bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Upah Kerja Terhadap Produktivitas Pengrajin Rotan Di PT. House Of Rattan
Authors: Irfan Ali  , Gian Fitralisma  , Nur Alia Sumanti
Published: July 2024

House of Rattan is a company operating in the furniture industry made from wood, rattan, aluminum and iron. This company collaborates with sub-partners for the rattan weaving department with a work contract agreement and piecework wages paid every week. This research is motivated by the low productivity of rattan craftsmen at PT. House of Rattan. Thus, the research objective that the author of this study wants to achieve is to find out whether wages influence the productivity of rattan craftsmen at PT. House of Rattan. The method in this study uses a quantitative approach, using data obtained by distributing questionnaires to respondents. In this research, data analysis uses data quality testing, classical assumption testing, and hypothesis testing. The population and sample in this study were rattan craftsmen at PT. House of Rattan numbering 55 people. Based on the research results, it can be seen that the Wage variable (X) has a significant influence on the Labor Productivity variable (Y), with the results of the t test, the results obtained for the Work Wage variable are tcount > ttable (6.022 > 2.004), so Ha is accepted and Ho is rejected. So, within a confidence range of 95%, it can be concluded that there is sufficient evidence to say that the wage variable has a significant effect on the productivity of rattan chair craftsmen at PT. House of Rattan
ABSTRAK
House of Rattan merupakan perusahaan yang bergerak di bidang industri furniture dengan berbahan dasar kayu, rotan, alumunium dan besi. Perusahaan ini bekerja sama dengan sub mitra keja untuk bagian anyam rotan dengan perjanjian kontrak kerja dan upah kerja borongan yang dibayarkan setiap minggu nya. Penelitian ini dilatar belakangi oleh rendahnya produktivitas pengrajin rotan pada sub mitra kerja di PT. House of Rattan. Tujuan penelitian yang ingin dicapai oleh penulis dari penelitian ini adalah untuk mengetahui apakah Upah berpengaruh terhadap Produktivitas pengrajin rotan pada PT.House of Rattan. Metode pada penelitian ini menggunakan pendekatan kuantitatif, dengan menggunakan data yang diperoleh dengan menyebarkan kuesioner kepada responden. Dalam penelitian ini menggunakan analisis data uji kualitas data, uji asumsi klasik, dan uji hipotesis. Populasi dan sampel dalam penelitian ini adalah pengrajin rotan pada Sub Mitra Kerja PT. House of Rattan yang berjumlah 55 orang. Berdasarkan hasil penelitian dapat diperoleh bahwa variabel Upah (X) memiliki pengaruh signifikan terhadap variabel Produktivitas Tenaga Kerja (Y), dengan hasil uji t diperoleh hasil untuk variabel Upah kerja diperoleh nilai thitung > ttabel (6,022 > 2,004) maka  diterima dan Ho ditolak. Sehingga pada rentang kepercayaan sebesar 95% didapatkan kesimpulan bahwa terdapat cukup bukti untuk mengatakan bahwa variabel upah kerja berpengaruh signifikan terhadap produktivitas pengrajin kursi rotan di PT. House of Rattan.
Faktor Yang Mempengaruhi Kepuasan Masyarakat Pada Pelayanan Pemakaman Dinas Perumahan Rakyat Dan Kawasan Permukiman Kota Cirebon
Authors: Jajat Sudrajat  , Eman Sulaiman  , Suwandi
Published: July 2024

This study aims to identify the factors that influence community satisfaction with funeral services from the Public Housing and Settlement Area Office of Cirebon City. The factors studied include service quality, service procedures, and facilities as mediating factors that influence the relationship between the main variables and community satisfaction. The research method used was a survey involving 74 respondents who had used funeral services. Data were analyzed using multiple regression techniques. The results of the research show that service quality, service procedures, and facilities individually and collectively have a significant effect on community satisfaction. The implication of this research is the need to improve service quality, optimize service procedures, and improve funeral facilities as an effort to increase community satisfaction with the services of the Public Housing and Residential Area Office of the City of Cirebon.
ABSTRAK
Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi kepuasan masyarakat pada pelayanan pemakaman dari Dinas Perumahan Rakyat dan Kawasan Permukiman Kota Cirebon. Faktor-faktor yang diteliti meliputi mutu layanan, prosedur layanan, dan fasilitas sebagai faktor mediasi yang mempengaruhi hubungan antara variabel utama dengan kepuasan masyarakat. Metode penelitian yang digunakan adalah survei dengan melibatkan masyarakat yang telah menggunakan layanan pemakaman sebanyak 74 responden. Data dianalisis menggunakan teknik regresi berganda. Hasil penelitian menunjukkan bahwa mutu layanan, prosedur layanan, dan fasilitas secara individu maupun bersama-sama berpengaruh signifikan terhadap kepuasan masyarakat. Implikasi penelitian ini adalah perlunya peningkatan mutu layanan, optimalisasi prosedur layanan, dan peningkatan fasilitas pemakaman sebagai upaya untuk meningkatkan kepuasan masyarakat pada pelayanan Dinas Perumahan Rakyat dan Kawasan Permukiman Kota Cirebon.
Faktor Yang Mempengaruhi Kepedulian Masyarakat Terhadap Peningkatan Kapasitas Lingkungan Hidup Pada Dinas Lingkungan Hidup Kota Cirebon
Authors: Daniel Ady Putra  , Gian Fitralisma  , M. Anisul Fata
Published: July 2024

This study aims to analyze the effect of environmental awareness, environmental knowledge, and accessibility of information on public concern for increasing environmental capacity in the city of Cirebon. The data was obtained through a survey of respondents who are members of the community in the area. The results of data analysis using the t test and F test show that the three independent variables (environmental awareness, environmental knowledge, and information accessibility) have a significant influence on community awareness individually and collectively. The implication of this research is the importance of increasing public awareness, knowledge, and access to information about environmental issues to strengthen their participation in efforts to preserve and increase environmental capacity.
ABSTRAK
Penelitian ini bertujuan untuk menganalisis pengaruh kesadaran lingkungan, pengetahuan lingkungan, dan aksesibilitas informasi terhadap kepedulian masyarakat terhadap peningkatan kapasitas lingkungan hidup di Kota Cirebon. Data diperoleh melalui survei kepada responden yang merupakan masyarakat di wilayah tersebut. Hasil analisis data menggunakan uji t dan uji F menunjukkan bahwa ketiga variabel independen (kesadaran lingkungan, pengetahuan lingkungan, dan aksesibilitas informasi) memiliki pengaruh yang signifikan terhadap kepedulian masyarakat secara individu maupun bersama-sama. Implikasi dari penelitian ini adalah pentingnya meningkatkan kesadaran, pengetahuan, dan akses informasi masyarakat tentang isu-isu lingkungan untuk memperkuat partisipasi mereka dalam upaya pelestarian dan peningkatan kapasitas lingkungan hidup.
Pengaruh Kualitas Pelayanan Dan Harga Terhadap Kepuasan Pelanggan Di Percetakan “All In One”
Authors: Nyimas Riri Badriah  , Eman Sulaiman  , Winda Dwi Yanthi
Published: July 2024

This research aims to analyze the influence of service quality and price on customer satisfaction at All In One printing in Majalengka. Data was collected from 95 respondents who are consumers of the printing press. Data analysis includes validity tests, reliability tests, classical assumption tests, regression analysis, and F tests. Data were analyzed using multiple analysis techniques. The method used in this research is a quantitative descriptive method. Quantitative research methods are research methods that are based on the philosophy of positivism, used to research certain populations or samples, collecting data using research instruments, data analysis is quantitative/statistical. With the aim to test the hypothesis that has been set. The results showed that service quality and price have a significant effect on customer satisfaction. The variables of service quality and price together are able to explain 89.6% of the variation in customer satisfaction. The practical implication of this research is that improving service quality and setting the right prices can increase customer satisfaction at the printing shop.
ABSTRAK
Penelitian ini bertujuan untuk menganalisis pengaruh kualitas pelayanan dan harga terhadap kepuasan pelanggan di percetakan All In One di Majalengka. Data dikumpulkan dari 95 responden yang merupakan konsumen dari percetakan tersebut. Analisis data meliputi uji validitas, uji reliabilitas, uji asumsi klasik, analisis regresi, dan uji F. Data dianalisis menggunakan teknik analisis berganda. Metode yang digunakan dalam penelitian ini adalah metode deskriptif kuantitatif. Metode penelitian kuantitatif adalah metode penelitian yang berlandaskan pada filsafat positivisme, digunakan untuk meneliti pada populasi atau sample tertentu, pengumpulan data menggunakan instrumen penelitian, analisis data bersifat kuantitatif/statistik. Dengan tujuan untuk menguji hipotesis yang telah ditetapkan. Hasil penelitian menunjukkan bahwa kualitas pelayanan dan harga memiliki pengaruh yang signifikan terhadap kepuasan pelanggan. Variabel kualitas pelayanan dan harga secara bersama-sama mampu menjelaskan 89,6% variasi dalam kepuasan pelanggan. Implikasi praktis dari penelitian ini adalah bahwa perbaikan kualitas pelayanan dan penetapan harga yang tepat dapat meningkatkan kepuasan pelanggan di percetakan tersebut.
Big Data Predictive Analytics for Personalized Medicine: Perspectives and Challenges
Authors: Al-Quraishi Tahsien , Al-Quraishi Naseer , AlNabulsi Hussein , AL-Qarishey Hussein , Ali Ahmed Hussein
Published: July 2024

The integration of predictive analytics into personalized medicine has become a promising approach for improving patient outcomes and treatment efficacy. This paper provides a review of the field, examining the tools, methodologies, and challenges associated with this advanced statistical methodology. Predictive analytics leverages machine learning algorithms to analyze vast datasets, including Electronic Health Records (EHRs), genomic data, medical imaging, and real-time data from wearable devices. The review explores key tools such as the Hadoop Distributed File System (HDFS), Apache Spark, and Apache Hive, which facilitate scalable storage, efficient data processing, and comprehensive data analysis. Key challenges identified include managing the immense volume of healthcare data, ensuring data quality and integration, and addressing privacy and security concerns. The paper also highlights the difficulties in achieving real-time data processing and integrating predictive insights into clinical practice. Effective data governance and ethical considerations are critical to maintaining trust and transparency. The strategic use of big data tools, combined with investment in skill development and interdisciplinary collaboration, is essential for harnessing the full potential of predictive analytics in personalized medicine. By overcoming these challenges, healthcare providers can enhance patient care, optimize resource management, and drive medical discoveries, ultimately revolutionizing healthcare delivery on a global scale.
Advanced Ensemble Classifier Techniques for Predicting Tumor Viability in Osteosarcoma Histological Slide Images
Authors: Al-Quraishi Tahsien , Keong NG Chee , Mahdi Osama A. , Gyasi Amoakoh , Al-Quraishi Naseer
Published: July 2024

Background: Osteosarcoma is considered as the primary malignant tumor of the bone, emanating from primitive mesenchymal cells that form osteoid or immature bone. Accurate diagnosis and classification play a key role in management planning to achieve improved patient outcomes. Machine learning techniques may be used to augment and surpass existing conventional methods towards an analysis of medical data.
Methods: In the present study, the combination of feature selection techniques and classification methods was used in the development of predictive models of osteosarcoma cases. The techniques include L1 Regularization (Lasso), Recursive Feature Elimination (RFE), SelectKBest, Tree-based Feature Importance, while the following classification methods were applied: Voting Classifier, Decision Tree, Naive Bayes, Multi-Layer Perceptron, Random Forest, Logistic Regression, AdaBoost, and Gradient Boosting. Some model assessment was done by combining metrics such as accuracy, precision, recall, F1 score, AUC, and V score.
Results: The combination of the Tree-Based Feature Importance for feature selection and Voting Classifier with Decision Tree Classifier proved to be giving a higher performance compared to all other combinations, where such combinations helped in correct classification of positive instances and wonderful minimization of false positives. Other combinations also gave significant performances but slightly less effective, for example, L1 Regularization with the Voting Classifier, RFE with the Voting Classifier.
Conclusion: This work presents strong evidence that advanced machine learning with ensemble classifiers and robust feature selection can result in overall improvement of the diagnostic accuracy and robustness for the classification of osteosarcoma. Research on class imbalance and computational efficiency will be its future research priority.
Optimal Time Window Selection in the Wavelet Signal Domain for Brain–Computer Interfaces in Wheelchair Steering Control
Authors: Al-Qaysi Z.T. , Suzani M. S , Bin Abdul Rashid Nazre , Aljanabi Rasha A. , Ismail Reem D. , Ahmed M.A. , Sulaiman Wan Aliaa Wan , Kumar Harish
Published: July 2024

Background and objective: Principally, the procedure of pattern recognition in terms of segmentation plays a significant role in a BCI-based wheelchair control system for avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe situations. Arguably, each subject might have different motor-imagery signal powers at different times in the trial because he or she could start (or end) performing the motor-imagery task at slightly different time intervals due to differences in the complexities his or her brain. Therefore, the primary goal of this research is to develop a generic pattern recognition model (GPRM)-based EEG-MI brain-computer interface for wheelchair steering control. Additionally, having a simplified and well generalized pattern recognition model is essential for EEG-MI based BCI applications. Methods: Initially, bandpass filtering and segmentation using multiple time windows were used for denoising the EEG-MI signal and finding the best duration that contains the MI feature components. Then, feature extraction was performed using five statistical features, namely the minimum, maximum, mean, median, and standard deviation, were used for extracting the MI feature components from the wavelet coefficient. Then, seven machine learning methods were adopted and evaluated to find the best classifiers. Results: The results of the study showed that, the best durations in the time-frequency domain were in the range of (4-7 s). Interestingly, the GPRM model based on the LR classifier was highly accurate, and achieved an impressive classification accuracy of 85.7%.
A Frequency-Domain Pattern Recognition Model for Motor Imagery-Based Brain-Computer Interface
Authors: Al-Qaysi Z.T. , Suzani M. S , Bin Abdul Rashid Nazre , Ismail Reem D. , Ahmed M.A. , Wan Sulaiman Wan Aliaa , Aljanabi Rasha A.
Published: July 2024

Brain-computer interface (BCI) is an appropriate technique for totally paralyzed people with a healthy brain. BCI based motor imagery (MI) is a common approach and widely used in neuroscience, rehabilitation engineering, as well as wheelchair control. In a BCI based wheelchair control system the procedure of pattern recognition in term of preprocessing, feature extraction, and classification plays a significant role in system performance. Otherwise, the recognition errors can lead to the wrong command that will put the user in unsafe conditions. The main objectives of this study are to develop a generic pattern recognition model-based EEG –MI Brain-computer interfaces for wheelchair steering control. In term of preprocessing, signal filtering, and segmentation, multiple time window was used for de-noising and finding the MI feedback. In term of feature extraction, five statistical features namely (mean, median, min, max, and standard deviation) were used for extracting signal features in the frequency domain. In term of feature classification, seven machine learning were used towards finding the single and hybrid classifier for the generic model. For validation, EEG data from BCI Competition dataset (Graz University) were used to validate the developed generic pattern recognition model. The obtained result of this study as the following: (1) from the preprocessing perspective it was seen that the two-second time window is optimal for extracting MI signal feedback. (2) statistical features are seen have a good efficiency for extracting EEG-MI features in the frequency domain. (3) Classification using (MLP-LR) is perfect in a frequency domain based generic pattern recognition model. Finally, it can be concluded that the generic pattern recognition model-based hybrid classifier is efficient and can be deployed in a real-time EEG-MI based wheelchair control system.
Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks
Authors: Al-Rubaye Rasha Hameed Khudhur , TÜRKBEN AYÇA KURNAZ
Published: July 2024

This paper is devoted to the use of AI managed to contribute to security of the MANETs (Mobile Ad-hoc Networks), decentralized and mobile wireless networks, that are fully dynamic in nature. The intention of the research is to audit the dangers of cyber and to spot the variety of cyber threats types, including Distributed Denial of Service (DDoS) attacks, malware intrusions, leakages or data breaches, or unauthorized access attempts, using AI-powered algorithms and models. The purpose is to obtain higher degree of veracity of defining and classifying these threats and as result puts more security and reliability to MANET networks. Anomaly detection addressed as a secondary line of defense specific for MANET hardware and network traffic. The monitoring method is needed here to find abnormal behavior that might anyhow signify the possible security flaws or the attacks of the MANET environments. This ultimate goal is penetrated with the timely detection Peculiarities, which makes possible to reinforce MANET security capabilities that require to be well-developed against cyber threats. Experimental results reveal a clear trend of Fleet Grid Algorithm Improvements along with Detection Accuracy (Digital Signals and Anomaly) by means of training AI models (CNN and RF) with algorithms like Random forest and Convolutional neural networks. The machine learning based algorithms often present remarkable results comprising efficiency in detecting and effectively categorizing different cyber threats existing such as DDoS attacks, malware infiltrations and attempted unauthorized access. This method of anomaly detection is able to accurately detect robot anomalies and malicious activities in network traffic in addition to we preventing system vulnerabilities or threats from occurring prematurely. Besides, the findings of this study wide relatively efficient AI-based cybersecurity systems for dynamic decentralized MANET systems, which are developed for street-view switching and path finding, self healing and self configuration.
Underwater Wireless Sensors Increase Routing Performance using Impact Efficient localization-based Routing protocols
Authors: Kavitha T. , Venkatesan M. , Gopalakrishnan S. , Chand S.Ravi , Gopianand M , Abirami S.
Published: July 2024

The Underwater Wireless Sensor Network (UWSN) is an organization used to perform observing of errands over a particular region; it is furnished with shrewd sensors and vehicles that are adjusted to convey helpfully through wireless connections. Remote sensor networks are enormous scope networks comprised of modest, reduced sensors with immense scope energy and settling limit that might be utilized in different unpredictable circumstances under factor conditions. The use of UWSNs is growing daily due to their significant contribution to several applications, including underwater surveillance and search. Wireless sensor networks submerged in water confront unique challenges. Therefore, particular routing protocols are needed from source to destination; security concerns that should be taken into consideration by routing protocols must be addressed by many UWSN applications. The proposed routing protocols influencing IHELBRP (Impact High Efficient Localization-Based Routing Protocols) and the UWSN architectural perspective. Reviewing and examining steering conventions concerning energy consumption, packet delivery rate, and packet delivery rate is finished. The advantages and disadvantages of each steering convention are recorded. To shield the correspondence medium here, a rundown of safety needs is incorporated alongside an investigation of safety worries in UWSN.
Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm
Authors: Sheela M.Sahaya , Suganthi R. , Gopalakrishnan S. , Karthikeyan T. , Jyothi K. Jeevana , Ramamoorthy K.
Published: July 2024

Mobile Ad-hoc Network (MANET) autonomous operation can be multi-hop it is the infrastructure-less wireless network.  Security is one of the biggest challenges in Mobile Adhoc Network. The MANETs security there are considerations must be so that the routing protocol in order to protect the secure data transmission. In the routing and security that is an important aspect for in a MANETs, existing method routing protocol, however, is not enough to security requirements. The proposed method using Fast Recursive Transmission Algorithm (FRTA) used designed to maximize the data security, routing optimization, minimizing the impact of malicious attack using Collision Detection Avoid Algorithm (CDAA) activity over the MANET and select the best path. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. The proposed method shows high performance than other existing evaluations of the most advanced security and routing energy, end-to-end delay, packet transfer rate, packet loss
Exploring Deep Learning Methods Used in the Medical Device Sector
Authors: Kayusi Fredrick , Turyasingura Benson , Chavula Petros , Justine Amadi Orucho
Published: July 2024

The healthcare sector is witnessing significant development in many aspects thanks to the effects of artificial intelligence or software, which has turned out to be the centre of attraction all over the world. This is evidence of a simple development in acquiring deep knowledge of the methods and areas in which they are used. Face detection, voice recognition, autonomous use, the defence industry, the security industry, and other fields may be displayed as examples that help complete tasks. This article surveys the impact of deep learning methods and practices in the medical device industry, and we also examine the distribution of multi-year data. It is divided into six categories: healthcare, big data and wearable technologies, biomedical code, image processing, diagnostics, and the Internet of Medical Things. As a result, the medical device industry has grown in recent years through deep learning techniques and the use of most research related to diagnosis and image processing.
DARKNET-53 Convolutional Neural Network-Based Image Processing for Breast Cancer Detection
Authors: Rajkumar R. , Gopalakrishnan S. , Praveena K. , Venkatesan M. , Ramamoorthy K. , Hephzipah J. Jasmine
Published: July 2024

Breast cancer is a common type of cancer in women, denoted by the uncontrolled growth of cells in breast tissue. Thus, manually detecting breast cancer is time-consuming and necessitates automated systems. Existing breast cancer screening methods often have limited efficacy and may delay detection and complicate the individual treatment planning process. However, early detection of breast cancer can be costly and impact the accuracy of diagnosis. To address this issue, we introduce a Darknet-53 Convolutional Neural Network (darknet-53CNN) approach for classifying breast cancer images and improving precision. Furthermore, we utilise the Contrast-Limited Adaptive Histogram Equalization (CLAHE) technique to pre-process breast cancer images to enhance image quality. Furthermore, we evaluate the intensity level of pixel images by feature extraction using the Haralick Grey-Level Co-Occurrence Matrix (HGLCM) technique. Finally, the DarkNet-53 CNN method improves the accuracy of detecting breast cancer and classifying images as benign or malignant. The proposed algorithm evaluates the specificity, sensitivity, accuracy and precision of predictive test results based on the classification of breast cancer images. Moreover, the accuracy of the proposed method has increased to 95.6% compared to the methods obtained from previous approaches.
Benchmarking Generative AI: A Call for Establishing a Comprehensive Framework and a Generative AIQ Test
Authors: Sallam Malik , Khalil Roaa , Sallam Mohammed
Published: July 2024

The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAI tools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI models in healthcare should be accompanied appropriate and rigorous performance evaluation tools. In this rapid communication, we emphasizes the urgent need to develop a “Generative AIQ Test” as a novel tailored tool for comprehensive benchmarking of genAI models against multiple human-like intelligence attributes. A preliminary framework is proposed in this communication. This framework incorporates miscellaneous performance metrics including accuracy, diversity, novelty, and consistency. These metrics were considered critical in the evaluation of genAI models that might be utilized to generate diagnostic recommendations, treatment plans, and patient interaction suggestions. This communication also highlights the importance of orchestrated collaboration to construct robust and well-annotated benchmarking datasets to capture the complexity of diverse medical scenarios and patient demographics. This communication suggests an approach aiming to ensure that genAI models are effective, equitable, and transparent. To maximize the potential of genAI models in healthcare, it is important to establish rigorous, dynamic standards for its benchmarking. Consequently, this approach can help to improve clinical decision-making with enhancement in patient care, which will enhance the reliability of genAI applications in healthcare.
Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems
Authors: Ruby E.D. Kanmani , Amirthayogam G. , Sasi G. , Chitra T. , Choubey Abhishek , Gopalakrishnan S.
Published: July 2024

Advances in computer vision and machine learning have transformed leaf disease detection by enabling efficient and accurate identification of subtle disease signs in leaves. Leveraging high-resolution imaging, pattern recognition algorithms, and deep learning models, researchers and farmers can now conduct automated detection across various plant species. The development focuses on sophisticated image processing techniques applied to diverse datasets captured under controlled conditions, ensuring comprehensive coverage of lighting, time, and weather variations. Expert annotation of infection stages and types enhances dataset reliability, while pre-processing stages such as resizing and normalization optimize image consistency for robust model training. Data augmentation techniques enrich dataset diversity, complemented by feature extraction methods like RGB color analysis, GLCM texture analysis, and shape descriptors to discern healthy and infected leaves with precision Validation through K-fold cross-validation ensures model reliability across diverse datasets, culminating in a deployable application for real-time leaf health monitoring. Results demonstrate significant advancements, with the proposed model achieving 92% accuracy, surpassing Logistic Regression (87%), Decision Tree (82%), and Support Vector Machine (79%). Over 10 epochs, the model achieves steady improvements to 95% training accuracy and 85% validation accuracy, underscoring its effectiveness. Implementing data augmentation boosts accuracy from 85% to 89%, while analysis of prediction errors refines model performance for enhanced automated plant health monitoring and precision agriculture applications. These advancements highlight the transformative impact of technology in safeguarding crop resilience and optimizing agricultural practices.
Formulation and Characterization of Antioxidant Capsules Containing Cantigi Extract-loaded Nanoparticles Synthesized Via Nanoprecipitation Method
Authors: Kosasih Kosasih , Deny Susanti  , Immanuel Puji Hosanna Asianski  , Dede Irving Maryanto
Published: July 2024

Cantigi extract exhibit numerous activities, including antioxidants. Various dosage forms have been developed, except a capsule formulation. Capsule formulation offers numerous advantages. Objectives: To prepare and characterize the cantigi extract, extract-loaded nanoparticles, capsules containing extract, and capsules containing nanoparticles as antioxidants. Methods: The Cantigi extract was prepared using the maceration method and characterized for both specific and non-specific parameters. The preparation of extract-loaded nanoparticles utilized the nanoprecipitation method with gelatin, poloxamer, and glutaraldehyde as the polymer, stabilizer, and crosslinker, respectively. Characterization of the nanoparticle included particle size, polydispersity index, zeta potential, and entrapment efficiency. Capsule formulation (F): F1 contained the extract, while F2 contained the nanoparticles. Capsule characterization involved assessment of content uniformity, weight variation, disintegration, and release profile. Antioxidant activity was measured using the DPPH method for extract, nanoparticles and capsules. Results: The cantigi extract met specific and non-specific parameters. Nanoparticle size was found to be 235.8+12.62 nm with a polydispersity index of 0.226+0.050, a zeta potential of -8.53+0.27 mV, and an entrapment efficiency of 52.69+1.06%. Characteristics of F1 and F2 included content uniformity of 97.24+4.23 and 89.86+1.48%, weight variation of 185.5+1.31 and 127.22+1.86%, disintegration time of 3.67-4.08 and 5.35-5.97 minutes, extract content of 97.24+4.23 and 89.86±1.48%, and release rate of 86.78+3.98 and 61.79+3.48%, respectively. Antioxidant activities of the extract, extract-loaded nanoparticles, and capsules (F1 dan F2) were determined to be 17.40+0.94, 33.60+1.85, 66.81+1.46, and 106.61+3.32 ppm, respectively. Conclusion: Cantigi extract and nanoparticles exhibited potent antioxidant activities while meeting quality parameters. The capsule formulation displayed varying antioxidant activities and release profiles, with capsules containing nanoparticles demonstrating a sustained release profile.
Determination of Phytochemicals and Some Elemental Compositions of Watermelon from Sokoto, Nigeria
Authors: Ladan Zubaida Abubakar
Published: July 2024

Currently, many people are battling with infectious and chronic diseases in the country. More especially, the chronic diseases are associated with the level of ingredients in the food we take in, therefore, the easiest way to remedy our challenges is to monitor levels of chemicals in foods, especially the ones around us at affordable prices. Watermelon is a typical fruit that is conspicuous in the state, and can be a source of important natural chemicals for body health. The objective of this study was to perform a phytochemical and an elemental analysis of water melon grown in Sokoto state, Nigeria. The phytochemicals were determined with appropriate methods and chemicals of analytical grade, and elements are determined using atomic absorption spectroscopy. Regarding phytochemicals, flavonoids, alkaloids, saponins, and steroids were determined. Sodium levels in the seed, pulp, and peel reveals 0.40±0.02 (ppm), 3.0±0.01 (ppm), 2.00±0.01(ppm) respectively; potassium levels in the seed, pulp, and peel reveals, 5.000 ±0.04(ppm), 0.15±0.02 (ppm), 3.00±0.02 (ppm) respectively. Calcium determination shows values 20±1.0 (ppm), 20.0±1.00 (ppm), 20.30±0.3 (ppm) in seed, pulp, and peel respectively; and magnesium levels in seed, pulp, and peel are, 2.20±0.001 (ppm), 6.0±0.01 (ppm), and 0.40±0.03 (ppm) respectively. Iron levels of seed, pulp, and peel of watermelon reveals, 1.01±0.02 (ppm), 0.10±0.01(ppm), 15.00±0.2 (ppm) respectively; and Zinc 15±0.20 (ppm), 20.0±2.0 (ppm), and 10.00±1.05 (ppm) respectively.  The watermelon contains significant amount of phytochemicals like flavonoids, alkaloids, saponins, and steroids useful in many purposes such as antimicrobial agents in neutralising the infectious diseases, a burden in the state. Also, the fruit contains useful elements in significant amount like, sodium, potassium, calcium, magnesium, iron, and zinc, that can be used in people that suffers specific nutritional problems.
Implications of Social Media Usage on Jukun Culture: A Multidisplinary Approach
Authors: Maikomo John Moses , Ishaku Jesse , Asicus Samuel
Published: July 2024

The intrusive impact of social media has revolutionised communication globally, thereby altering the status quo in virtually every aspect of life in the society. This is manifestly seen in the influence social media exerts on indigenous cultures in most especially developing countries like Nigeria. Jukun culture which is a rich traditional culture from Taraba State, Nigeria is not spared from this unfortunate reality. This study therefore, ascertains the implications of social media usage on Jukun culture from a multidisplinary point of view. Specifically, the study unravels the socio-cultural perspectives of social media usage on Aku-uka’s transitional rites; examines the perceptions of Jukun stakeholders on the implications of social media usage on Jukun traditional rites and proffers possible ways of integrating social media for promotion and transmission of Jukun culture. Anchored on cultural imperialism theory, the study adopts indepth interview to elicit relevant data from Jukun stakeholders on the implications of social media usage on Jukun culture. Among other things, the study found out that the use of social media breaches some aspects of Jukun culture and that certain foreign practices are also being introduced. The study recommends that indigenous pages and accounts should be created on different social media handles for the preservation and propagation of culture in its original form. Conclusively, social media usage has a double-edged impact on Jukun culture with the negative impact competing side-by-side with the positive impact.
Prestasi Akademik dan Perilaku Komunikasi Remaja Pengguna Game Online Free Fire (Studi Kasus di Desa Hijrah, Kecamatan Lape)
Authors: Aminuddin Aminuddin , Rakhmatullah Vivin Nila
Published: July 2024

This research aims to determine the Academic Achievement and Communication Behavior of Adolescent Free Fire Online Game Users in Hijrah Village, Lape District. The research method used is qualitative descriptive with a research focus on case studies. This case study is a type of research that tries to understand a situation in depth and provide meaning. The type of case study used in this research is a situation analysis case study of a particular event or event. The data collection technique used is through documentation, in-depth interviews with informants to determine the Academic Achievement and Communication Behavior of Teenagers who use the Free Fire Online Game in Hijrah Village, Lape District. These findings provide valuable images and insights for parents, educators and researchers to understand more deeply about academic achievement and communication behavior.
Analisis Pengaruh Iklan Judi Slot Online pada Situs Tokyo99 terhadap Masyarakat Kabupaten Sumbawa
Authors: Alfian K Sukma Deva Noer
Published: July 2024

The current development of the internet really helps people so that they can communicate easily without having to meet in person or communicate long distances. This is a positive impact for someone who uses internet technology media wisely. Those who initially used the Internet only to access learning information, games and social media, then because of the curiosity of young people, some began to access online gambling games. One of the online slot gambling sites is TOKYO99. The behavior of playing online slot gambling can have a negative impact on the perpetrator and the social environment. The impact that can arise from this gambling behavior is usually called the Phatalogical Gambler. The aim of this research is to determine the influence of advertising on the TOKYO99 site in promoting online slot gambling games to the people of Sumbawa Regency. This research uses a descriptive qualitative approach method. Qualitative research methods are qualitative data processing techniques carried out in order to describe or discuss research results using conceptual analysis and theoretical analysis approaches. This thesis also uses the theory of syringes and uses and gratification.  The research results show that the TOKYO99 site uses social media as promotional material to encourage people to play online slot gambling. In playing online slot gambling games, the TOKYO99 site offers quite fantastic results, so that people who see this online slot gambling advertising promotion are interested and start playing the game.
The Influence of Employee Commitment on the Performance of State Departments in Kenya
Authors: Mwaniki George Kihara , Omuya Jacqueline
Published: July 2024

The Public Service in Kenya is encountering numerous problems that demand attention to achieve its maximum capability of delivering high-quality services to citizens. The study is to assess the influence of employee commitment on performance of state departments in Kenya. Data obtained was then analysed through descriptive statistics, frequencies and correlational analysis, that the correlation between variables organisational commitment and organisational performance is strong and significant (r = 0.610, p < 0.01), indicating a positive relationship and the findings then presented through tables, bar charts, and histograms. Research limitations are constraints of time and difficulty in gathering information from state department officers. The results revealed that employee commitment improves productivity, service delivery, cost efficiency, and a positive work environment in state departments. Thus, the state departments should implement are to Strengthen Leadership and Management, Employee Engagement, Improve Working Conditions, Promote Employee Well-being, and Implement Fair Policies and Practices.
COMPARATIVE STUDY OF INNER AND OUTER ROTOR FLUX REVERSAL PERMANENT MAGNET MACHINE FOR DIRECT DRIVE WIND TURBINE
Authors: BOULAYOUNE AHCENE , OUBELAID ADEL , CHIBAH AREZKI
Published: July 2024

The topology, operation principle, and electromagnetic performance of the inner rotor flux reversal permanent magnet (IRFRPM) machine and the outer rotor flux reversal permanent magnet (ORFRPM) machine are quantitatively compared in this paper. First, the mass torque of both machines is optimized and compared using particle swarm optimization (PSO) combined with the finite element method (FEM). Second, the static properties of both machines are calculated using finite element analysis (FEA). Both machines are suitable for direct-drive wind turbines, but ORFRPM outperforms IRFRPM.
THREE-LEVEL DIRECT TORQUE CONTROL BASED ON COMMON MODE VOLTAGE REDUCTION STRATEGY FED TWO PARALLEL CONNECTED FIVE-PHASE INDUCTION MACHINE
Authors: BENZAOUI KHALED MOHAMMED SAID , BENYOUSSEF ELAKHDAR , KOUACHE AHMED ZOHIR
Published: July 2024

This survey proposes a mitigation approach for common mode voltage produced in a three-level five-phase inverter fed two parallel-connected five-phase induction machines controlled by direct torque control. The switching frequencies used in the inverter fed these types of drives are characterized by a large amplitude of the produced common mode voltage waveforms (), leading to prominent issues, especially with the near introduction of wide-gap semiconductor technologies. In this context, the introduced approach is an extension to a multi-motor drive of the TATTE method, exploiting the concept of virtual voltage vectors to mitigate the resulting common mode voltage. The collected results critically evaluate the reported approach by improving the drive’s generated common mode voltage status to () compared to () in the conventional method, in addition to a significant reduction in the flux and torque ripples by 22 % and 39 %, respectively, at the cost of an insignificant increase in the current harmonic content by 6 %.
PERFORMANCE OF ZINC OXIDE-VANADIUM PENTOXIDE VARISTORS IN MEDIUM VOLTAGE SURGE ARRESTERS
Authors: LUNGU MAGDALENA-VALENTINA , CARAMITU ALINA , MARIN MIHAI , PĂTROI DELIA , MARINESCU VIRGIL , MANEA CIPRIAN , GODEANU PETRIŞOR , BARBU ALEXANDRA
Published: July 2024

Metal oxide varistor (MOV) discs (Ø28 mm × 12 mm) made of 97.5 mol.% zinc oxide (ZnO), and 0.5 mol.% each of vanadium pentoxide (V2O5), tin (IV) oxide (SnO2), antimony(III) oxide (Sb2O3), cobalt(II, III) oxide (Co3O4), and chromium(III) oxide (Cr2O3) additives were produced using powder metallurgy. The obtained MOVs were polycrystalline with high density. This study focused on analyzing the microstructure and mechanical and electrical properties of the MOVs. The developed MOVs had a fine-grained microstructure with an average ZnO grain size of about 10 μm. The Ag-coated MOV discs tested in a range of 100 – 1300 V ac exhibited a leakage current of 0.033 – 1.420 mA and a minimum varistor voltage of 2.37 ± 0.15 kV. After conducting functional tests on Ag-coated MOVs installed in polymer-housed surge arresters, it was found that they are suitable for use in medium voltage arresters with a nominal voltage (Un) of 25 kV, a continuous operating voltage (Uc) of 12 kV and a leakage current (IL) of 1 mA.
MAXIMUM POWER QUALITY TRACKING OF ARTIFICIAL NEURAL NETWORK CONTROLLER-BASED DOUBLE FED INDUCTION GENERATOR FOR WIND ENERGY CONVERSION SYSTEM
Authors: ELUMALAI RAJENDRAN
Published: July 2024

Renewable energy sources are playing a crucial role in satisfying the upcoming energy source needs for the world. The current power system is power electronics converter based, with several non-linear loads and spotted generations of renewable energy sources, resulting in many power quality issues. Most existing research analyzed MATLAB simulations and presented a high Total Harmonics Distortion (THD) level. This research work proposed an artificial neural network (ANN) control technique for a doubly fed induction generator (DFIG) based wind energy conversion systems (WECSs). To decrease chattering phenomena during the excitation arrangement, an advanced controller works for adaptive modification of the irregular power gain, even preserving the strength of the closed-loop scheme. The proposed PI controller one step forward improves reliability and tracks maximum voltage from the pulse width modulation rectifier. At primary, the modeling of the DFIG was presented. Then, using the proposed ANN controller, the rotor magnitude was tuned to recognize vector control of active and reactive power. The converter intends to activate at unity power factor and provide input currents with adequate harmonic content. The interface between the power’s electronic converter and the DFIG pulls out the most real power potential.  The proposed prototype hardware model and simulation results are verified.
USING ARTIFICIAL NEURAL NETWORKS TO IMPROVE THE EFFICIENCY OF TRANSFORMERS USED IN WIRELESS POWER TRANSMISSION SYSTEMS FOR DIFFERENT COIL POSITIONS
Authors: ÖZÜPAK YILDIRIM , ASLAN EMRAH
Published: July 2024

This study uses magnetic resonance-based coupling theory to study the various placements of transmitter and receiver coils in wireless power transfer (WPT) systems. Various coil placements are examined to show where high efficiency can be achieved within the air gap. Basic characteristics such as self-inductance, mutual inductance, and coupling coefficient were calculated. Artificial neural networks (ANNs) in WPT are a powerful technique for predicting performance characteristics. Using ANNs provides an excellent method for streamlining the design process and reducing time-consuming calculations. To quickly determine and optimize coil design, this study compares recent research on ANN applications in WPT and the performance of different types of ANNs in WPT systems. An artificial neural network (ANN) was trained to predict the magnetic properties of a wireless power transfer (WPT) device. Appropriate cost functions have been implemented to train the ANN properly. It was shown that the trained ANN can effectively reproduce the data obtained by the finite element method (FEM). The results show an effective power transmission at different coil placements, with decreased efficiency observed after a certain distance. These data will help determine the proposed WPT system's air gap and angular limits.
EFFECTIVE OFFENSIVE LANGUAGE DEDUCTION USING DEEP LEARNING IN SOCIAL MEDIA
Authors: ADAIKKAN KALAIVANI , THENMOZHI DURAIRAJ
Published: July 2024

Offensive language detection is the technique of identifying and detecting user-generated offensive comments such as insults, pain, profanity, and racism that are targeted at a specific individual or group on social media. As social media platforms become more prominent, offensive language is used more frequently, becoming a major challenge in modern society. A novel effective offensive language classification (EOLC) technique has been proposed to overcome these challenges. English language tweets from YouTube and X (Twitter) with offensive, mild, swear, and non-offensive tweets are used in this paper. Initially, the tweets and comments are pre-processed, and the features are extracted using different techniques, namely term frequency-inverse document frequency (TF-IDF), WordVec, and lexicon-based features. The extracted features are classified using the graph-based deep learning (GDL) method for numerical representation and decision-making. GDL network is optimized with red fox optimization (RFO) to normalize the weight and biases of the network and achieve better accuracy. The proposed GDL model achieves the highest levels of classification accuracy on the X (Twitter) and YouTube datasets, with 95.5 % and 96.8 %, respectively. The results obtained from GDL are more accurate and of higher quality than those obtained from traditional classifiers. The proposed EOLC method improves the overall accuracy by 5.56 %, 7.4 %, 7.7 %, and 10.2 % better than Text CNN, CNN-LSTM, DRNN, and LogitBoost, respectively.