bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving the Writing Skills of Class XB Students at SMA Negeri 5 Kupang Through the Development of Canva-Based Interactive Teaching Materials
Authors: Akanfani Crosby , Margareta Karus M. , Djerahi Evarista Yulia
Published: October 2024

Penelitian ini dilatarbelakangi oleh terbatasnya referensi pembelajaran yang digunakan siswa Kelas XB SMA Negeri 5 Kupang dalam menulis pada pembelajaran Bahasa Indonesia. Penelitian ini bertujuan untuk meningkatkan keterampilan menulis siswa kelas XB SMA Negeri 5 Kupang dengan menggunakan bahan ajar interaktif berbasis Canva. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) yang melibatkan 36 siswa SMA Negeri 5 Kupang kelas XB tahun ajaran 2023/2024. Alat yang digunakan dalam penelitian ini adalah observasi, wawancara dan tes yang semuanya dianalisis menggunakan statistik deskriptif. Hasil penelitian menunjukkan bahwa (1) hasil penilaian siswa meningkat dari siklus I ke siklus III yaitu masing-masing mencapai 75%, 90% dan 95% yang melampaui Kriteria Ketuntasan Tujuan Pembelajaran (KKTP) dengan nilai 88; (2) guru sudah menunjukkan peningkatan dalam membuat bahan ajar terbuka interaktif dengan menggunakan aplikasi Canva; dan (3) siswa memiliki respon positif ketika menggunakan bahan ajar interaktif karena lebih mudah dipahami. Hasil penelitian menunjukkan bahwa, (1) Hasil penilaian siswa meningkat dari siklus I ke siklus III yaitu 75%, 90%, 95% dan melampaui Kriteria Ketuntasan Sasaran Pembelajaran (KKTP) dengan nilai 88; (2) Telah terjadi peningkatan keterampilan guru dalam mengembangkan bahan ajar interaktif berbasis aplikasi Canva; (3) Siswa memberikan respon positif ketika menggunakan bahan ajar interaktif karena lebih mudah dipahami, menarik, serta dapat digunakan kapan saja dan dimana saja. Simpulan dari penelitian ini yaitu pengembangan bahan ajar interaktif berbasis Canva mampu meningkatkan motivasi belajar siswa kelas XB SMA Negeri 5 Kupang tahun pelajaran 2023/2024. 
Kata Kunci: Motivasi Belajar, Canva, Pembelajaran Bahasa Indonesia
Peningkatkan Ketrampilan Membaca Siswa Kelas XB SMA Negeri 5 Kupang Melalui Penggunaan Media Wordwall
Authors: Pasos Arnaldo D. S. , Margareta Karus M. , Djerahi Evarista Yulia
Published: October 2024

Abstrak
Tujuan dari penelitian ini adalah untuk meningkatkan kemampuan siswa dalam belajar dan membaca melalui penggunaan media permainan edukatif Wordwall di kelas XB SMA Negeri 5 Kupang. Observasi awal menunjukkan bahwa metode pembelajaran Bahasa Indonesia yang monoton dan tidak interaktif menyebabkan siswa kurang tertarik dan aktif. Penelitian Tindakan Kelas (PTK) digunakan, yang terdiri dari empat fase: perencanaan, pelaksanaan, evaluasi, dan refleksi. Penelitian dilakukan dalam tiga siklus, dan setiap siklus melibatkan berbagai upaya untuk meningkatkan proses pembelajaran. Hasil penelitian menunjukkan bahwa siswa meningkatkan keterampilan membaca mereka dan keterlibatan belajar mereka setelah menggunakan media Wordwall. Hasil observasi dan wawancara menunjukkan bahwa siswa lebih tertarik dan terlibat dalam pembelajaran, dan nilai asesmen formatif meningkat. Oleh karena itu, siswa di SMA Negeri 5 Kupang meningkatkan kemampuan membaca dan belajar dengan menggunakan media Wordwall.
Kata Kunci: keterampilan membaca, Wordwall, media permainan edukatif, Penelitian Tindakan Kelas, SMA Negeri 5 Kupang
Wordwall, Media Pembelajaran, Pe Penerapan Wordwall Sebagai Media Pembelajaran Interaktif dalam Meningkatkan Pemahaman Peserta Didik Kelas XI E SMA Negeri 1 Kupang
Authors: Nesimnasi Ance Delwanti , Robot Marselus , Christina Frederika
Published: October 2024

ABSTRAK
Tujuan dari penelitian ini yaitu untuk mengevaluasi peningkatan pemahaman peserta di kelas XI E SMA Negeri 1 Kupang dengan menggunakan Wordwall sebagai media pembelajaran interaktif. Penelitian  tindakan kelas ini terdiri dari dua siklus yang meliputi empat tahap utama: perencanaan, pelaksanaan, pengamatan, dan refleksi. Temuan penelitian ini adalah sebagai berikut: a) Pada siklus I, penggunaan aplikasi Wordwall menunjukkan bahwa sebagian besar siswa belum mencapai standar ketuntasan dalam pembelajaran. Rata-rata nilai hasil pembelajaran mencapai 75% dengan tingkat ketuntasan klasikal sebesar 60%. b) Pada siklus II, terjadi peningkatan aktivitas siswa di mana 33 siswa menunjukkan partisipasi aktif dalam proses pembelajaran dengan rata-rata skor 90%, yang dikategorikan sebagai tingkat klasikal baik sekali. Selain itu, 33 siswa lainnya mencapai rata-rata skor 85% dan 90%. Dalam menjawab pertanyaan di Wordwall, siswa juga menunjukkan tingkat keaktifan yang baik dengan rata-rata skor 75%, melibatkan 28 siswa. Dengan demikian, implementasi Wordwall mampu meningkatkan minat belajar siswa, khususnya dalam memahami materi karya ilmiah di Kelas XI E SMA Negeri 1 Kupang.
Kata Kunci: Wordwall, Media Pembelajaran, Peningkatan Pemahaman, Penelitian Tindakan Kelas
Penggunaan Metode Pembelajaran Talking Stick untuk Meningkatkan Keterampilan Berbicara Peserta Didik Kelas X G SMA Negeri 5 Kupang
Authors: Jelalu Quirinus , Djuli Labu , Lakapu Imanuel
Published: October 2024

Dengan menggunakan model Kemmis dan Taggart, penelitian ini bertujuan untuk mengetahui seberapa besar pengaruh penggunaan metode pembelajaran Talking Stick terhadap keterampilan berbicara peserta didik Kelas X G di SMA Negeri 5 Kupang. Penelitian tindakan kelas terdiri dari 4 fase, yaitu perecanaan, pelaksanaan, observasi, dan refleksi. Penelitian ini dilakukan terhadap 36 peserta didik Kelas X G di SMA Negeri 5 Kupang. Semua data dikumpulkan melalui wawancara, tes tertulis, dan observasi. Hasil penelitian menunjukan adanya peningkatan signifikan dalam mengidentifikasi tema dan suasana dalam puisi seteleh penggunaan metode Talking Stick. Pada kondisi awal, keterampilan berbicara peserta didik ≤50%, kegiatan siklus 1 mencapai rata-rata 55% dengan peningkatan sebesar 5%. Oleh karena itu, peneliti melakukan perbaikan pada kegiatan siklus 2 mencapai rata-rata 85%. Berdasarkan tabel perbandingan hasil belajar menunjukan bahwa adanya peningkatan setelah melakukan kegiatan siklus 1 diperoleh Ketuntasan Belajar Klasikal (KBK) sebesar 62%. Oleh karena itu, maka peneliti melakukan perbaikan pada kegiatan siklus 2 diperoleh Ketuntasan Belajar Klasikal (KBK) sebesar 100% dengan peningkatan sebesar 38% dari siklus 2. Peningkatan hasil belajar dipengaruhi oleh penggunaan metode Talking Stick yang sangat cocok diterapkan pada Mata Pelajaran Bahasa Indonesia, sehingga mudah memahami materi  dan dapat meningkatkan keterampilan berbicara, menciptakan keaktifan dalam aspek intelektual, sikap, dan keterampilan motorik peserta didik.
 Kata Kunci: Metode Talking Stick, Keterampilan Berbicara, Puisi, Penelitian Tindakan Kelas.
PENERAPAN MODEL PROBLEM BASED LEARNING UNTUK MENINGKATKAN KETERAMPILAN BERBICARA SISWA KELAS X SMAN 3 KUPANG
Authors: Bulla Erna Theresia , Djawa Alex , Ga Susana
Published: October 2024

Abstrak
Penelitian ini bertujuan untuk meningkatkan keterampilan berbicara siswa kelas X melalui penerapan model pembelajaran Problem Based Learning (PBL). Fokus permasalahan yang terjadi dalam pembelajaran Bahasa Indonesia di SMAN 3 Kupang adalah kurangnya minat dan partisipasi aktif siswa, yang menyebabkan hasil belajar di bawah Kriteria Ketuntasan Minimal (KKM). Penelitian ini menggunakan metode Penelitian Tindakan Kelas (PTK) dengan dua siklus, meliputi tahap perencanaan, tindakan, observasi, dan refleksi. Pada siklus I, hasil belajar menunjukkan nilai rata-rata 71,67 dengan ketuntasan klasikal 50%, dimana hanya 18 dari 36 siswa yang mencapai nilai ≥75. Setelah perbaikan pada siklus II, nilai rata-rata meningkat menjadi 90 dengan ketuntasan klasikal 92%, di mana 33 dari 36 siswa mencapai nilai ≥75. Peningkatan ini menunjukkan bahwa penerapan PBL efektif dalam meningkatkan keterampilan berbicara siswa. Partisipasi dan keterlibatan aktif siswa dalam proses pembelajaran juga meningkat secara signifikan. Penelitian ini menyimpulkan bahwa model Problem Based Learning berbantuan LKPD dapat meningkatkan hasil belajar Bahasa Indonesia dan keterampilan berbicara siswa kelas X di SMAN 3 Kupang.
Kata kunci: LKPD, Teks Negosiasi, Problem Based Learning (PBL)
PENINGKATAN MINAT BELAJAR PADA PEMBELAJARAN BAHASA INDONESIA MELALUI PENGGUNAAN MEDIA (Autosaved)
Authors: Wulandari Sri , Nitbani Samuel H. , Muli Margarita T. B.
Published: October 2024

Tujuan penelitian ini untuk 1) penggunaan media mading amplop guna meningkatan minat membaca dalam pembelajaran Bahasa Indonesia, 2)  pembuktian menggunakan media mading amplop dapat mendorong minat membaca siswa. Penelitian ini adalah Penelitian Tindakan Kelas di SMAN 3 Kupang, kelas XA semester 1 tahun pelajaran 2023/ 2024 dilaksanakan dari September 2023 s.d Februari 2024. Teknik pengumpulan data yang digunakan adalah wawancara mendalam, observasi, dan dokumentasi. Teknik analisis data menggunakan analisis kritis dan analisis interaktif. Hasil penelitian menunjukkan bahwa nilai terendah pada siklus I adalah yang terendah 65, tertinggi 85, rata-rata 72, jumlah siswa yang tuntas 23 dan yang belum 13. Pada siklus II, nilai terendah 68, tertinggi 92, rata- rata 78, siswa yang tuntas 33 dan yang belum 3 anak. Berdasarkan hasil penggunaan media mading amplop tersebut dapat dikatakan bahwa pembelajaran dengan strategi ini hendaknya bisa dikembangkan dan dilaksanakan dalam proses pembelajaran Bahasa Indonesia, khususnya dalam meningkatkan keterampilan membaca siswa.
 
Kata kunci: minat belajar, Bahasa Indonesia, media mading amplop
IMPLEMENTATION OF THE CRT APPROACH ASSISTED BY WORDWALL MEDIA IN IMPROVING THE POETRY WRITING SKILLS OF CLASS XE STUDENTS OF SMA NEGERI 5 KUPANG
Authors: Dai Ndiha Diandra , Djuli Labu , Lakapu Emanuel
Published: October 2024

Abstract
The aim of this research is to improve students' ability to write in class using a Culturally Responsive Teaching (CRT) approach with the help of Wordwall media. The CRT approach is a learning method used to appreciate and respect the diversity of students. Wherever, this approach will increase the effectiveness of writing poetry in the learning process. The method used in this research is called Classroom Action Research (PTK), which was carried out over two semesters of lectures. This research consists of four stages: preparation, action, evaluation, and reflection. The object of the research was 36 students in class X E of SMA Negeri 5 Kupang. The data collection methods used are observation and checking learning tasks. Qualitative descriptive analysis was used in collecting research data. The results of this research were obtained by applying the Culturally Responsive Teaching (CRT) approach.
 
Keywords: Culturally Responsive Teaching (CRT), Writing poetry, and Wordwall.
MELKI DAN JOHNY CALON GUBERNUR DAN WAKIL GUBERNUR NUSA TENGGARA TIMUR
Authors: Djawa Alex
Published: October 2024

Pemilihan gubernur/wakil gubernur, bupati/wakil bupati, wali kota/wakil wali kota akan berlangsung pada tanggal 27 November yang akan datang. Para kandidat yang bertarung dan bersaing terus melakukan berbagai pendekatan dan aktivitas untuk meyakinkan masyarakat untuk memilih mereka. Salah satu cara mereka mendekatkan diri pada masyarakat adalah dengan menyampaikan pesan dan informasi melalui baliho. Pendekatan dengan menggunakan sarana ini kelihatannya lebih efektif untuk menyampaikan ketokohan dan pikiran mereka kepada masyarakat. Pendekatan ini juga dianggap lebih murah dibandingkan dengan pendekatan yang lain.
Postharvest conservation of ‘Prata’ bananas with a starch-based coating derived from straw potato processing
Authors: Sancho Bruno Jorge Torres Pinto , Oliveira Aléxia da Costa , Vilas Boas Brígida Monteiro
Published: October 2024

In the production process of straw potatoes, a large amount of residual starch from the washing stage can be used to formulate biodegradable materials. The present study evaluated the use of a coating made from this residual starch in the postharvest conservation of ‘Prata’ bananas during storage (23.5±0.5ºC and 73.3±2.4% RH). The following postharvest treatments were performed: 1) control; 2) immersion in a solution (82% residual starch, 13% glycerol, and 5% acetic acid at 4.5% w/v). Samples were characterized every two days for mass loss, L* value, hue angle, chroma, firmness, soluble solids, titratable acidity, and pH. The coated fruits showed greater mass loss compared to the control from the fourth day onwards, without changes in the appearance and quality of the banana. A lower hue angle and higher L* value and chroma were observed in the control fruits compared to the coated ones. A more pronounced linear reduction in firmness was observed in the control fruits compared to the coated bananas. The coating was effective in reducing the soluble solids content of the fruits. Therefore, the starch, acetic acid, and glycerol coating applied to the banana peel proved to be effective in delaying the ripening of the fruits for 8 days.
Analysis of oral and maxillofacial injuries in women victims of domestic violence
Authors: Lemos Isabella Maria da Silva , Da Silva Maria Luany , Da Silva Robinson Felipe Santana , Bushatsky Magaly , Gomes Ana Claudia Amorim
Published: October 2024

The study analyzed the profile of oral and maxillofacial injuries and the vulnerability factors of women victims of domestic violence, based on 662 reports made available by the 1st Specialized Police Station for Women's Assistance in Pernambuco. This is a secondary database research using a quantitative and qualitative approach to the variables present in the reports, from January 2020 to January 2024. The most affected age group was 21 to 30 years old, and the Most victims were single (40.12%), with a significant p-value. The main aggressors were male partners and ex-partners (95.30%). The most common injuries were caused by relevant instruments, equipment in the orbital and labial regions, and settings such as levels. In the analysis of generalized linear models (GLMs), no statistical differences were discovered regarding the level of detail in the reports produced by experts of both sexes (Pearson χ2= 432). The study highlights the vulnerability of young and single women to violence from intimate partners, highlighting the complexity and challenges of gender-based violence in society.
Exploring health and depression in quilombola communities: a look at symptoms, negative self-rating, and intervention opportunities
Authors: Valentim Livia de Aguiar , Quaresma Tatiane Costa , Fernandes Franciane de Paula , Oliveira Sheyla Mara Silva de
Published: October 2024

This study aimed to explore health and depression in quilombola communities, focusing on health symptoms, negative self-assessment, and intervention opportunities. A cross-sectional survey was conducted in two riverside quilombola communities in the Amazon region of Pará, with a sample of 63 households, totaling 256 people. The collected data were analyzed using statistical methods. Commonly reported physical symptoms included frequent headaches (42.86%), poor appetite (19.05%), and back pain (48.33%). Emotional symptoms such as feeling nervous, tense, or worried (55.56%) and sadness (30.16%) were also prevalent, and although not the focus of the study, the mention of suicidal ideation drew attention. The study revealed a significant association between feeling nervous, headaches, and negative self-rated health. These findings reinforce the need for interventions tailored to the specific needs of Quilombolas to promote their mental and physical health. In conclusion, understanding the profile and health conditions of quilombola communities is essential for the development of effective health programs. Health promotion in these communities requires integrated actions, cultural sensitivity, and community participation.
Legal abortion performed by pre-teens: primary health care workers’ perception and knowledge
Authors: Ataide Danielle Lourenço , Da Silva Zembrzuski Isabela , Caetano de Lima Isamara , André Honorato Franzoi Mariana , Serrão Peres Teixeira Bethania
Published: October 2024

This study aims to put into evidence Distrito Federal’s (DF) primary health care workers’ perception and knowledge regarding legal abortion, performed by 10 to 14 year old pre-teens. This is a descriptive study of qualitative approach. Semi-structured interviews were done to 17 primary health care workers currently working in Basic Health Units (BHU) of DF, under contract with DF’s Secretariat of Health. This article showed the worker’s lack of knowledge regarding legal abortion and specific care towards the age group, copying their knowledge regarding adults without adaptation for pre-teens. Workers’ personal beliefs influenced their practice and their choice to correctly inform Health Care users.
The impact of prehospital use of ultrasound by EMS: Diagnostic and interventional benefits
Authors: Al-Jasser Saleh AbdulLatif , AL Khathami Mohammed Mesfer Musaed , Alenazi Ahmed Mufleh , Alareefi Hind Saad , Alomran Razan Walid , Abdullah Alkhathami Bandar Mohammad , Dobayan Alenazi Nawaf Subhi , Mohammed Alhazmi Fares Khalid , Alrashidi Abdulaziz Ahmad , Isa Gaddourah Ahmad Mohammed , Alrakhimy Hamad Dafalh
Published: October 2024

Background: Prehospital care, particularly for trauma patients, is complex due to the unpredictable environments in which emergency medical services (EMS) operate. The integration of prehospital ultrasound (PHUS) offers potential improvements in trauma management, a leading cause of mortality among younger populations in the U.S. Aim: This systematic review evaluates the application of PHUS in trauma management, focusing on its diagnostic and interventional benefits when utilized by different provider types, including EMS professionals, physicians, and mixed teams. Methods: A comprehensive literature search was conducted across multiple databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The analysis used a PICO framework to compare ultrasound-assisted care with standard practices. A total of 16 studies were included for evaluation, assessing various ultrasound protocols and their impact on treatment decisions and outcomes. Results: The review highlighted significant variability in sensitivity and diagnostic accuracy among studies using PHUS. While the majority of studies involved physician-operated ultrasounds, limited research focused specifically on EMS professionals. Notably, the findings indicated that PHUS positively influenced treatment and transport decisions, although no studies directly addressed its effect on mortality rates.
Cancer prevention and early detection: Emerging technologies and interventions
Authors: Alenezi Abdulrahman Ramadhan , Alanazi Mohammed Ayyat , Aldhafeeri Fares Saqir , Alotaibi Bader Naif , Alshamri Ahmed Saer , Alenezi Meshal Falah , Al-Jasser Saleh AbdulLatif , Alowaidan ‏Salwa Rashed , Nashar ‏Reem Mahmoud , Algfari Sultan Mohammed
Published: October 2024

Background: Precision oncology is transforming early cancer detection among average-risk individuals. Advances in next-generation sequencing have led to significant insights into the cancer genome and the identification of biomarkers to improve early detection. Aim: This article examines emerging technologies and interventions in cancer prevention and early detection, focusing on the latest advancements in screening methodologies. Methods: The review analyzes various single- and multi-cancer early detection tests, discussing their methodologies, biomarker identification, clinical trial results, and the challenges associated with current screening approaches. Results: Innovative tests, such as multi-cancer early detection (MCED) assays, have shown superior sensitivity compared to traditional methods by identifying circulating tumor DNA (ctDNA) before symptoms arise. While promising, these technologies face challenges, including the potential for false positives and negatives, overdiagnosis, and disparities in access to testing. Conclusion: Emerging technologies in cancer detection hold great potential to revolutionize screening practices. However, careful consideration of their clinical utility and potential harms is necessary to ensure equitable access and effective implementation.
Design of Variation Tolerant Near Threshold Processor Using Artificial Ecosystem Optimizer with Hybrid Deep Learning
Authors: Gundaala Raghu , K Selvakumarasamy
Published: October 2024

Recently, several applications of data mining and pattern recognition require statistical signal processing (SP) to be used and machine learning (ML) techniques for processing massive data volumes in energy-constrained contexts. It is developing interest in executing difficult ML techniques like convolutional neural network (CNN) on lesser power embedding environments to allow on-device learning and inference. Several of these platforms are that utilized as lower power sensor nodes with lower to medium throughput conditions. Near threshold processor (NT) proposals are appropriate for these applications where as affected by a vital enhancement in variants. This research offers an Artificial Ecosystem Optimizer with Hybrid Deep Learning for Variation-Tolerant Near-Threshold Processor (AEOHDL-VTNT). The inference of embedded systems at the network edge serves as the foundation for the AEOHDL-VTNT approach that is being discussed. In the described AEOHDL-VTNT approach involves two primary processes: HDL-based VTNT design and hyper parameter tweaking. Initially, the HDL model is used to develop the VTNT. Next, in the second step, the AEO method is used for hyper parameter tweaking of the HDL model, which improves the HDL's overall performance. A number of simulations were carried out to show how the AEOHDL-VTNT approach improved performance. The simulation results showed that the AEOHDL-VTNT approach outperformed other models.
Robust Approach of Automatic Number Plate Recognition System using Deep CNN
Authors: S Mahalakshmi , J Dheeba
Published: October 2024

Automatic License plate / Number plate / Registration plate recognition is recognized as an automation which evolved mostly based on image processing techniques. It has been extensively used in recognizing vehicles in applications such as red-light enforcement, over speeding, parking control, toll collection. The main objective of the paper is to identify the most well-planned way to identify the registration plate from the digital image (gained from the camera) and recognize with high accuracy. ANPR is employed to localize the license plates, segment each character and extract the text from the license plate and then recognition each character successfully. The main issue of registration plate recognition rely on the accuracy rate. Advancement in deep learning methods has improved the ability to solve visual recognition task. Henceforth using deep Convolutional Neural Networks (DCNN) will intensification the precision, recall, processing speed, reduce the error rate in solving the ANPR process. The use of deep learning CNN helps in identification of license plates of any aspect ratio which would work good for places like India where license plate style differs remarkably. The CNNs are up skilled and balanced so that they are strong under various states like variations in pose, lighting, occlusion etc. In our dataset we have used 100 images to train our network and obtained an 99% accuracy for plate localization and 93% accuracy for recognition.
Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection
Authors: B Rupa Devi , G Suseela , Painam Ranjith Kumar , Swetha Thammisetty , G Suryanarayana , K Reddy Madhavi
Published: October 2024

Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.
Enhancing International Logistics and Supply Chain Management: Deep Learning Strategies for Enhanced Route Planning and Warehouse Optimization
Authors: Tian Shiyu , Luo Jianghua
Published: October 2024

Logistics and Supply Chain Management (SCM) are both key areas in modern industry and commerce which need better route planning and warehouse optimization. Traditional methods that are in practice have often fall short in of addressing the dynamic complexities of modern logistics which results in inefficient travel times, fuel consumption, and space utilization. To counter these limitations this study introduces an integrated model that combines Long Short-Term Memory (LSTM) networks, Radial Basis Function Neural Networks (RBNN), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for route planning and warehouse optimization. The proposed model employ LSTM to predict traffic patterns and RBNN to optimize space utilization in warehouse. The NSGA-II model is then utilized for multi-objective optimization of minimizing travel times and maximizing warehouse space utilization. In experiment analysis the proposed model achieved the highest accuracy and least variability in predictions, with mean MAE, RMSE, and MAPE values of 0.57, 1.12, and 5.9%, respectively.
Classification of an Individual's Vaccination Status Using Ensemble Hard Voting Classifier
Authors: S Saranya , R Agusthiyar
Published: October 2024

Vaccination is a proactive medical immunization procedure where an inactivated form of a disease-causing agent (such as a virus) is administered to boost the body's defense systems. Efficient management of vaccination status is crucial in healthcare management, disease eradication, community immunity ("herd immunity"), disease prevention, and global health security. Ensuring precise monitoring and validation of an individual's vaccination status is indispensable, especially in the context of emerging diseases and epidemics. This study evaluates the likelihood of individuals obtaining vaccination for the H1N1 virus and the seasonal flu vaccine. Ensemble methods combine the predictions of multiple base classifiers to enhance overall performance. One such method, the hard voting classifier, aggregates the votes from each base classifier and selects the class with the majority vote as the final prediction. This approach leverages the strengths of different classifiers, reducing the risk of individual model biases and improving generalization using metrics such as precision, recall, accuracy, and F1-score are employed to assess the system's effectiveness. The results demonstrate how data-driven methods can address population wellness and improve vaccination rates using an ensemble method. The proposed ensemble hard voting classifier achieved accuracies of 0.905 and 0.907 on the H1N1 and seasonal vaccine datasets, respectively. Using an ensemble approach like the hard voting classifier enhances prediction accuracy and robustness, ultimately leading to better decision making in public health initiatives.
A Novel DC GCN with Attention Mechanism for Accurate Near Duplicate Video Data Cleaning
Authors: D Jayalakshmi , R Hemavathi , L Murali , Duraisamy Baskar , Murthy Banda SNV Ramana , Sunita
Published: October 2024

There has been a steady emergence of nearly identical recordings in the last several decades, thanks to the exponential development of video data. The use of regular videos has been impacted by data quality difficulties produced by near-duplicate movies, which are becoming increasingly noticeable. While there has been progress in the field of near-duplicate video detection, there is still no automated merging method for video data characterised by high-dimensional features. As a result, it is challenging to automatically clean near-duplicate videos in advance video dataset data quality. Research on removing near-duplicate video data is still in its early stages. The precision of near-duplicate video data cleaning is severely compromised by the delicate issues of video data organization besides initial clustering centres in the current research, which arise when the previous distribution is unknown. In tackle these problems, we offer a new kind of Graph Convolutional Neural Network (GCN) that uses dense influences and a categorization attention mechanism. Deeply connected graph convolutional networks (DC-GCNs) learn about faraway nodes by making GCNs deeper. By using dense connections, the DC-GCN is able to multiplex the small-scale features of shallow layers and generate features at diverse scales. Finally, an attention mechanism is incorporated to aid in feature combination and importance determination. Sparrow Search Optimisation Algorithm (SSA) is used to pick the parameters of the given model in the most optimal way. In the end, experiments are carried out using a coal mining video dataset and a widely known dataset called CC_WEB_VIDEO. The simulation findings show that the suggested strategy performs better than certain previous studies.
Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture
Authors: A Hussain , P Balaji Srikaanth
Published: October 2024

India has a vast number of inhabitants and the main food source distribution is from agriculture. Agricultural lands are being demolished generally owing to plant and crop illnesses. The detection of plant diseases by using image processing models can aid agriculturalists in defending the farming area from damaging or affecting it. Paddy is the main harvest worldwide. Early recognition of the paddy diseases at dissimilar phases of development is very vital in paddy production. However, the present manual technique in identifying and classifying paddy diseases needs a very educated farmer and is time-consuming. Deep learning (DL) is an effectual research area in the classification of agriculture patterns where it can efficiently solve the problems of diseases identification. Therefore, the articles focus on the design and expansion of Deep Learning based Crested Porcupine Optimizer for the Detection and Classification of Paddy Leaf Diseases (DLCPO-DCPLD) method for Sustainable Agriculture. The main aim of the DLCPO-DCPLD method use DL method for the recognition and identification of rice plant leaf diseases. To accomplish this, the DLCPO-DCPLD technique performs the image pre-processing using Median Filtering (MF) to recover the excellence of the input frames. Next, the ConvNeXt-L method is applied for extraction of feature vectors from the pre-processed images. Also, the Conditional Variational Autoencoder (CVAE) model is utilized for the automated classification of Paddy Leaf diseases. Eventually, the hyperparameter tuning of the CVAE technique is accomplished by implementing the Crested Porcupine Optimizer (CPO) technique. To safeguard the enhanced predictive results of the DLCPO-DCPLD method, a sequence of experimentations is implemented on the benchmark dataset. The experimental validation of the DLCPO-DCPLD method portrayed a superior accuracy value of 99.12% over existing approaches.
Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G Networks
Authors: R Karthikeyan , S Usha , V Dineshbabu , R Jeena , R Anitha Govindaram , A Jegatheesan
Published: October 2024

When it comes to clinical applications, sensor-based human activity recognition (HAR) is invaluable, and numerous machine learning algorithms have effectively used to obtain excellent presentation. Using a variety of on-body sensors, these systems attempt to ascertain the subject's status relative to their immediate surroundings. There was a time when feature extraction was done by hand, but now more and more people are using Artificial Neural Networks (ANNs). A number of innovative approaches to HAR have surfaced since the advent of deep learning. Problems arise, however, for sensor-based HAR classification algorithms in today's communication networks. Among these, you can find solutions to problems like deal with complicated and large-scale data signals, extract characteristics from complicated datasets, and meet explainability standards. For complicated 5G networks, these difficulties become even more apparent. In particular, explainability is now critical for the broad use of sensor-based HAR in 5G networks and beyond. The research suggests a classification approach based on path signatures, recurrent signature (ReS), to address these issues. This cutting-edge model employs deep-learning (DL) approaches to circumvent the tedious feature selection challenge. Furthermore, the study investigates how to improve the ReS model's classification accuracy by using graph-based optimisation methods. To test how well the suggested framework worked, to dug deep into the publicly available dataset, which included a separate set of tasks. The paper's empirical results on AReM datasets achieved an average accuracy of 96%.
Precision Offloading in Edge Computing: Leveraging Predictive Model
Authors: T S Suganya , J Saivijayalakshmi , M Karthik , T Keerthana , V Srikanth , U Vidhya
Published: October 2024

The intended effect of the investigation is to provide sophisticated prediction and decision-making models in order to optimize service delivery and improve the Quality of Experience (QoE) for users. This research tackles the problems that are associated with job offloading in edge computing settings. In order to reduce service latency and improve overall performance, the Bi-Directional Long Short-Term Memory (B-LSTM) model is used. This model provides the ability to forecast task creation and server load. In order to accommodate the particular qualities of different devices, the Selective Objective Offloading Decision (SOOD) approach is presented. This method makes use of the TOPSIS methodology to turn server assessment into a decision-making issue that involves several criteria. A considerable increase of 98.4% in user quality of experience is achieved by the SOOD paradigm. In addition, the Rapid Offloading Decision (ROD) model is presented in order to manage unexpected work patterns. This is accomplished by using the log information of surrounding devices, which results in instantaneous and dependable offloading choices. Through the usage of prediction algorithms and selective decision-making, this research gives a complete strategy to improving the efficiency of edge computing. The goal of this technique is to maximize the utilization of servers and the user experience.
State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach
Authors: P Vinoth Kumar , N Selvarani , D Gunapriya , M Batumalay
Published: October 2024

For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.
Analyzing the Impact of Scientific and Technological Innovations on Financial Performance: A PCA-Based Study of an AI Healthcare Startup
Authors: Ghanimi Hayder M A , Painoli Girish Kumar , Contractor Dipenkumar , G N Sowjanya , A Vani Lavanya , Poon Kamal
Published: October 2024

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how several dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. This paper aims to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.
An Efficient Transfer Learning-Based Framework for Health Care Application
Authors: V Pavithra , Srinivasan Uma Shankari , K Sutha , K Saraswathi , Yalawar Mrutyunjaya S , B Sathiya
Published: October 2024

Deep learning has revolutionized healthcare applications, particularly in the diagnosis, treatment, and management of infectious diseases. The main objectives of this investigation are to propose several methods for assessing high-resolution X-ray images with the purpose of identifying the occurrence or not of symptoms associated with pneumonia. The objective of this exam was to identify fixes for these existing problems. Our offering entails a deep learning (DL) technique for detecting chest anomalies using the X-ray modality using the EfficientNet B0 model. In order to make accurate diagnoses of pneumonia, both the EfficientNet B0 and the upgraded CNN model undergo extensive data-driven training. The CNN model that underwent upgrades was determined to be the most effective in this analysis because to its high level of accuracy. The results of our research conclusion are that DL models are capable of monitoring pneumonia's development, increasing diagnostic precision overall and giving patients new optimism for immediate relief.
Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting
Authors: Ayasrah Firas Tayseer , Alsharafa Nabeel S. , S Sivaprakash , B Srinivasarao , Sengan Sudhakar , N Kumaran
Published: October 2024

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.
Peningkatan Penjualan Melalui Digital Marketing Pada Olahan Singkong Thailand Di Desa Talang Nangka
Authors: Anwar Aliyah Dwiriani , Pratama Lucky , Erpin Erpin , Berliana Eliya , Putri Andini Utari , Irmaya Nisa , Ardiwinata Yogie
Published: October 2024

Tujuan dari kegiatan pengabdian ini adalah untuk menggunakan strategi pemasaran digital agar meningkatkan penjualan olahan singkong Thailand di Desa Talang Nangka. Dalam upaya untuk memperluas pilihan produk berbasis singkong, produk olahan singkong Thailand baru-baru ini diperkenalkan kepada masyarakat desa. Kegiatan ini melibatkan UMKM di Desa Talang Nangka. Program ini mencakup pelatihan dan bimbingan dalam penggunaan platform digital, serta e-commerce untuk meningkatkan jangkauan pasar. Kegiatan ini menunjukkan bahwa strategi digital marketing dapat secara signifikan meningkatkan penjualan produk. Selain itu, pelaku UMKM lebih memahami pentingnya kehadiran online untuk kelangsungan bisnis mereka. Jadi, digital marketing adalah alat yang bagus untuk mempromosikan produk lokal dan dapat digunakan secara luas oleh pelaku UMKM di desa lainnya.
Pemberdayaan Masyarakat Melalui Pengembangan Inovasi Produk Turunan Kopi Berbasis Ekonomi Hijau Di Desa Sukamaju
Authors: Nursetiawan Irfan , Yuliani Dini , Haris Eko Prabowo Faizal , Maharani Regina , Karina Seviany Devi , Peni  , Satria Nugraha Fariz
Published: October 2024

Desa Sukamaju Kecamatan Cihaurbeuti Kabupaten Ciamis memiliki potensi besar dalam sektor produk kopi, namun keterbatasan dalam inovasi pengolahan produk turunan kopi menyebabkan belum optimalnya kesejahteraan masyarakat desa. Mitra dalam kegiatan pengabdian ini, yakni BUMDes Maju Mandiri. Pemahaman mitra mengenai konsep ekonomi hijau masih belum dipahami sepenuhnya. Oleh karena itu, program pengabdian masyarakat ini bertujuan untuk memberdayakan masyarakat Desa Sukamaju melalui pengembangan inovasi produk turunan kopi berbasis ekonomi hijau. Kegiatan pengabdian ini dilaksanakan melalui beberapa tahapan, yaitu: (1) sosialisasi konsep ekonomi hijau; (2) pelatihan pembuatan produk turunan kopi; serta (3) pendampingan usaha bagi masyarakat desa. Pendekatan partisipatif diterapkan untuk memastikan keterlibatan aktif masyarakat dalam setiap proses, sehingga mampu mengembangkan keterampilan, pengetahuan, dan kesadaran dalam pelestarian lingkungan. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan masyarakat dalam mengembangkan produk turunan kopi yang ramah lingkungan. Selain itu, kesadaran akan pentingnya praktik ekonomi hijau meningkat, yang diharapkan mampu berkontribusi pada keberlanjutan usaha dan peningkatan kesejahteraan ekonomi masyarakat perdesaan. Digitalisasi pemasaran produk telah dilakukan dan inovasi produk kopi sachet atau instant menjadi produk baru dari hasil kegiatan Pengabdian Kepada Masyarakat skema Pemberdayaan Berbasis Masyarakat Kemendikbudristek tahun 2024.
Pelatihan dan Pendampingan Pembuatan Produk Turunan Desa Sentra Kain Kulit Kayu Desa Onondowa
Authors: B M Rusli , Hidayat Wahyu , Al Anshori Fitrah , Seriyanti Seriyanti
Published: October 2024

"Desa Sentra Kain Kulit Kayu" merupakan kolaborasi strategis antara Universitas Cokroaminoto Palopo dengan Desa Onondowa. Kegiatan ini adalah bagian dari rangkaian program membangun desa dengan fokus pada pengembangan ekonomi lokal melalui peningkatan industri kerajinan kain kulit kayu. Tujuan dari program ini ialah menjadikan Desa Onondowa sebagai salah satu sentra kain kulit kayu yang produktif dan berdaya saing, baik di tingkat local, nasional, hingga internasional. Tahapan pelaksanaan solusi kegiatan PDB Desa Sentra Kain Kulit Kayu dilaksanakan melalui 5 pendekatan pelaksanaan yaitu Sosialisasi, Pelatihan, Penerapan Teknologi, Pendampingan & Evaluasi, dan Keberlanjutan Program. Setelah mengikuti kegiatan pelatihan dan pendampingan. Mitra saat ini sudah dapat menghasilkan produk turunan berupa tas, topi, selendang, gantungan kunci, tas handphone yang berbahan dasar kain kulit kayu. Manfaat yang didapatkan dari kegiatan ini yaitu meningkatnya pendapatan mitra dari hasil penjualan dan penyewaan produk turunan kain kulit kayu. Kesimpulan dari kegiatan ini yaitu mitra mengalami peningkatan omset dan pendapatan setelah mengikuti kegiatan pelatihan dan pendampingan dari tim pelaksana.
Mechanical and physical properties of sand concretes reinforced with date palm fibers (Fibrillum and Leaflets)
Authors: Gadri Karima , Jaradat Oday Zaher , Izemmouren Ouarda
Published: October 2024

In the construction sector, there is increasing interest in expanding the use of eco-friendly materials by leveraging natural resources and recycled materials. Among the most abundant natural fibers in several countries, including Algeria, are date palm (DP) fibers. This paper explores the use of date palm fibers as reinforcement in sand concrete (SC), aiming to develop a cost-effective and environmentally friendly material. Without any prior treatment, the study incorporates two types of (DP) fibers: leaflets (LF) and fibrillium (FB) into sand concrete. These fibers were incorporated at three different percentages of the total dry mix weight: 0.5%, 1%, and 1.5%. For each percentage, tests were carried out to assessflow, density, compressive strength, flexural strength, and water absorption by capillarity and immersion. The findings indicate that sand concrete (SC) reinforced with 0.5% fibrillium fibers exhibits sufficient mechanical properties for practical applications. Additionally, the paper suggests avenues for future research in this area.