bluuebunny/crossref_metadata_2025 huggingface dataset:
ANALYSİS AND ASSESSMENT BASED ON THE GIS PROGRAM OF ANTHROPOGENİC LOAD İN SOME LANDSCAPE COMPLEXES OF THE LENGEBİZ RİDGE
Authors:   ANAS Institute of Geography named after Academician H.A. Aliyev, Baku, Azerbaijan, Imanova Gulsare
Published: October 2022

Purpose. GIS program-based analysis and evaluation of anthropogenic load in some landscape complexes of the Lengebiz ridge. Methodology. The study was carried out in three stages: cameral preparatory, field laboratory, and final. At the final stage, a landscape map of the area at a scale of 1:100,000 was compiled. The boundaries of landscapes and contours of anthropogenic origin within them were calculated based on the GIS program. Conclusion. Within the object of study, the following types of landscape are identified: low-mountain forests; low-mountain forest shrubs (arid-sparsely wooded and shrub complexes); low-mountain steppes; xerophytic dry steppes of upland plains; low-mountain semi-deserts; dry steppes of accumulative plains; river-valley landscapes. The low-mountain forest landscape is represented by a mixture of oak and hornbeam oak. Among anthropogenic factors, settlements, communication routes, as well as agricultural land, especially arable land, have a significant impact on landscape complexes. the total area allocated for settlements, roads, etc., structures is 76.19 hectares, or 0.83% of the area of this landscape type. However, depending on individual types of landscapes, this indicator is 0.56% -1.09%. And the total area of agricultural land on the territory of the studied object is 2102.9 hectares, which is 22.96% of the landscape type area. Depending on individual landscapes, this indicator increases from 13.21% to 31.96%. Due to the great interest in arable land, their share in landscapes ranges from 0.31% to 3.04%, and hayfields and pastures - from 11.29% to 27.24%. 442.05 or 8.99% of the territory of this type of landscape is located on slopes with a slope of more than 400. 1871 thousand hectares or 38.05% of the area were subjected to anthropogenic load. However, this indicator has different values depending on the type of landscape: 23,63%-65,92 %. The total area occupied by settlements, roads, communication lines and man-made structures of the district was 100.5 hectares or 2.04%. The total area of agricultural land (arable land, hayfields, pastures) within this landscape amounted to 1771.3 hectares (36.01%) were subjected to anthropogenic influence. Scientific novelty. For the first time, based on the GIS program, the anthropogenic load in some landscape complexes of the Lengebiz range of Azerbaijan was analyzed and evaluated.
The Role and Importance of the Prosecutor's Office in Protecting the Rights of Persons with Disabilities
Authors: Bayramov Farman J. , Golovacheva Yulia  A.
Published: October 2022

This scientific work reveals the importance of the activities of the Prosecutors Office of the Russian Federation in the protection of a socially unprotected group of persons such as the disabled. To date, an extensive domestic and international regulatory framework has been developed that regulates the protection of the rights of persons with disabilities. However, relevant offenses continue to be identified. In view of this, the employees of the Prosecutors Office of the Russian Federation are taking a number of effective measures to restore these rights, including going to court with a statement in defense of the rights, freedoms, and legitimate interests of an indefinite number of persons. Relying on its activities on the principle of publicity, the Prosecutors Office is taking the first significant steps in protecting the rights of persons with disabilities.
Temporal Variation of the Spectral Decay Parameter Kappa Detected before and after the 2016 Main Earthquakes of Central Italy
Authors: Castro Raúl R. , Spallarossa Daniele , Pacor Francesca , Colavitti Leonardo , Lanzano Giovanni , Vidales-Basurto Claudia A. , Sgobba Sara
Published: October 2022

We investigated the temporal variation of the spectral decay parameter κ before and after two main earthquakes that occurred in the central Italy region, namely the Amatrice (Mw 6.0) of 24 August 2016 and the Norcia (Mw 6.5) of 30 October 2016 earthquakes. For this analysis, we used seismograms from the central Italy dense seismic array stations, and earthquakes located at hypocenter distances r < 80 km, having magnitudes Mw 3.4–6.5. The dataset consists of 393 events recorded at 92 stations. We estimated, for both earthquake sequences, average functions κ˜(r) that describe the distance dependence of κ along the S-wave source-station paths using acceleration spectra from foreshocks, mainshock, and aftershocks. We observed that there was a regional attenuation drop within approximately two months after the Amatrice earthquake. Then, κ˜(r) tends to return toward the attenuation values observed before the occurrence of the main event, namely to the values of κ˜(r) obtained from the foreshocks, when the earthquake cycle is probably completed. We also estimated the near-source kappa (κs) using aftershocks from 24 August 2016 to 3 September 2016. The results show that the values of κs are lower than those from aftershocks located to the north near the epicenter of the Amatrice earthquake, suggesting that the tectonic stress was probably high near the rupture zone, and that there may be a likely fluid flow of crustal fluids. κ˜(r) obtained from the foreshocks of the Norcia earthquake is like that calculated with the records of the Amatrice aftershocks. Then, κ˜(r) drops to lower attenuation values during the Norcia main event and tends to increase again during the aftershocks. From the analysis of these two earthquake sequences that occurred in a short-time interval in central Italy, we conclude that the temporal variation of κ˜(r) could be a valuable indicator to monitor the earthquake cycle.
The Urban Underground Space beneath the Karst Basin of Guilin, China, Revealed by Ambient Seismic Noise Tomography
Authors: Yu Guiping , Zhang Zhi , Xu Tao , Li Xuelai , Wang Minling , Guo Xi , Xu Jiao , Hou Jue , Guo Guihong , Badal José
Published: October 2022

Detection of hidden faults and sedimentary layers in the urban subsoil is significant for the utilization of the underground space, earthquake hazard mitigation, and so forth. Guilin, located in southwest China, is well known for the development of the most typical karst landform in the world and has become an international tourist city that needs scientific planning and knowledge of the urban underground space. After collecting waveform data recorded continuously over a period of about 1.5 months by a dense array of 114 short-period seismic stations installed in and around Guilin, we adopt ambient seismic noise cross-correlation method to extract Rayleigh-wave phase velocity dispersion curves within the period range from 0.5 to 5 s and to obtain a high-resolution S-wave velocity (VS) model of the shallow crust above 9 km using surface-wave tomography. The vertical VS gradient image indicates that the sediment thickness of the Guilin Karst basin is about 1–3 km. Sedimentary layers are relatively thick between Yanshan Mountain and Haiyang Mountain, and along the Yi river valley, where karst groundwater may be abundant and used as an important option for urban water supply. Both the absolute VS velocity image and the relative VS anomaly image clearly reveal the occurrence, location, and deep extension characteristics of major faults. Longsheng–Yongfu, Nanning–Guilin, Yaoshan–Yanshan, Baishi, and Guanyang–Hengyang may be regional deep faults cutting through the upper crust at least. The evolution of the basin is mainly controlled by the steep dipping Longsheng–Yongfu and Baishi faults, and partially controlled by the gently dipping Nanning–Guilin fault in the interior of the basin. The Nanning–Guilin fault is an active and partially buried fault obliquely crossing Guilin city. Urban seismic imaging, such as the new VS tomography presented here, can play an important role in understanding tectonic and tectonic-subsidence earthquake hazards associated with these buried faults.
COVID-19 VACCINE HESITANCY AND VACCINATION STATUS OF FIRST-YEAR MEDICAL STUDENTS
Authors: KONUŞ Süleyman , SERT Berna , BAKAR Coşkun , ÇAN Gamze
Published: October 2022

Objective: This study aims to determine the prevalence of vaccine hesitancy and vaccination status in Çanakkale Onsekiz Mart University medical faculty first-year students. Methods: The population of this cross-sectional study consisted of 170 first-year students, and 81.2% of the students were reached (138 students). Microsoft Excel and SPSS programs were used for data analysis. The Chi-square test was used for the comparison of groups. For statistical significance, the p
Investigation of Women's Awareness Levels of Domestic Violence
Authors: ATAMAN Hacer , BOZKURT Adile , ÖZTÜRK AKDENİZ Ceyda
Published: October 2022

Objective: The study was conducted to examine the awareness levels of women about domestic violence.

Material and Method: The population of the descriptive study consists of women between the ages of 18-65 residing in Artvin and Osmaniye. The study was completed with 433 participants who met the research criteria. The data were collected using the “Sociodemographic Data Form” and the “Domestic Violence Awareness Scale”.

Findings: 37,9% of the participants were exposed to family violence. Women reported verbal and emotional violence (21%) as the most common type of violence. 17,8% of women have been exposed to violence by more than one person. The total mean score of Domestic Violence Awareness Scale is 24,15±0,22. A statistically significant difference was found between the age, employment status, education level, marital status, number of children and type of marriage of the woman and her husband, and the total mean score of Domestic Violence Awareness Scale. A statistically significant difference was found between the type of violence and the total mean score of Domestic Violence Awareness Scale. According to this, the total mean score of Domestic Violence Awareness Scale of the women who were exposed to economic violence was found to be statistically significantly higher than the women who were not exposed to domestic violence, who were exposed to verbal emotional violence, and who were exposed to multiple violence.

Conclusion: In the study, it was determined that women's awareness of domestic violence was at a low level. Factors affecting the awareness of domestic violence and the level of family violence awareness of women should be evaluated; Studies should be carried out to raise awareness about domestic violence and to prevent violence.
Diagnostic evaluation of optical coherence tomography parameters in normal, preperimetric and perimetric glaucoma patients
Authors:   Department of Ophthalmology, Tuanku Mizan Armed Forces Hospital, Kuala Lumpur 53300, Malaysia, Yusof Aimy Mastura Zurairah , Tang Seng Fai ,   Department of Ophthalmology, UKM Medical Center, Jalan Yaacob Latif, Cheras 56000, Kuala Lumpur, Malaysia, Hassan Mohd Rohaizat ,   Department of Community Health, UKM Medical Center, Jalan Yaacob Latif, Cheras 56000, Kuala Lumpur, Malaysia, Din Norshamsiah Md ,   Department of Ophthalmology, UKM Medical Center, Jalan Yaacob Latif, Cheras 56000, Kuala Lumpur, Malaysia
Published: October 2022

AIM: To compare the diagnostic ability of glaucoma parameters measured by the optical coherence tomography (OCT) in normal, preperimetric glaucoma (PPG) and perimetric glaucoma (PG) patients.
METHODS: This cross-sectional observational study includes 127 eyes of 127 subjects. Patients were divided into PPG (51 eyes), PG (46 eyes), and normal controls (30 eyes) based on clinical optic disc assessment and Humphrey visual field changes. The Heidelberg Spectralis OCT machine using Glaucoma Module Premium Edition software was used to measure the retinal nerve fiber layer (RNFL) and Bruch’s membrane opening-minimum rim width (BMO-MRW) to assess the optic nerve head and ganglion cell layer (GCL) thickness in the macula.
RESULTS: RNFL, MRW, and GCL thickness were all significantly thinner in PG compared to PPG and the normal group. The BMO-MRW parameters showed better specificity (>70%) at 90% specificity compared to both RNFL and GCL parameters to discriminate normal, PPG, and PG patients. All BMO-MRW parameters showed higher area under curves (AUC) compared to RNFL and GCL parameters with the highest AUC observed in the superotemporal sector of the BMO-MRW (AUC=0.819 and and 0.897 between normal and PPG and PG groups respectively).
CONCLUSION: While the BMO-MRW best discriminates PPG and PG against normal eyes, GCL parameters poorly differentiate the three groups.
Factors associated with diabetic retinopathy screening and regular eye checkup practice among diabetic patients attending Felege Hiwot Specialized Hospital
Authors:   Department of Optometry, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Fekadu Sofonias Addis , Akalu Yonas ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Gela Yibeltal Yismaw ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Diress Mengistie ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Getnet Mihret ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Dagnew Baye ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia, Belsti Yitayeh ,   Department of Human Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar P.O.BOX 196, Ethiopia
Published: October 2022

AIM: To identify associated factors of diabetic retinopathy (DR) screening and ey e check-up practice among diabetes mellitus (DM) patients attending Felege Hiwot Specialized Hospital.
METHODS: An institution-based cross-sectional study was applied from October 4, 2019 to January 12, 2020 at Felege Hiwot Specialized Hospital. A systematic random sampling technique was used to recruit participants and an interviewer-administered questionnaire was employed to collect the data. The collected data were entered into Epi Info version 7 and transposed to SPSS version 24 for statistical analysis. Descriptive statistics were executed and associated factors were identified using binary logistic regression. The strength of association between the independent and the outcome variable was determined using an adjusted odds ratio (AOR) with 95% confidence interval (CI).
RESULTS: Four hundred and six participants partake with a response rate of 95.7% and a mean age of 47±11.5y. The magnitude of DR screening was 308 (75.9%, 95%CI: 71.5%, 79.8%). Higher educational level (AOR=3.25; 95%CI: 1.40, 8.78), good knowledge of DR (AOR=2.50; 95%CI: 1.55, 4.46), and family history of DM (AOR=2.15; 95%CI: 1.41, 3.85) were significantly associated with DR screening. On the other side, rural residence [AOR=3.11 (1.89, 5.02)] and undesirable attitudes toward DR [AOR=5.65 (3.14, 8.76)] were significantly associated with poor regular eye checkup practice.
CONCLUSION: Most of the participants are screened for DR. Higher education, family history, and good knowledge are associated with DR screening. In addition, rural residence and undesirable attitudes toward DR are associated with regular eye checkup practice.
Toward a “New Normal”: A Case Study of the Pandemic's Effect on Film Exhibition
Authors: Owczarski Kimberly
Published: October 2022

As the largest theatrical chain globally, AMC Theatres faced enormous financial losses during the coronavirus pandemic, but the company also found opportunities to expand their business and contest the studios' growing dependence on streaming services. AMC provides a case study of how the pandemic affected movie theaters and how chains negotiated this challenging time. AMC's ability to overcome these challenges provides a blueprint for what it takes to survive in the theatrical business moving forward.
No Time to Die—Literally: Risk, Fandom, and Theatergoing during the COVID‐19 Pandemic
Authors: Johnson Tyler , Funnell Lisa
Published: October 2022

COVID‐19 impacted moviegoing and fandom in profound ways. Such concerns were especially acute for properties delayed by the pandemic, like the 25th James Bond film, No Time to Die. Original survey research shows that willingness to see the film opening weekend without social distancing was significantly lower amongst Bond fans who still perceived COVID to be a crisis. Interest in returning to the theater in general was also impacted by such assessments of the pandemic.
Evaluation of Deep Convolutional Neural Network with Residual Learning for Remote Sensing Image Super Resolution
Authors: Sustika Rika
Published: October 2022

Remote sensing images generally have low spatial resolution because of the limitations of sensing devices, bandwidth transmission, or storage capacity. An effective way to improve spatial resolution with low cost is by using algorithm based approach, known as super resolution (SR). In recent years, deep learning is super resolution technique that received special attention because it gave better performance than traditional method. In this research, we evaluated two simple deep learning architectures and explored parameters setting of deep convolutional neural network with residual learning, to achieve the trade-off between performance and speed or computational complexity, for implementation on remote sensing image super resolution. Results from the experiment show that deeper network with smaller number of filter gives faster model than shallow network with bigger number of filter, without sacrificing the performance.
Cloud-based ECG Interpretation of Atrial Fibrillation Condition with Deep Learning Technique
Authors: Tutuko Bambang , Passarella Rossi , Firdaus Firdaus , Rachmatullah Muhammad Naufal , Darmawahyuni Annisa , Sapitri Ade Iriani , Nurmaini Siti
Published: October 2022

The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99%. The overall satisfaction performance for the process of AF screening, and it is feasible to conduct AF screening by using a telehealth monitoring system containing an embedded cloud-computing algorithm.
Detection of Atrial Fibrillation Based on Long Short-Term Memory
Authors: Auliya Ghina , Effendi Jannes
Published: October 2022

Atrial fibrillation is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure, and even sudden cardiac death. This study used several public datasets of electrocardiogram (ECG) signals, including MIT-BIH Atrial Fibrillation, China Physiological Signal Challenge 2018, MIT-BIH Normal Sinus Rhythm based on QT-Database, and Fantasia Database. All datasets were divided into 3 cases with the experiment windows size 10, 5, and 2 seconds for two classes, namely Normal and Atrial Fibrillation. The recurrent neural networks method is appropriate for processing sequential data such as ECG signals, and k-fold Cross-Validation can help evaluate models effectively to achieve high performance. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 94.56% 94.67%, 94.67%, 94.43%, and 94.51%.
Application of the Relief-f Algorithm for Feature Selection in the Prediction of the Relevance Education Background with the Graduate Employment of the Universitas Sriwijaya
Authors: Yahdin Sugandi , Desiani Anita , Gofar Nuni , Agustin Kerenila
Published: October 2022

Career Development Center (CDC) at Universitas Sriwijaya provided a tracer study dataset for graduates. The data contained feature questions about the relevance of background education and graduate employment, namely about lectures, research projects experience, internships experience, English skill, internet knowledge, computer skill and others.  the data was filled in by graduates in 2014, 2015, and 2016. Applying the Relief-f algorithm was to select the pattern features that most influence the relevance of education background and graduate employment. This study used Naive Bayes and KNN methods to measure the success rate of the Relief-f algorithm. The results of the accuracy of the data before the feature selection process for the naïve Bayes method were 73.43% and the KNN method was 66.24%, after the feature selection process the accuracy obtained in both methods increased to 74.38% for the Naive Bayes method and 72.22% for the KNN method. The best pattern features selected were 8 features: department relationship with work, the competence of education background, English skill, research projects experience, extracurricular activities, the competence of education background, internships experience, and communication skills. Based on the accuracy obtained, it was concluded that the Relief-f algorithm worked well in the feature selection and improved the accuracy.
Traffic Violation Detection System Using Image Processing
Authors: Umar Buhari Ugbede , Olaniyi Olayemi Mikail , Agajo James , Isah Omeiza Rabiu
Published: October 2022

Over the last three decades, the global population of human beings has increased at an exponential rate, resulting in an equal rise in the number of vehicles owned and used globally. Vehicle traffic is a major economic component in both urban and rural areas, and it requires proper management and monitoring to ensure that this mass of vehicles coexists as smoothly as possible. The amount of vehicular traffic on roads around the world, with Nigeria as a case study, results in varying degrees of traffic rule violations, especially red light jumping.  To arrest offenders and resolve the weaknesses and failures of human traffic operators who cannot be everywhere at once, efficient traffic violation and number plate recognition systems are needed. There are several methods for reading characters, which can be alphabets, numbers, or alphanumeric. To minimize processing time and computational load on the machine, this research proposed k-Nearest Neighbour for plate number character recognition. The system was developed and evaluated. From the result, the localization of license plate regions within an image was 92 percent accurate, and character recognition was 73 percent accurate.
Forward Chaining for Contextual Music Recommendation System
Authors: Dewi Ratih Kartika , Ramadhan M. Salman , Harjananto Dwi Yovan , Sari Chindy Aulia , Islamiah Zumrotul
Published: October 2022

Music is an important aspect of people's daily lives. The reasons people listen to music include to fill their free time and to keep the mood in good condition. Music recommendations are a recommendation system that exists not only because of the many types of music available, but also because people's perceptions of music are still not fully understood. But with so many music choices it makes it difficult for users to find music that fits their context. Examples include considering music based on the current user's location or current activities. A system is required that can recommend music in the context faced by the user.Music Recommendation System Development, Based on user context is a mobile application that uses the Android operating system. The recommendations provided by this system use expert system methods with forward chaining flow. The system will process inputs obtained from users and provide musical recommendations in accordance with the references provided by experts. The result of this study is a rule that is built to produce an average accuracy between user choice and system recommendations of 72%.
Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network
Authors: Rifai Ahmad , Rachmamtullah Muhammad Naufal , Sutarno Sutarno , Tutuko Bambang
Published: October 2022

Atrial fibrillation is the most common type of arrhythmia. The process of detecting AF disease is quite difficult. This is because it is necessary to detect the presence or absence of a P signal wave in the ECG signal. However, this method requires special expertise from a cardiologist. Several literatures have proposed an automatic ECG classification system. However, the intra-patient paradigm does not simulate real-world scenarios. One of the challenges in the inter-patient paradigm is the morphological differences between one subject and another. In order to overcome the problems that arise in the automatic classification of ECG signal patterns a deep learning approach was proposed. This study proposes the classification process of atrial fibrillation in the inter-patient paradigm using a one-dimensional convolutional neural network architecture. The test is divided into two cases: two labels (Normal and AF) and three labels (Normal, AF and Non-AF). In the case of two test labels with an inter-patient scheme, the performance was 100% for all test metrics (accuracy, sensitivity, precision, and F1-Score). However, in the three-label case, the model's performance decreased to 85.95, 70.02, 72.50, 71.19 for accuracy, sensitivity, precision and F1-Score, respectively.
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering
Authors: Setiowati Sulis , Adji Teguh Bharata , Ardiyanto Igi
Published: October 2022

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.
Automated Continuous IoT-based Monitoring System for Vaname Shrimp Cultivation Management
Authors: Syauqy Dahnial , Hanggara Buce Trias , Purnomo Welly , Putra Widhy Hayuhardhika Nugraha , Prasetya Nyoman Wira
Published: October 2022

Shrimp cultivation in Indonesia has been increasing since the introduction of white leg shrimp or often known as vaname (Penaeus vannamei) from the South Pacific waters. The use of a cultivation model with a circular pond with a diameter of 10 meters has begun to attract shrimp farmers in the northern coastal areas of Java, including Tuban Regency. There are several water quality parameters that affects survival rate such as Dissolved Oxygen (DO), Temperature, and Total Dissolved Solids (TDS). Shrimp pond farmers in Tuban Regency have used digital measuring tools to monitor the environmental conditions. However, these measurements cannot be carried out continuously for 24 hours. This often causes delays in identifying problems that occur in ponds and eventually impacts on reducing biomass weight, resulting in not achieving harvest targets. In this study, a continuous monitoring system for water quality management was designed and implemented. The system consists of an IoT-based water quality monitoring device combined with a Shrimp Aquaculture Management Information System. Based on the system that has been built, it is found that the system has been able to acquire all sensor parameters and send them to the server. The results of calibration and prediction using linear regression show that the average data reading error is achieving 14% for DO sensors, and 1% each for temperature and TDS sensors. The aggregated data is presented in tabular and graphic formats so that daily monitoring and predictions can be carried out in ponds.
Parking System Optimization Based on IoT using Face and Vehicle Plat Recognition via Amazon Web Service and ESP-32 CAM
Authors: Ashari Ilham Firman
Published: October 2022

Today's technology has developed rapidly. One application of technology is in the parking lot. Most parking lots in Indonesia can already recognize the vehicle plate image, but it is hoped that it can be even better by applying Internet of Things (IoT) technology that is integrated with facial recognition images. One of the parking problems is in the parking lot at the Sumatran Institute of Technology, where checking is still done manually by security officers. This of course will take time and the level of security is also not good, because when you enter there is no checking. Checks are only carried out at the time of exit and the officer who checks is not necessarily the same and memorized as the owner of the vehicle. The addition of this facial image recognition feature is expected to increase the security of the parking system. Facial image recognition can be assisted by Cloud services from Amazon Image Recognition. With this service, no training data is required. The system developed is only a prototype. The developed parking system can recognize facial images and vehicle license plates with 2 cameras using the ESP32-Cam when entering and exiting the parking lot. The use of the ESP32-cam can recognize facial images both during the day and at night. The results obtained by the system can work effectively with an increase of 21 %.
Analysis and Implementation of Augmented Reality Using Markerless and A-Star Algorithm (Case Study: Gedung Kuliah Umum ITERA)
Authors: Ashari Ilham Firman
Published: October 2022

Institut Teknologi Sumatera is a public university in the province of Lampung. Institut Teknologi Sumatera (ITERA) has many buildings, including Gedung Kuliah Umum (GKU). GKU is the largest and widest lecture building in ITERA. GKU has four floors, where each floor has many rooms in it with different functions in each room. As the largest building in ITERA, GKU is often used for various events, including CPNS exams, new student admissions, or for visits from other campuses. Due to the size of this building, this allows visitors from outside ITERA to GKU to experience problems in terms of time to ask questions and difficulty finding various spaces in the GKU Building. This research uses Augmented Reality technology to help make it easier for visitors from outside ITERA to find space quickly and precisely. In its development using several tools, including the ARWaKit SDK. This framework is used on devices with the IoS operating system. In its implementation, it requires a camera on a smartphone to capture existing images and convert them into cyberspace. In the ARWayKit framework, Azure Spatial Anchors have been used which can be used to carry out the mapping process as a markerless method and to optimize the distance from the user's position to the destination location, the a-star algorithm is used. The results obtained from the Variation-2 test were 91.6%.
Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model
Authors: Rini Dian Palupi , Afandi Defrian , Rodiah Desty
Published: October 2022

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.
PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction
Authors: Desiani Anita , Maiyanti Sri Indra , Miraswan Kanda Januar , Arhami Muhammad
Published: October 2022

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader.
Trough OpenMP Platform for Reducing Computational Time Cost in Underwater Landslide Simulation on Inclined Bottom
Authors: Gunawan Putu Harry , Lobma Fadhil
Published: October 2022

Simulation of underwater landslide becomes important, since underwater landslide phenomena is very dangerous in real life. One of the enormous disasters caused by this phenomena can be a Tsunami. Computer simulation of underwater landslide can reduce cost of time and money from conventional simulation (using laboratory). However, to obtain high resolution of computer simulation, large discrete points should be computed. In this paper, the numerical simulation of underwater landslide using two-layers shallow water equations (SWE) and OpenMP platform is elaborated. Here, the finite volume method framework using upwinding dispersive correction hydrostatic reconstruction (UDCHR) scheme is used. The results of numerical simulation is in a good agreement with the numerical simulation using Nasa-Vof2d numerical scheme. In parallel performance, speedup and efficiency of this numerical simulation are observed 2.8 times and 76% respectively at t=0.8 s final time simulation.
Design and Implementation of Temperature Control for a Mini-chamber using Self-Tuning PID Controller
Authors: Muslim Muhammad Aziz , Hidayatullah M. Rony
Published: October 2022

Despite its popularity in industrial application, PID controller suffers parameters setting difficulty due to set point change, disturbance, and ageing. This paper proposed Self-tuning PID controller using Dahlin method for temperature control of a laboratory scale mini-chamber. Experimental results show that the proposed controller has better performance compared to the conventional PID controller in term of rise time and settling time. It also shows that the algorithm can compensate the changing environment and robust toward the existence of disturbance.
Fuzzy-PID Controller Design of 4 DOF Industrial Arm Robot Manipulator
Authors: Oktarina Yurni , Septiarini Fradina , Dewi Tresna , Risma Pola , Nawawi Muhammad
Published: October 2022

Arm robot manipulator is the most applied robot to substitute human labor in industries. Due to the importance of arm robot manipulator in manufacturing lines, the robustness and effective design are essential in building an arm robot. This paper presents the controller, mechanical, and motion designs of an arm robot manipulator. The fuzzy logic controller is employed to ensure the effectiveness in detecting the target object. PID controller is designed to enhance the smooth and stability of robot motion. The simulation of how the robot move inside its workspace was conducted using RSTX toolbox in SciLab. The motion is generated by deriving Denavit-Hartenberg parameters of the mechanical design. The result shows the effective design of Fuzzy-PID controller and mechanical design of a pick and places arm robot manipulator.
Drowsing Driver Alert System for Commercial Vehicles
Authors: Kommey Benjamin , Kotey Seth Djanie , Agbemenu Andrew Selasi
Published: October 2022

A number of accidents on our roads are caused by driver fatigue or drowsiness. Human fatalities as a result of driver drowsiness has been a major challenge for road safety bodies worldwide. Various road safety campaign messages have been put out to discourage drivers from driving whilst tired, but the problem still persists. Different technologies have been proposed over the years, but most seem to be too expensive to implement on a large scale. We present an inexpensive drowsing driver alert system in this paper. The system, known as Drowsing Driver Alert System (DDAS) is a smart system intended to effectively keep commercial drivers alert when driving. The system is able to detect when a driver is drowsy and alert him/her in real-time to prevent a potential accident. Using a camera, the eyes of the driver are monitored continuously whiles driving and analyzed to determine if they are shut or the blink rate is not normal. Two stages of alerts are given if the driver is determined to be drowsy. Log files of activities performed by the system are also saved to an external storage device to enable further analysis later.
Optimal Control of Jebba Hydropower Operating Head by a Dynamic Programming
Authors: Ogunbiyi Olalekan , Thomas Cornelius T
Published: October 2022

Nigeria with a generating potential of roughly 12,522 MW only supplies less than 20% of the national demand. This necessitates an optimal use of the Jebba Hydroelectric Power Plant whose optimal generation depends on the operating head. This paper presents the solution to an optimal control problem involving the operating head of the plant. An optimal control problem consisting of a model of the system dynamics, performance index and system constraints was solved using a dynamic programming approach. The control procedure was built on the integration of the nonlinear dynamical model by an Adams-Moulton technique with Adams-Bashfort as predator and Runge-Kutta as a starter. The numerical solution, coupled with dynamic programming was employed in developing an optimal control procedure for the regulation of the operating head. Result presented shows the potential of the control procedure in determining the amount of inflow required to restore the operating head to a nominal level whenever there is a disturbance.
The Application of Push Button Switch as Inverse Kinematics Input on Adaptive Walking Method for Hexapod Robot
Authors: Risma Pola , Bagaskara Muhammad , Latifah Nyayu Husni , Anisah Masayu , Rialita Adella
Published: October 2022

Any kinds of natural disaster are undesirable. Loss and damage are the most experienced as they come. Property and people have to be relieved, and it's not an easy matter. Among the deaths caused by buildings, some may still be alive and need helps as soon as possible, but this is too risky for the rescue team since the location is still in dangerous level. Therefore, we created the detector hexapod robot to replace the tasks of the rescue teams in searching for the victims of the disasters, so there are no more victims from the rescue team. The hexapod robot is a six-legged robot which shapes and runs like a spider. This research focuses on the analysis of the push button switch as a robotic foot control input. This is because walking technique is an effective major factor in navigation of robots. A good method is required to maintain the height of the robot's foot while it is walking. So to solve this, the push button switch application is used along with the inverse kinematics calculations on each routine program in adjusting the position of the end effector on the floor surface. In shifting, the navigation runs well without any failure if the position of the foot does not touch the floor. The test is done in 2 steps, comparing the inverse kinematics calculations with x and y inputs which are applied to the robot program code then comparing the travel time condition by using push button switch and without push button switch. The result of robot in this study can be re-developed in the future, using servos with greater torque and better control input than push button switch.
Ultra-Wideband Spectrum Hole Identification Using Principal Components and Eigen Value Decomposition
Authors: Emeshili Joseph
Published: October 2022

Ultra-Wideband Spectrum Hole identification using Principal Components and Eigen Value Decomposition evolve a method of detecting spectrum hole from complex and corrupted wide band spectrum signal, due to the effect of noise spectrum hole detection is usually a challenge in wideband signal, as the presence of noise give rise to error alert, that is, noise can be misconstrued for signal. Dimensionality reduction was first used as the first level of denoising   technique, Principal component Analysis (PCA) was used in dimensioning Wide Band Spectrum Data; this was able to reduce the noise level in the signal which made it convenient for Fast Fourier Transform (FFT) to act on it.  FFT was used to decompose the signal to 64 sub band channels and on further reduction using principal Component Analysis (PCA), a 32 Level sub-band decomposition was carried out. Eigen Value generated shows that the magnitude of the signal to Noise ratio between Eigen Value 1 to 19 was high enough to show the that there exist a signal, while between 20 to 32 shows no signal by implication it indicates that these areas have high possibility of unoccupied spectrum holes.
A Stochastic Modelling Approach to Student Performance Prediction on an Internet-Mediated Environment
Authors: Khakata Esther , Omwenga Vincent Oteke , Msanjila Simon Samwel
Published: October 2022

Student performance prediction presents institutions and learners with results that assist them to gauge their academic abilities within their context of learning. Performance prediction has been done using different approaches over the years. In this case, stochastic modelling is used and it takes into consideration the use of random variables in the prediction process. The random variables are generated from different scenarios in order to generate a possible output. As a result, the generated output is used to indicate the likelihood of very rare occurrence scenarios which may or may not take place at a future date. With the vast availability of educational data that is available within the learning sector, this data forms the basis of input data that is required for the prediction of student performance within internet-worked environments. This paper develops the prediction model using Stochastic Differential Equations (SDEs). This then gives way to the analysis of data collected from varied respondents within universities leading to the generation of a student performance trajectory.
A Smart Real-Time Standalone Route Recognition System for Visually Impaired Persons
Authors: Abdullahi Ibrahim Mohammed , Isah Rabiu Omeiza , Aliu Daniel , Momoh Muyideen Omuya
Published: October 2022

Visual Impairment is a common disability that results in poor or no eyesight, whose victims suffer inconveniences in performing their daily tasks. Visually impaired persons require some aids to interact with their environment safely. Existing navigation systems like electronic travel aids (ETAs) are mostly cloud-based and rely heavily on the internet and google map. This implies that systems deployment in locations with poor internet facilities and poorly structured environments is not feasible. This paper proposed a smart real-time standalone route recognition system for visually impaired persons. The proposed system makes use of a pedestrian route network, an interconnection of paths and their associated route tables, for providing directions of known locations in real-time for the user. Federal University of Technology (FUT), Minna, Gidan Kwanu campus was used as the case study. The result obtained from testing of the device search strategy on the field showed that the complexity of the algorithm used in searching for paths in the pedestrian network is , at worst-case scenario, where N is the number of paths available in the network. The accuracy of path recognition is 100%. This implies that the developed system is reliable and can be used in recognizing and navigating routes by the visual impaired in real-time.
Gas Source Localization Using Bio-inspired Algorithm for Mini Flying Sniffer Robot: Development and Experimental Investigation
Authors: Fikri Muhamad Rausyan
Published: October 2022

In this paper, we demonstrated a gas source localization (GSL) using a mini quadrotor as a mini flying sniffer robot. The algorithm employed is based on a bioinspired algorithm from insect behavioral searching and it is constrained to perform only in 2D dimension open space area. In this study, we deliver some information such as system development, and algorithm flowchart to highlight how this study can achieve the target goal. The performance of insect behavioral based for searching the source location shows an interesting result. Where we can achieve a satisfactory result to find the source position using a bioinspired algorithm. The experimental results are provided to evaluate the performance of the searching algorithm.
Reducing Generalization Error Using Autoencoders for The Detection of Computer Worms
Authors: Odunga Nelson Ochieng , Mwangi Ronald Waweru , Lukandu Ismail Ateya
Published: October 2022

This paper discusses computer worm detection using machine learning. More specifically, the generalization capability of autoencoders is investigated and improved using regularization and deep autoencoders. Models are constructed first without autoencoders and thereafter with autoencoders. The models with autoencoders are further improved using regularization and deep autoencoders. Results show an improved in the capability of models to generalize well to new examples.