequency bands (microwave band). In the simulations, all sources, relays, blockages, and users are distributed in the network as a Poisson point process with various densities. Simulation experiments show that the proposed relay-assisted mm-wave network with buffering capabilities outperforms the conventional relay in both throughput and coverage and achieves about 3 dB gain. This superiority of the proposed solution holds in different scenarios such as increasing the ratio of the blockages or decreasing the ratio of relays. In particular, employing buffer-aided relays has almost the same effect of doubling the number of conventional relays, which is cost and spatial effective. Furthermore, in the majority of buffer-aided relays studies, the impractical infinite buffer-size is considered; however, the proposed solution utilizes a practical finite buffer-size with prioritizing transmission, and the results show that the practical solution can outperform the infinite buffer-size solution in the mm-wave networks.
GRWR Correlates with the Metabolism of Tacrolimus after Pediatric Living Donor Liver Transplantation According to Donor CYP3A5 Polymorphism
Authors: Wan Ping , Hou Yuchen , Qiu Bijun , Feng Mingxuan , Yang Taihua , Luo Yi , Xia Lei , Chen Xiaosong , Zhang Jianjun , Xue Feng , Xia Qiang
Published: October 2022

Objectives. Tacrolimus is characterized by high pharmacokinetic variability in combination with a narrow therapeutic range. However, influence of donor CYP3A5 genotype and graft‐to‐recipient body weight ratio (GRWR) on tacrolimus’ pharmacokinetics after pediatric living donor liver transplantation (LDLT) remains unclear. Methods. A total of 174 LDLT recipients (<6 y) were grouped according to donor CYP3A5 genotypes (nonexpressor (NEX) or expressor (EX)) and GRWR (<3.0% (SS, small‐size) or ≥3.0% (LS, large‐size)): SS/NEX (n = 40), SS/EX (n = 38), LS/NEX (n = 48), and LS/EX (n = 48). Pharmacokinetics of tacrolimus and clinical outcomes were analyzed. Results. The relationships between the concentration‐dose ratio and donor CYP3A5 genotypes and graft size were examined 3, 7, 14, and 30 days after the transplantation. Tacrolimus C0 levels varied greatly among groups, although recipients started with the same initial dosage. LS/EX recipients had significantly lower C0 levels in comparison with those of other groups. The use of CYP3A5‐EX‐grafts and a greater GRWR both resulted in significantly higher TAC dose requirements and lower C/D ratios. However, the significance of GRWR no longer exists 3 months after transplantation. The multivariate generalized linear mixed model analysis showed that donor CYP3A5 genotypes (F = 11.876; P = 0.01) and GRWR (F = 4.631; P = 0.033) were independent impact factors for C/D ratios 3, 7, 14, and 30 days after transplantation. Donor CYP3A5‐EX genotype was associated with significantly increasing risks of infectious complications and significantly lower Cylex ATP values. However, no significant difference was observed in acute rejections among 4 groups. Conclusions. Monitoring of C0 levels alone is not reliable to guide tacrolimus administration. Donor CYP3A5 and GRWR both significantly affect tacrolimus pharmacokinetics after pediatric LDLT. The use of Cylex ATP tests would be helpful to avoid overimmunosuppression.
IL‐17A Promotes the Migration, Invasion and the EMT Process of Lung Cancer Accompanied by NLRP3 Activation
Authors: Liu Wenping , Xin Miaomiao , Li Qing , Sun Linqian , Han Xiao , Wang Jibo
Published: October 2022

Background. Lung cancer is a deadly cancer worldwide, and its pathogenesis and treatment methods require continuous research and exploration. As a representative factor of adaptive immunity, the role of interleukin‐17A (IL‐17A) in lung cancer is still unclear. The purpose of the present study was to investigate the effect of IL‐17A on the biological behaviour of lung cancer cells and the relative mechanism. Methods. The human lung adenocarcinoma A549 and H1299 cell lines were used for in vitro study. The effects of IL‐17A on cell proliferation, migration and invasion were assessed by CCK‐8 assay, wound‐healing assay, transwell invasion assay and real‐time cell analysis (RTCA). The expression levels of marker proteins in the process of epithelial‐mesenchymal transition (EMT) were detected by western blot analysis. Caspase‐1 activity and the concentration of IL‐1β after NLRP3 inflammasome activation were measured by a Caspase‐1 Activity Assay Kit and an IL‐1β ELISA kit, respectively. Results. Compared to the control group, IL‐17A treatment did not affect the proliferation of A549 and H1299 cells in vitro, but it promoted cell migration, invasion and the EMT process. IL‐17A treatment increased NLRP3 expression, caspase‐1 activity and IL‐1β level. Blockade of NLRP3 alleviated the cell migration, invasion and the EMT process induced by IL‐17A. Conclusions. In conclusion, these findings indicated that NLRP3 participates in the migration, invasion and the EMT process of IL‐17A‐stimulated lung cells in vitro.
Pyroptosis-Related Gene Signature Predicts the Prognosis of ccRCC Using TCGA and Single-Cell RNA Seq Database
Authors: Gan Ying , Zhang Zhenan , Wang Xiaofei , Li Aolin , Fan Yu , Zhang Qian
Published: October 2022

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal carcinoma, which is not sensitive to both radiotherapy and chemotherapy. The objective response rate of metastatic renal cancer to targeted drugs and immunotherapy is unsatisfactory. Pyroptosis, proven as an inflammatory form of programmed cell death, could be activated by some inflammasomes, while could create a tumor-suppressing environment by releasing inflammatory factors in the tumor. To explore indicators predicting the prognosis of ccRCC and the effect of antitumor therapy, we constructed a pyroptosis risk model containing 4 genes after 11 pyroptosis-related genes of 516 ccRCC cases in the TCGA database were scanned. Based on the risk score, 516 ccRCC cases were divided into two groups for functional enrichment analysis and immune profile to seek functional pathways and potential therapeutic targets. Besides, those results were verified in GSE29609 and single-cell transcriptomic data. The study suggests that the conducted pyroptosis model could predict the prognosis of ccRCC and reflect the immune microenvironment, which may help in immune checkpoint inhibitor treatment.
Network Traffic Prediction: Apply the Transformer to Time Series Forecasting
Authors: Kong Qian , Zhang Xu , Zhang Chongfu , Zhou Limengnan , Yu Miao , He Yutong , Chen Youxian , Miao Yu , Yuan Haijun
Published: October 2022

Along with the development of technology and social progress, the Internet is increasingly widely used in life. Mobile communication, fiber optic broadband, and other essential Internet networks have gradually become indispensable in everyday life. The task of further improving and optimizing the quality of Internet network links and improving the efficiency of Internet networks has been on the agenda. This paper proposed a deep learning-based network traffic prediction model, which can capture the characteristics of network traffic information changes by inputting past network traffic data to achieve the effect of future network traffic prediction. The model structure is flexible and variable, which improves the problems of other methods that cannot capture long time series prediction features and cannot parallelize the output. It also has apparent advantages in time complexity and model convergence speed without the evident disadvantage of time lag. Based on this network traffic prediction model, it can help Internet service providers optimize network resource allocation, improve network performance, and allow Internet data centers to provide abnormal network warnings and improve user service level agreements.
An Efficient and Lightweight Method for Human Ear Recognition Based on MobileNet
Authors: Xu Xuebin , Liu Yibiao , Cao Shuxin , Lu Longbin
Published: October 2022

In recent years, biometric recognition patterns have attracted the attention of many researchers, among which human ears, as a unique and stable biometric feature, have significant advantages in verifying personal identity. In the Internet era, a system with low computing cost and good real-time performance is more popular. Most of the existing ear recognition methods are based on a large parameter network model, which causes a large memory footprint and computational overhead. This paper proposes an efficient and lightweight human ear recognition method (ELERNet) based on MobileNet V2. Based on the MobileNet V2 model, dynamic convolution decomposition is introduced to enhance the representation ability of human ear features. Then, combined with the coordinate attention mechanism, the spatial features of human ear images are aggregated to locate the location information of the human ear features more accurately. We conducted experiments on AWE and EarVN1.0 human ear datasets. Compared with the MobileNet V2 model, the recognition accuracy of our method is significantly improved. Using less computing hardware resources, the ELERNet model achieves 83.52% and 96.10% Rank-1 (R1) recognition accuracy, respectively, which is better than other models. Finally, we provide a visual interpretation using GradCAM technology, and the results show that our method can learn specific and discriminative features in the ear images.
Chemical Composition, Antibacterial, Antifungal and Antidiabetic Activities of Ethanolic Extracts of Opuntia dillenii Fruits Collected from Morocco
Authors: Loukili EL Hassania , Bouchal Btissam , Bouhrim Mohamed , Abrigach Farid , Genva Manon , Zidi Kahina , Bnouham Mohamed , Bellaoui Mohammed , Hammouti Belkheir , Addi Mohamed , Ramdani Mohammed , Fauconnier Marie-Laure
Published: October 2022

Opuntia dillenii (Ker Gawl.) Haw. belongs to the Cactaceae family and is native to the arid and semi-arid regions of Mexico and the southern United States. O. dillenii are now used as medicinal plants in various countries. In this study, we investigated the chemical composition of ethanolic extracts obtained from seeds, juice, and peel of O. dillenii fruits collected from Morocco, and we evaluated their antibacterial, antifungal, and antidiabetic activities. Phytochemical screening revealed high quantities of polyphenols (193.73 ± 81.44 to 341.12 ± 78.90 gallic acid eq [g/100 g dry weight]) in the extracts. The major phenolic compounds determined by HPLC were gallic acid, vanillic acid, and syringic acid. Regarding flavonoids, quercetin 3-O-β-D-glucoside and kaempferol were the predominant molecules. Juice extracts showed weak to moderate antibacterial activity against the bacteria species Listeria monocytogenes, Escherichia coli, and Salmonella braenderup. All tested extracts displayed a significant inhibitory effect on α-glucosidase and α-amylase activities in vitro, with the peel extracts showing the greatest inhibitory effects. Together, these findings suggest that O. dillenii fruits are a promising source for the isolation of novel compounds with antibacterial or antidiabetic activities. For the most abundant phytochemicals identified in O. dillenii peel ethanolic extract, molecular docking simulations against human pancreatic α-amylase enzyme were performed. These indicated the presence of bioactive compounds in the extract with a better potential to decrease the enzyme activity than the commercial drug acarbose.
Comprehensive Multiomic Analysis Identified TUBA1C as a Potential Prognostic Biological Marker of Immune-Related Therapy in Pan-Cancer
Authors: Zou Yiming , Wang Guoqing , Fan Min
Published: October 2022

TUBA1C is correlated with an unfavourable prognosis and the infiltration of immune cells in several cancers. However, its function as a significant biomarker for the prognosis of immunotherapy in pan-cancer remains unclear. This study aims at assessing the role of TUBA1C in pan-cancer at multiple levels, including mutations, gene expression, methylation, m6A methylation, and immune cell infiltration levels. Data retrieved from major public databases, such as TCGA, GEO, GTEx, GSCA, CancerSEA, HPA, and RNAactDrugs, revealed that TUBA1C expression was high in 33 cancer types. Survival analysis revealed that TUBA1C was a poor prognostic factor for 12 tumour types, and mutations, CNVs, and methylation affected the prognosis of some cancer types. Furthermore, TUBA1C was found to be related to immune-related genes, immune cell infiltration, and the immune microenvironment. In addition, the sensitivity of 10 anticancer drugs was associated with high TUBA1C expression. Therefore, TUBA1C may serve as a viable prognostic biomarker for immunotherapy of pan-cancer.
Using Rooftop Solar Heating to Supply Part of a High-Rise Residential Building Heat in the Cold Climate of Iran: One-Year Dynamic Analysis
Authors: Pishkar Iman
Published: October 2022

Higher energy consumption, especially for heating, in high-rise buildings than conventional buildings, necessitates partially supplying thermal energy by solar water heaters (SWHs). Considering the very high solar radiation potential in Iran, this study used roof solar collectors to partially supply the heat required for domestic hot water (DHW) and heat the indoor space and a swimming pool in a 48-unit building in a cold climate (Shahrekord, Iran). Climatic data were extracted from Meteonorm 7.3, and technical, energy, environmental, and economic analyses were performed with the help of TSOL 2018 R(1). The one-year dynamic analysis was performed, considering all possible losses, and an auxiliary gas boiler (AGB) was also used. According to the results, considering the available roof area, 8.7% of the total required heat equivalent to 82814 kWh is supplied by SWHs with a unit cost of $0.022/kWh. The AGB supplied 867259 kWh/year, preventing CO2 emissions by 25.5 tons. This is the first study in this regard in Iran, and the authors hope that the results can be employed as a guideline for decision makers to extend the use of SWHs in Iran.
Design and implementation monitoring robotic system based on you only look once model using deep learning technique
Authors: Al-Tameemi Maad Issa , Hasan Ammar A. , Oleiwi Bashra Kadhim
Published: October 2022

The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.
Information-gathering dialog system using acoustic features and user’s motivation
Authors: Togai Ryota , Tsunakawa Takashi , Nishida Masafumi , Nishimura Masafumi
Published: October 2022

Recently, as society continues to age, automation of watching elderly people who live apart from their families has been gradually expected. However, we must prevent them from losing their purpose in life, which declines due to lack of communication. Thus, a chat dialog system has attracted widespread attention as a method that achieves both problems: keeping their purpose in life and watching their daily lives. Unlike a task-oriented dialog system, a chat dialog system has explicitly no task to accomplish and makes a conversation to continue communication with the users. Keeping a conversation is essential for elderly people who are mostly unfamiliar with digital devices. Moreover, conversing daily on the chat dialog system provides the opportunity to collect information for their care. This study realizes an information-gathering dialog system, a chat dialog system that collects healthcare information of elderly people. Furthermore, we use the nonverbal acoustic features from their speech, since automatic speech recognition is not necessarily accurate in current systems. This paper illustrates the effectiveness of two important elements, topic change for keeping the talking user motivated with the dialog system and motivation estimation, for attaining an information-gathering dialog system using nonverbal acoustic features.
A convolutional neural network framework for classifying inappropriate online video contents
Authors: Tanantong Tanatorn , Yongwattana Patcharajak
Published: October 2022

In the digital world, the Internet and online media especially video media are convenient and easy to access. It leads to problems of inappropriate content media consumption among children and youths. However, measures or methods to control the inappropriate content for children and young people are still a challenge for management. In this research, an automated model was developed and presented to classify the content on online video media using a deep learning technique namely convolution neural networks (CNN). For data collection and preparation, the researchers collected video clips from movies and television (TV) series from websites that distribute the clips online. It consists of different types of content: i) sexually inappropriate content; ii) violently inappropriate content; and iii) general content. The collected video clip data was then extracted into frames and then used for developing the automatically-content-classifying model with algorithm CNN, analyzing and comparing the result of CNN model performance. For enhancing the model performance, a transfer learning approach and different regularization techniques were adopted in order to find the most suitable method to create high-performance modeling to classify content in video clips, movies and TV series published online.
Vehicle make and model recognition using mixed sample data augmentation techniques
Authors: Anwar Talha , Zakir Seemab
Published: October 2022

Vehicle identification based on make and model is an integral part of an intelligent transport system that helps traffic monitoring and crime control. Much research has been performed in this regard, but most of them used manual feature extraction or ensemble convolution neural networks that result in increased execution time during inference. This paper compared three deep learning models and utilized different augmentation techniques to achieve state-of-the-art performance without ensembling or fusing the models. Experimentations are made without any augmentation, with standard augmentation, and by mixed sample data augmentation techniques. Gradient accumulation and stochastic weighted averaging with mixed precision are used to have a large batch size that helped to reduce training time. The dataset comprised 48 vehicles’ models running on the road of Pakistan. The highest accuracy and F1 score of 97% and 95% using the FMix augmentation technique with EfficientNetV2-S architecture gave the confidence that the proposed solution can be implemented in production. 
Multimedia information retrieval using artificial neural network
Authors: Mahmood Maha , AL-kubaisy Wijdan Jaber , Al-Khateeb Belal
Published: October 2022

The importance of the multimedia information retrieval (MIR) is highlighted by the extensive amount of the information on the internet. Image, audio, video, and text are all examples of the characteristics of the raw multimedia data. It is greatly challenging to represent a concept of human perception and how the machine-level language can grasp it (semantic gap of MIR). However, this paper aims to improve the information retrieval model that retrieves data from multimedia. This can be implemented by leveraging the use of variety of algorithms that go through training and testing to extract the model. One of these algorithms extracts text information based on the query language's nature as the vector space model (VSM) and the latent semantic index (LSI) were used. The other technique uses curvelet decomposition and statistic parameters like mean, standard deviation, and signal energy to recover these properties. Additionally, a discrete wavelet transforms (DWT) and signal characteristics-based method is used to retrieve audio signals. Finally, the neural network learning is modeled and trained on a collection of different multimedia images. The learned features have been utilized for presenting a highly sufficient system of multimedia retrieval which operates for a large set of multi-modal datasets. 
Fuzzy C-means clustering on rainfall flow optimization technique for medical data
Authors: Mabel Rani Antony Jaya , Srivenkateswaran C. , Rajasekar M. , Arun M.
Published: October 2022

Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clustering by hybrid fuzzy C-means (FCM) clustering on rainfall flow optimization technique (RFFO), which is the normal flow and behavior of rainfall flow from one position to another position. FCM clustering algorithm is used to cluster the given medical data and RFFO is used to produce optimum clustering centroid. Finally, the clustering performance is also measured for the proposed FCM clustering on RFFO technique with the help of accuracy, random coefficient, and Jaccard coefficient for medical data set and find the risk factor of a heart attack.
Intelligent system for Islamic prayer (salat) posture monitoring
Authors: Rahman Md Mozasser , Ahmed Alharazi Rayan Abbas , Zainal Badri Muhammad Khairul Imban b
Published: October 2022

This paper introduced an Intelligent Salat Monitoring and Training System based on machine vision and image processing. In Islam, prayer (i.e. salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean distance, template matching and grey-level correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat.
Karawitans’ musician brain adaptation: standardized low-resolution electromagnetic tomography study
Authors: Wardani Indra K. , Sittiprapaporn Phakkharawat , Djohan Djohan , Tyasinestu Fortunata
Published: October 2022

The rapid advancement of music studies has resulted in a plethora of multidisciplinary participants. Rather than distinguishing between musicians and non-musicians’ brain activity, the current study indicated differences in brain activity while musicians listened to music based on their musical experience. In Go/NoGo response task reaction times, it showed that effects between treatments and visits were different across periods of cognitive function tests. The cognitive function at post-listening assessment out-performed the pre-listening in terms of reaction times (531.94 (±24.70) msec for post-listening assessment; and 557.13 (±37.15) msec for pre-listening assessment. The results of using electroencephalography (EEG) recording in an experimental manner with Karawitan musicians (N=20) revealed that listening to unknown cultural music, Mozart's Piano Sonata in C Major, and western music resulted in increased brain activity. Furthermore, while Karawitan musicians were listening to Mozart's Piano Sonata in C Major, the major brain activity occurred in the frontal lobe. This outcome will elicit additional consideration of music's integration, such as neuroscience of music.
A fuzzy observer synthesis to state and fault estimation for Takagi-Sugeno implicit systems
Authors: Aitdaraou Khaoula , Essabre Mohamed , El Assoudi Abdellatif , El Yaagoubi El Hassane
Published: October 2022

The present paper inquire a fuzzy observer design issue for continuous time Takagi-Sugeno aiming at estimating both of fault and state in case of unmeasurable premise variables. Thanks to singular value decomposition (SVD) approach, the fuzzy observer is synthesized in explicit form. The developed method is based on augmented structure that assemble state and actuator fault, sensor fault as well as their sequential derivatives is enjoined to institute the destined observer. The Lyapunov function is analyzed to assume the exponential stability of the studied observer, furthermore convergence conditions are expressed as linear matrix inequalities (LMIs) form. The applicability of the nominated theoritical result is elucidated and confiremed through numerical simulation using an example of an implicit fuzzy model.
Machine learning and artificial intelligence models development in rainfall-induced landslide prediction
Authors: Harsa Hastuadi , Hidayat Anistia Malinda , Mulsandi Adi , Suprihadi Bambang , Kurniawan Roni , Habibie Muhammad Najib , Hutapea Thahir Daniel , Swarinoto Yunus S. , Syahputra Makmur Erwin Eka , Fitria Welly , Sri Sudewi Rahayu Sapta , Praja Alfan Sukmana
Published: October 2022

In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.
A hybrid composite features based sentence level sentiment analyzer
Authors: Maree Mohammed , Eleyat Mujahed , Rabayah Shatha , Belkhatir Mohammed
Published: October 2022

Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely Support Vector Machines, Naive Bayes, Logistic Regression and Random Forest. We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations. Additionally, we measure the impact of incorporating lexical semantic knowledge captured by WordNet on expanding original words in sentences. Findings demonstrate that the utilizing different NLP pipelines and semantic relationships impacts the quality of the sentiment analyzers. In particular, results indicate that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy results. With this coupling, the accuracy of the support vector machine (SVM) classifier has improved to 90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three other classifiers. 
An adaptive metaheuristic approach for risk-budgeted portfolio optimization
Authors: Kumar Gandikota Naga Sunil , Hasan Mohd Hilmi , Jaafar Jafreezal
Published: October 2022

An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame via  differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.
Hybridizing genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources
Authors: Abed Munther H. , Mohmad Kahar Mohd Nizam
Published: October 2022

This paper focuses on solving unrelated parallel machine scheduling with resource constraints (UPMR). There are j jobs, and each job needs to be processed on one of the machines aim at minimizing the makespan. Besides the dependence of the machine, the processing time of any job depends on the usage of a rare renewable resource. A certain number of those resources Rmax can be disseminated to jobs for the purpose of processing them at any time, and each job j needs units of resources (rjm) when processing in machine m. When more resources are assigned to a job, the job processing time minimizes. However, the number of resources available is limited, and this makes the problem difficult to solve for a good quality solution. Genetic algorithm shows promising results in solving UPMR. However, genetic algorithm suffers from premature convergence, which could hinder the resulting quality. Therefore, the work hybridizes genetic algorithm with a single-based metaheuristics (SBH) to handle the premature convergence in the genetic algorithm with the aim to escape from the local optima and improve the solution quality further. The single-based metaheuristics replaces the mutation in the genetic algorithm. The evaluation of the algorithm performance was conducted through extensive experiments. 
A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data
Authors: Tinh Pham Doan , Hoang Bui Huy , Cuong Nguyen Duc
Published: October 2022

The recent trend in location-based services has led to a proliferation of studies in indoor positioning technology. Wi-Fi received signal strength indicator (RSSI) Fingerprinting and pedestrian dead reckoning (PDR) are the two best representatives from both approaches. This research proposed a genetic algorithm to combine Wi-Fi Fingerprinting and PDR. By taking advantage of PDR and genetic algorithm, we only need to collect a limited number of points for the fingerprint dataset with known coordinates, then target trajectories' position can be estimated with high accuracy. Results from our experiments and simulations have shown that even in the scenario of noisy inertial measurement unit (IMU) sensors data, using RSSI measurements and the coordinate of 8 points, our proposed method was able to achieve 1.589 meters of average distance error which is 34.4 percent lower than the conventional Fingerprinting method.
Impedance characteristic of the human arm during passive movements
Authors: Rahman Md Mozasser , Ikeura Ryojun
Published: October 2022

This paper describes the impedance characteristics of the human arm during passive movement. The arm was moved in the desired trajectory. The motion was actuated by a 1-degree-of-freedom robot system. Trajectories used in the experiment were minimum jerk (the rate of change of acceleration) trajectories, which were found during a human and human cooperative task and optimum for muscle movement. As the muscle is mechanically analogous to a spring-damper system, a second-order equation was considered as the model for arm dynamics. In the model, inertia, stiffness, and damping factor were considered. The impedance parameters were estimated from the position and torque data obtained from the experiment and based on the “Estimation of Parametric Model”. It was found that the inertia is almost constant over the operational time. The damping factor and stiffness were high at the starting position and became near zero after 0.4 seconds.
Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array
Authors: Al-Musawi Wisal Adnan , Ali Al-Ibadi Mohammed Abd , Wali Wasan A.
Published: October 2022

Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.
Investigating the roles of demographic profiles on usability assessment: case study on CiteGuru application
Authors: Mohamad Rosman Mohamad Rahimi , Nik Rosli Nik Nur Izzati , Mohd Shukry Amira Idayu , Razlan Noor Masliana , Alimin Noor Azreen
Published: October 2022

Postgraduate education is the highest education structure in all countries across global. In Malaysia, postgraduate education is usually pursued by several candidates, such as academicians, professionals, and motivated junior learners. As such, one of the requirements of postgraduate education is to have a publication as a mandatory graduation requirement. Thus, one of the challenging issues in the publication is to ensure the quality of the references and citation. However, the lack of mobile applications available that focus on citation management caused several problems such as succumbing to predatory journals, poor citation work, desk rejection, and inaccurate facts. Therefore, the purpose of this study is twofold: first, to investigate the roles and usability assessment of CiteGuru application as a mobile solution for improving learners’ skills, ability, and knowledge on referencing and second, to investigate the roles of demographic profiles on the usability perception among the respondents. A quantitative research methodology using a survey was adopted with 23 expert panels selected based on three distinct positions–academic, industries, and librarians. Data were analysed using statistical package for social science version 26. The result indicates that i) the panel rate the usability of the application as acceptable and ii) demographic profiles (sector, education, and gender) prove insignificant on the usability assessment.
Multiple face mask wearer detection based on YOLOv3 approach
Authors: Ge Cheng Xiao , As’ari Muhammad Amir , Jasmin Sufri Nur Anis
Published: October 2022

The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. 
Efficient lightweight residual network for real-time road semantic segmentation
Authors: Kherraki Amine , Maqbool Muaz , Ouazzani Rajae El
Published: October 2022

Intelligent transportation system (ITS) is currently one of the most discussed topics in scientific research. Actually, ITS offers advanced monitoring systems that include vehicle counting, pedestrian detection. Lately, convolutional neural networks (CNNs) are extensively used in computer vision tasks, including segmentation, classification, and detection. In fact, image semantic segmentation is a critical issue in computer vision applications. For example, self-driving vehicles require high accuracy with lower parameter requirements to segment the road scene objects in real-time. However, most related work focus on one side, accuracy or parameter requirements, which make CNN models difficult to use in real-time applications. In order to resolve this issue, we propose the efficient lightweight residual network (ELRNet), a novel and ELRNet, which is an asymmetrical encoder-decoder architecture. Indeed, in this network, we compare four varieties of the proposed factorized block, and three loss functions to get the best combination. In addition, the proposed model is trained from scratch using only 0.61M parameters. All experiments are evaluated on the popular public the cambridge-driving labeled video database (CamVid) road scene dataset and reached results show that ELRNet can achieve better performance in terms of parameters requirements and precision compared to related work.
Survey of indoor tracking systems using augmented reality
Authors: Shewail Ashraf Saad , Elsayed Neven Abdelaziz , Zayed Hala Helmy
Published: October 2022

Augmented reality overlays virtual content on the physical world, displaying location-based information more efficiently. Tracking is a trace detail of the location recorded, either by taking a reading based on a set time interval, set distance, and change in direction by more than a certain angle, or a combination of these. Tracking is divided into two types, outdoor tracking, and indoor tracking. Augmented reality added value and information to tracking applications, whether indoor or outdoor. Recently, outdoor tracking by the global positioning system (GPS) became an essential component in navigation applications. However, indoor tracking is still challenging in the augmented reality field. Most augmented reality indoor tracking use optical and sensor-based tracking, such as markers, beacons, Raspberry Pi, and route planner modules for the building. With the technology enhancement, indoor tracking started to rely on WiFi, Bluetooth, geographic information system, radiofrequency identification, and sensor chip technologies. Our paper describes the recent augmented reality tracking techniques from 2011 to 2021. The paper compares image detection algorithms with various communication technologies, disscusing the advantages and the limitations of each technology.
A new approach to achieve the users’ habitual opportunities on social media
Authors: Lubis Arif Ridho , Nasution Mahyuddin K. M. , Sitompul Opim Salim , Zamzami Elviawaty Muisa
Published: October 2022

The data generated from social media is very large, while the use of data from social media has not been fully utilized to become new knowledge. One of the things that can become new knowledge is user habits on social media. Searching for user habits on Twitter by using user tweets can be done by using modeling, the use of modeling lies when the data has been preprocessed, and the ranking will then be checked in the dictionary, this is where the role of the model is carried out to get a chance that the words that have been ranked will perform check the word in the dictionary. The benefit of the model in general is to get an understanding of the mechanism in the problem so that it can predict events that will arise from a phenomenon which in this case is user habits. So that with the availability of this model, it can be a model in getting opportunities for user habits on Twitter social media.
Autonomous driving system using proximal policy optimization in deep reinforcement learning
Authors: Yazid Imam Noerhenda , Rachmawati Ema
Published: October 2022

Autonomous driving is one solution that can minimize and even prevent accidents. In autonomous driving, the vehicle must know the surrounding environment and move under the provisions and situations. We build an autonomous driving system using proximal policy optimization (PPO) in deep reinforcement learning, with PPO acting as an instinct for the agent to choose an action. The instinct will be updated continuously until the agent reaches the destination from the initial point. We use five sensory inputs for the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect the initial point, and reach the destination point. We evaluated our proposed autonomous driving system in a simulation environment with several branching tracks, reflecting a real-world setting. For our driving simulation purpose in this research, we use the Unity3D engine to construct the dataset (in the form of a road track) and the agent model (in the form of a car). Our experimental results firmly indicate our agent can successfully control a vehicle to navigate to the destination point. 