bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the global competence and experience of undergraduates
Authors: Zhao Xiantong , Yi Hang , Lai Qian , Xu Renying
Published: January 2025

In response to the growing demand for higher education to cultivate global talent amid contemporary global developments, this study investigates the status of Chinese universities’ efforts in fostering global competency among undergraduates. Addressing a research gap in understanding the relationship between students’ global experiences and their global competency, the study surveyed 3,513 undergraduates, comparing outcomes across different types of universities and analyzing key factors contributing to global competency. Using regression analysis, the study reveals that undergraduates from Project 985 universities scored significantly higher in global competency and engaged more frequently in globalization-related activities compared to their peers at nonelite institutions. Global experiences, including participation in internationalization-focused courses, academic events with global themes, social interactions with overseas students, and maintaining connections with foreign peers, were found to significantly enhance students’ global competency. These findings highlight the need to balance internationalization efforts across both elite and nonelite universities while emphasizing the importance of enriching students’ academic and communicative experiences. The study underscores the critical role of equitable and diverse internationalization strategies in developing global talent, offering actionable insights for policymakers and educators aiming to strengthen global competency across higher education institutions.
Keywords：Global competence; global experience; undergraduates
Exploring the differential effects of pre-task planning conditions on intermediate EFL learners’ oral production
Authors: Khoram Alireza
Published: January 2025

Task planning plays a crucial role in task-based language teaching (TBLT) and second language (L2) acquisition. However, the effects of different classroom organizations during pre-task planning on task performance remain unclear. This study examines how various planning approaches impact intermediate English as a Foreign Language (EFL) learners' oral performance in terms of complexity, accuracy, and fluency. Sixty intermediate EFL students were randomly assigned to three experimental groups and one control group. The control group performed a decision-making task without planning, while the experimental groups engaged in pre-task planning under three conditions: individual planning, pair work, and guided planning (using content and language guidelines). Findings revealed that individual and pair work planning significantly improved fluency compared to guided planning. Individual planners achieved the highest complexity levels, followed by pair work and guided planners, while guided planning resulted in better accuracy than the other approaches. These results highlight the varying effects of classroom structures on different aspects of L2 performance. Tailoring pre-task planning methods to instructional goals can optimize specific language performance dimensions. The study emphasizes the need for flexible planning strategies to support diverse learner needs and pedagogical objectives in TBLT.
Keywords: Guided planning; individual planning; pair planning; task planning
Pengaruh Struktur Modal dan Profitabilitas terhadap Harga Saham Perusahaan
Authors: Dyah Puspa Rahmiati  , Maya Panorama  , Deky Anwar
Published: January 2025

Penelitian ini bertujuan untuk menganalisis pengaruh dari pervariabel yaitu pengaruh struktur modal dan profitabilitas terhadap harga saham perusahaan yang terdaftar pada Jakarta Islamic Index (JII) pada tahun 2016-2019. Metode pengambilan data yang digunakan adalah kuantitatif dengan metode purposive sampling, dimana peneliti menyeleksi beberapa data yang akan diteliti lebih lanjut. Populasi dalam penelitian ini yaitu seluruh perusahaan yang terdaftar di JII, sampel yang di ambil untuk penelitian ini berjumlah 10 perusahaan. Sedangkan metode analisis yang digunakan pada penelitian ini yaitu metode regresi linier berganda dan uji asumsi klasik. Hasil penelitian ini menunjukkan hasil Pengaruh struktur modal dan profitabilitas terhadap harga saham perusahaan yang terdaftar pada Jakarta Islamic Index (JII) pada tahun 2016-2019.
Penerapan Metode Demonstrasi dalam Meningkatkan Minat Belajar Siswa di Era Generasi Z
Authors: Winata Ari , Rahmad Salahuddin Tri Putra
Published: January 2025

Penelitian ini bertujuan untuk mengevaluasi dan memahami implementasi model pembelajaran demonstrasi dalam mata pelajaran Pendidikan Agama Islam (PAI) di SMP Muhammadiyah 1 Sidoarjo, khususnya dalam meningkatkan minat belajar siswa. Penelitian dilakukan menggunakan metode deskriptif kualitatif dengan subjek penelitian yang melibatkan guru PAI. Data dikumpulkan melalui wawancara mendalam dan observasi. Analisis data dilakukan secara sistematis melalui tahapan reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa guru PAI telah menerapkan model pembelajaran demonstrasi sesuai dengan pedoman Kementerian Pendidikan dan Kebudayaan. Implementasi model ini dilakukan melalui praktik langsung dan wawancara dengan peserta didik, yang memberikan pengalaman belajar interaktif dan menarik. Selain itu, model pembelajaran ini terbukti efektif dalam meningkatkan minat belajar siswa karena memungkinkan mereka untuk lebih aktif terlibat dalam proses pembelajaran dan memahami konsep keagamaan secara praktis. Guru dan kepala sekolah mengapresiasi penerapan model ini karena dinilai relevan dengan kebutuhan pembelajaran di era globalisasi, yang menuntut keterampilan berbasis nilai dan keimanan. Kesimpulannya, model pembelajaran demonstrasi merupakan pendekatan yang efektif dalam mata pelajaran PAI, karena mampu mengintegrasikan nilai-nilai spiritual, penguasaan materi, serta keterampilan praktis, sehingga membentuk siswa yang berkarakter dan berprestasi sesuai tuntunan ajaran Islam.
Peran Guru dalam Mengembangkan Keaktifan Belajar Siswa di Full Day School
Authors: Hakiki Muhammad Ilham , Anita Puji Astutik
Published: January 2025

Penelitian ini bertujuan untuk mengetahui sistem pembelajaran full day school dan peran guru dalam mengembangkan keaktifan belajar siswa di SD Muhammadiyah 1 Driyorejo. Metode yang digunakan dalam penelitian ini adalah metode kualitatif, metode kualitatif berpusat pada observasi yang mendalam. Hasil penelitian menunjukkan bahwa penerapan sistem pembelajaran full day school dapat meminimalisir pengawasan orang tua yang bekerja, dan siswa dapat mengembangkan minat, bakat, dan potensinya di sekolah. Seorang guru harus profesional dan berperan penting dalam mendidik agar dapat mengembangkan keaktifan belajar siswa dan mengembangkan potensi dirinya baik di dalam kelas maupun diluar kelas, terlebih sistem pembelajaran full day school dimana siswa cenderung mudah merasa bosan ketika lama berada dikelas, Untuk megatasi hal tersebut guru dapat menggunakan strategi, metode, dan media pembelajaran yang dapat dipahami oleh siswa dengan mudah.
Integrasi Karakter Kedisiplinan dalam Kegiatan Hizbul Wathan Sebagai Aktualisasi Akhlak Mulia
Authors: Ristyawanda Auwinah , Anita Puji Astutik
Published: January 2025

Penelitian ini bertujuan untuk mengkaji integrasi pendidikan karakter kedisiplinan dalam kegiatan Hizbul Wathan (HW) sebagai upaya mengaktualisasi akhlak mulia pada pandu Hizbul Wathan di SMA Muhammadiyah 4 Porong. Metode penelitian yang digunakan adalah pendekatan kualitatif deskriptif dengan teknik pengumpulan data melalui studi pustaka, observasi, dan wawancara kepada waka kesiswaan, pembina dan anggota HW. Data dianalisis dengan teknik analisis naratif. Hasil penelitian menunjukkan bahwa kegiatan HW efektif dalam menanamkan karakter kedisiplinan melalui pembelajaran berbasis pengalaman. Aktivitas seperti latihan baris-berbaris, penjelajahan, permainan edukatif, dan tugas kelompok membantu peserta didik mempraktikkan kedisiplinan, kerja sama, dan tanggung jawab secara langsung. Selain itu, kegiatan ini juga mendukung perkembangan akhlak mulia seperti kejujuran, kepedulian sosial, dan sikap saling menghargai dalam lingkungan yang inklusif dan kolaboratif. Kesimpulannya, integrasi pendidikan karakter kedisiplinan dalam kegiatan HW berkontribusi signifikan dalam membentuk peserta didik yang tidak hanya berdisiplin tinggi tetapi juga berakhlak mulia. Sinergi antara pendidikan formal dan kegiatan kepanduan menjadi solusi strategis dalam membangun generasi berkarakter kuat, berintegritas, dan siap menghadapi tantangan kehidupan.
Serbuan Informasi Media Sosial dan Kecenderungan Sikap Keberagamaan Generasi Alpha
Authors: Nabilah Roidatun Nabilah , Haryanto Budi
Published: January 2025

Penelitian ini bertujuan menganalisis faktor-faktor yang mempengaruhi pola penerimaan informasi dan pembentukan sikap keberagamaan pada generasi Alpha. Penelitian ini menggunakan metode systematic literature review. Penelitian ini menyoroti pentingnya keterampilan teknologi dalam pembelajaran agama di era digital, terutama bagi Generasi Alpha. Sekolah perlu membekali siswa dengan kemampuan beradaptasi, pembelajaran seumur hidup, dan keterampilan yang relevan untuk menghadapi tantangan dunia digital. Generasi Alpha, yang gaya belajarnya sangat terkait dengan teknologi, membutuhkan pendekatan pendidikan yang berfokus pada pembelajaran berbasis pengalaman. Pendidikan agama di masa depan akan menekankan penerjemahan informasi, interpretasi, dan penambahan nilai dengan memanfaatkan alat bantu visual, pendengaran, dan kinestetik untuk menciptakan lingkungan belajar yang interaktif dan mendukung hubungan sosial. Tantangan utama adalah mengurangi kesenjangan literasi teknologi antara guru dan siswa, meningkatkan interaksi sosial, dan mengembangkan keterampilan lunak seperti rasa kebersamaan dan kolaborasi. Guru memiliki peran penting dalam menciptakan suasana kelas yang kolaboratif, mendorong berpikir kritis dan kreatif, serta membantu siswa memahami dan mengaplikasikan nilai-nilai agama dalam kehidupan sehari-hari.
Peningkatan Pendapatan melalui Aplikasi Ciblon oleh Pedagang Kaki Lima
Authors: Selviani Linda , Baharudin Fadli Amar , Rivai Mahfud
Published: January 2025

Penelitian ini bertujuan untuk mengetahui strategi inovasi yang digunakan oleh pedagang kaki lima untuk meningkatkan pendapatan, menganalisis efektivitas aplikasi Ciblon (Cilacap Belanja Online) untuk  mendukung pertumbuhan ekonomi lokal dan memberdayakan pedagang kaki lima. Jenis penelitina ini adalah kualitatif dengan pendekatan studi kasus. Teknik sampling pada penelitian ini menggunakan purposive sampling, sedangkan pengumpulan data dilakukan dengan wawancara, observasi, dan diskusi kelompok terfokus (FGD) dengan memberikan kuesioner kepada 10 orang responden. Uji keabsahan data dilakukan dengan triangulasi untuk selanjutnya dilakukan analisis secara mendalam dengan menggunakan analisis data model interaktif dan disajikan secara deskriptif. Hasil penelitian menunjukan bahwa strategi peningkatan pendapatan oleh pedagang kaki lima dilakukan dengan berbagai cara yaitu pengembangan produk, peningkatan kualitas pelayanan, pemasaran, kemitraan, pemanfaatkan platform digital, dan pengembangan aplikasi Ciblon. Selain itu, penelitian ini juga mengidentifikasi kendala seperti kurangnya pengetahuan digital dan terbatasnya perangkat yang memadai menjadi faktor penghambat. Studi ini menyimpulkan bahwa aplikasi Ciblon memiliki potensi besar dalam memberdayakan pedagang kaki lima, namun perlu didukung dengan program pelatihan dan sosialisasi yang lebih intensif sehingga dapat meningkatkan pendapatan dan kesejahteraan mereka.
Strategi Dosen dalam Membangun Sikap Moderasi Beragama pada Mahasiswa
Authors: Putera Muhammad Yuda , Zainap Hartati  , Ali Iskandar Zulkarnain
Published: January 2025

Penelitian ini bertujuan untuk menganalisis strategi dosen serta implikasi dalam membangun sikap moderasi beragama pada mahasiswa Program Studi Pendidikan Agama Islam di IAIN Palangka Raya. Jenis penelitian adalah kualitatif dengan pendekatan studi kasus. Pengumpulan data melalui wawancara mendalam dan dokumen. Selanjutnya data dianalisis dengan analisis kualitatif. Hasil penelitian menunjukkan bahwa dosen menggunakan pendekatan integratif dalam pembelajaran, diskusi kasus nyata, dan evaluasi pemahaman mahasiswa. Implikasi dari penerapan strategi ini adalah meningkatnya sikap keterbukaan dan toleransi di kalangan mahasiswa, serta kemampuan mereka dalam menjembatani perbedaan di masyarakat. Dengan demikian, penelitian ini menyimpulkan bahwa pendidikan moderasi beragama memiliki peran penting dalam membentuk karakter mahasiswa yang lebih inklusif.
Analisis Faktor Pengetahuan Produk dan Promosi terhadap Keputusan Menabung pada Bank Syariah
Authors: Muhammad Daffa Dhiya’ulhaq  , Rika Lidyah
Published: January 2025

Penelitian ini bertujuan untuk mengetahui pengaruh pengetahuan produk, dan promosi terhadap keputusan nasabah menabung pada Bank Sumsel Babel Syariah Kantor Cabang Palembang. Variabel independen dalam penelitian ini yaitu pengetahuan produk (X1), dan promosi (X2), sedangkan variabel dependen dalam penelitian ini adalah keputusan nasabah (Y). Populasi dalam penelitian ini adalah seluruh nasabah Bank Sumsel Babel Syariah Kantor Cabang Palembang. Sampel sebanyak 100 nasabah dengan menggunakan rumus Lemeshow dan menggunakan teknik Sampling insidental. Data yang digunakan adalah data primer. Teknik analisis data pada penelitian ini menggunakan  SPSS, yang terdiri dari uji asumsi klasik, uji regresi linier berganda, dan uji hipotesis. Hasil penelitian ini didapat dari uji t dan uji F yang menunjukkan bahwa variabel, pengetahuan produk, dan promosi  berpengaruh positif dan signifikan terhadap keputusan nasabah secara parsial yaitu variabel Pengetahuan Produk terhadap Keputusan Nasabah Menabung pada Bank Sumsel Babel Kantor Cabang Palembang. Diperoleh nilai coefficients sebesar thitung > ttabel = 4,961> 1,98477 dan Variabel Promosi terhadap Keputusan Nasabah Menabung pada Bank Sumsel Babel Kantor Cabang Palembang diperoleh nilai coefficients sebesar thitung > ttabel = 2,850 > 1,98477 serta uji simultan berpengaruh positif dan signifikan.
Pengaruh Pendidikan, Pengangguran, dan Kemiskinan terhadap Ketimpangan Pendapatan di Indonesia
Authors: Nesha Rizky Ashari  , Syahrul Gunawan  , Irwan W  , Nilawati
Published: January 2025

Penelitian ini bertujuan untuk mengeksplorasi pengaruh pendidikan, pengangguran, dan kemiskinan terhadap ketimpangan pendapatan di Indonesia. Dengan pendekatan kuantitatif, penelitian ini menganalisis data panel dari 34 provinsi selama periode 2019–2023. Data yang digunakan diperoleh dari Badan Pusat Statistik (BPS) dan diolah menggunakan model Random Effect. Temuan penelitian menunjukkan bahwa pendidikan berpengaruh negatif signifikan terhadap ketimpangan pendapatan, yang berarti peningkatan kualitas pendidikan mampu mengurangi ketimpangan. Sebaliknya, kemiskinan memiliki pengaruh positif signifikan, menandakan bahwa peningkatan tingkat kemiskinan memperburuk ketimpangan pendapatan. Di sisi lain, pengangguran tidak memberikan dampak signifikan terhadap ketimpangan pendapatan. Secara keseluruhan, ketiga variabel independen ini secara simultan memengaruhi ketimpangan pendapatan dengan signifikansi yang relevan. Penelitian ini menyoroti pentingnya perbaikan akses dan kualitas pendidikan serta pengentasan kemiskinan sebagai strategi utama untuk mengurangi ketimpangan pendapatan di Indonesia. Hasil studi ini juga memberikan rekomendasi kebijakan inklusif untuk mewujudkan pemerataan ekonomi dan sosial yang lebih berkelanjutan.
Pengembangan E-Modul IPA Berbasis Etnosains untuk Meningkatkan Kemampuan Literasi Sains Siswa
Authors: Gudesma Arin , Ismet  , Kistono
Published: January 2025

Penelitian ini bertujuan mengembangkan E-Modul berbasis etnosains untuk meningkatkan kemampuan literasi sains peserta didik. Jenis penelitian ini menggunakan metode penelitian pengembangan atau development research. Penelitian ini menggunakan model pengembangan 4D Thiagarajan. Teknik pengumpulan data yang dilakukan peneliti adalah Teknik walkthrough, angket respon peserta didik dan instrument tes. Hasil penelitian menunjukkan bahwa E-Modul yang dikembangkan dinyatakan valid oleh ahli. Pada tahap uji coba terbatas dengan 5 peserta didik diperoleh skor 90,00% (sangat praktis) dengan N-gain sebesar 0,64 (kategori sedang). Pada tahap uji coba luas dengan 22 peserta didik diperoleh skor 85,61% (sangat praktis) dan N- gain sebesar 0,68 (kategori sedang) sehingga E- Modul berbasis etnosains dapat menghasilkan E-Modul yang valid, praktis dan dapat meningkatkan kemampuan literasi sains. Dengan  demikian E-Modul yang dikembangkan dapat dengan mudah digunakan dalam pembelajaran Fisika.
Desentralisasi dan Pengelolaan Egosentrisme Berbasis Teori Kognitif Jean Piaget dalam Penugasan Kolaboratif Mata Pelajaran Bahasa Arab
Authors: Annisa Nurul Zakia  , Syihabuddin  , Shofa M Khalid
Published: January 2025

Penelitian ini bertujuan untuk mengetahui potensi desentralisasi dan pengelolaan egosentrisme siswa dalam penerapan penugasan kolaboratif pada usia sekolah dasar dan membantu guru dalam mengembangkan strategi serta memaksimalkan kapabilitas kognitif siswa. Penelitian ini merupakan penelitian kualitatif dengan pendekatan single case studies. Data dikumpulkan melalui dokumentasi, wawancara semi-terstruktur, dan observasi partisipatif. Adapun teknik analisis data yang digunakan adalah teknik analisis data Miles & Huberman dan teknik analisis tematik. Hasil penelitian menunjukkan bahwa siswa menampilkan kemampuan desentralisasi yang baik dalam penugasan kolaboratif. Selaras dengan teori Jean Piaget, kemampuan tersebut masih perlu arahan secara spesifik sesuai dengan tahapan usia perkembangan kognitif mereka. Dalam aspek pengelolaan egosentrisme, siswa menunjukkan kemampuan yang baik dalam bermusyawarah dan bekerja sama. Tidak hanya mendukung teori perkembangan kognitif Piaget, temuan ini juga memperluas pemahaman tentang faktor kecerdasan, lingkungan belajar, dan interaksi sosial dapat mempercepat perkembangan kognitif dan sosial anak.
Fenomena Perempuan Pekerja Migran Indonesia Untuk Kesejahteraan Keluarga
Authors: Devi Andriani  , Luhur Prasetiyo
Published: January 2025

Penelitian ini bertujuan untuk memahami peran perempuan Pekerja Migran Indonesia (PMI) asal Kabupaten Ponorogo dalam meningkatkan kesejahteraan keluarga. Melalui pendekatan kualitatif dengan studi kasus, penelitian ini mengungkapkan bahwa remitansi dari perempuan PMI menjadi sumber pendapatan utama keluarga, namun juga menimbulkan tantangan sosial dan emosional seperti beban ganda, stigma sosial, dan pemisahan keluarga. Hasil penelitian ini mengisi kekosongan dalam literatur mengenai pengalaman perempuan PMI di tingkat lokal. Penelitian ini menyarankan perlunya kebijakan yang lebih komprehensif untuk mendukung perempuan PMI, termasuk program pelatihan keterampilan, akses terhadap layanan keuangan, dan perlindungan sosial.
Efektivitas Model Pembelajaran Student Facilitator and Explaining dalam Meningkatkan Kemandirian Belajar
Authors: Muhammad Ilham Prasetyo  , Nurul Azizah  , Muhammad Akmansyah  , Umihijriyah  , Erni Yusnita
Published: January 2025

Penelitian ini bertujuan untuk mengetahui efektivitas model pembelajaran student facilitator and explaning (SFAE) terhadap kemandirian belajar peserta didik pada mata pelajaran Pendidikan Agama Islam di SMA Kemala Bhayangkari Lampung Utara. Jenis penelitian ini adalah kuantitatif. Penelitian ini menggunakan metode Quasi Experimental Design. Instrumen penelitian ini menggunakan angket. Diuji menggunakan SPSS 25 dan teknik analisis data menggunakan uji normalitas,uji reliabilitas dan uji T. Hasil penelitian ini menunjukkan model pembelajaran Student Facilitator and Explaining (SFAE) memiliki pengaruh yang signifikan dalam meningkatkan kemandirian belajar siswa, khususnya pada mata pelajaran Pendidikan Agama Islam. Berdasarkan uji hipotesis menggunakan Independent Samples T-Test, nilai  (Sig.) pada uji Levene sebesar 0,797 menunjukkan bahwa asumsi kesetaraan varians terpenuhi, sehingga analisis menggunakan baris pertama output uji T dinyatakan valid dan hasil nilai t sebesar 2,343 dengan derajat kebebasan (df) sebesar 53 dan nilai Sig. (2-tailed) sebesar 0,023, yang lebih kecil dari 0,05, sehingga dapat disimpulkan bahwa terdapat perbedaan rata-rata yang signifikan antara kelompok eksperimen dan kelompok kontrol. Hal ini membuktikan bahwa pendekatan SFAE efektif dalam meningkatkan kemandirian belajar siswa.
Strategi Guru Pendidikan Agama Islam dalam Meningkatkan Kesiapan Belajar Siswa
Authors: Faiz Faiz Aswa Nazhan , Syahidin  , Mokh. Iman Firmansyah
Published: January 2025

Penelitian ini bertujuan untuk mengeksplorasi strategi pembelajaran yang digunakan oleh guru PAI dalam meningkatkan kesiapan belajar siswa selama proses pembelajaran. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi lapangan. Teknik pengumpulan data dilakukan dengan wawancara mendalam, observasi langsung, dan analisis berbagai literatur yang relevan. Hasil penelitian mengungkapkan bahwa beberapa strategi yang diterapkan oleh guru PAI, seperti metode tanya jawab, reward and punishment, dan ice breaking, terbukti efektif dalam meningkatkan kesiapan belajar siswa. Penerapan strategi-strategi tersebut tidak hanya membantu siswa untuk tetap fokus dalam belajar, tetapi juga menciptakan suasana belajar yang lebih kondusif dan interaktif. Dengan demikian, penerapan strategi pembelajaran yang tepat dapat memberikan dampak positif terhadap kesiapan belajar siswa, serta mendukung tercapainya tujuan pembelajaran Pendidikan Agama Islam yang optimal.
Advances in neural stem cells in aging and age-related neurodegenerative diseases
Authors: Zhong Rujia , Huang Wenhao , Chen Chao , Zhang Baorong , Pu Jiali , Zhang Feng
Published: January 2025

With aging, neural stem cells (NSCs) undergo age-related changes, including metabolic abnormalities, disrupted protein homeostasis, mitochondrial dysfunction, reduced genetic stability, and more notably, a diminished capacity for proliferation and differentiation. These changes may contribute to the development of age-related neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In these conditions, disease-specific pathological changes may interact with age-related alterations of NSCs, resulting in impairment of neurogenesis, which further leads to cognitive, mood, and motor decline. Based on these changes, potential therapeutics targeting neurogenesis and stem cell-based therapies may restore the functions of NSCs and replace the degenerated neurons, aiming to ameliorate functional decline in these neurodegenerative diseases. While stem cell-based therapies, including stem cell transplantation and stem cell secretome therapy, show great potential in the treatment of neurodegenerative diseases, challenges in tumorigenesis, immune rejection, and extraction or storage of the stem cell secretome need to be further addressed. A better understanding of age- and disease-related changes in NSCs, the underlying mechanism driving these changes, and the benefits and drawbacks of the therapeutic approaches may provide insights for novel disease-modifying interventions for the future treatment of these diseases.
Leveraging Machine Learning Models for Customer Churn Prediction in Telecommunications: Insights and Implications
Authors: Ahmed Jamil , Younis Ilyas , Sarwar Usama , Ghaffar Rashid , Ahmed Tufail
Published: January 2025

In the world of telecommunications businesses, customer turnover poses a significant hurdle that can impact profits and weaken customer loyalty over time. Our solution to this challenge involves a method using Machine Learning (ML) tools to predict churn, with precision. We work with a set of 7In our research study we examined how well three different machine learning models performed. Random Forest (RF) Cat Boost (CB) and K nearest neighbors (KNN). Out of these models tested the Random Forest model stood out for its performance achieving 99 percent accuracy and precision along with an 88 percent recall rate and a 99 percent F1 score; additionally, it achieved an AUC of 0.99. These results clearly demonstrate the Random Forest model's ability, in identifying customers who are likely to churn. The findings of this study hold importance for telecommunications companies as they are equipped with a valuable resource to proactively tackle customer turnover issues and customize solutions to retain key clients while boosting overall customer happiness levels in an increasingly competitive market landscape where keeping customers is crucial for business success our research provides a data supported roadmap for continual expansion and staying ahead in the telecom industry spotlighted in this abstract is the critical relevance of churn prediction for telecom firms underscored by the tangible advantages of leveraging the Random Forest model for predicting customer churn. By utilizing this advanced technology, telecom companies can proactively identify at-risk customers and take targeted measures to prevent them from leaving. This not only helps to retain key clients but also improves overall customer satisfaction. In a constantly evolving market, having access to predictive analytics can give companies a significant edge and ensure long-term success in the industry.
Application of Machine Learning Techniques for Predicting Stroke Disease
Authors: Rafiq Muhammad Yasir , Nazeer Awais , Gilani Anusha
Published: January 2025

Stroke is a cerebrovascular illness caused by a sudden halt in blood flow to the brain, resulting in neurological impairment. Stroke is a major public health problem worldwide, affecting millions of people. It is a significant source of illness and mortality, imposing a significant socio-economic burden. A thorough awareness of the current global situation is required for effective treatments and preventive actions. This research compares data mining techniques for the prediction of stroke illness. Using a dataset obtained from Mayo Hospital, Lahore, that had 2326 instances, each with 11 attributes, we compared the performance of Support Vector Machine (SVM), Random Forest, Neural Network, and K-Nearest Neighbors (KNN) approaches. Orange Data Mining Software was applied to evaluate the data and execute machine learning techniques. The results show that Naïve Bayes is the best method for predicting the prevalence of Stroke disease. The proposed model demonstrates an Area Under the Curve (AUC) of 88.3 %, an accuracy of 80.8%, and notable metrics including an F1-Score and precision.
Towards Improved Assistive Technologies: Classification and Evaluation of Object Detection Techniques for Users with Visual Impairments
Authors: Naz Shakeela , Jabeen Fouzia
Published: January 2025

Even though millions of people struggle to interact with the outside world due to visual impairments, vision is an essential part of our daily lives. Because of its ability to identify and navigate around objects in their surroundings, object detection a crucial component of computer vision has become a potentially helpful solution. This study offers a thorough analysis of object detection techniques utilizing a dual classification system that combines traditional and deep learning methods. In addition, we analyze the most popular evaluation metrics and datasets for these systems' training and evaluation. Unlike previous surveys, our work provides a unique perspective by carefully examining the latest advancements in both innovative deep learning models and traditional approaches. The survey's conclusion highlights current problems and recommends future research directions, highlighting the need for more effective models, diverse datasets, and multi-modal data integration to improve assistive technologies for the visually impaired.
A Hybrid Approach for Simultaneous Effective Automobile Navigation with DE and PSO
Authors: Syed Muhammad Daniyal  , Abbasi Mohsin Mubeen , Hussain Dilbar , Amjad Usama , Abro Abdul Basit , Naeem Mehwish
Published: January 2025

Automobile Navigation is fundamentally an optimization challenge focused on transportation logistics between a depot and various clients. In this paper, we specifically address the complex variant of Automobile Navigation that involves simultaneous pickup and delivery tasks, which must be executed concurrently at clients' locations. This dual requirement introduces significant complexity, as traditional exact approaches struggle to rapidly identify near-optimal solutions due to the problem's NP-hardness. Therefore, the objective of this research is to develop a novel hybrid algorithm that integrates Differential Evolution (DE) and Particle Swarm Optimization (PSO) to effectively solve the Automobile Navigation problem with simultaneous pickup and delivery. The proposed method uses the nearest neighbor heuristic to initially produce results. It is based on the iterated local search paradigm. Variable neighborhood descent is used to improve the search process by adding random sequences to the neighborhood structures for improved search space intensification. Furthermore, exploration across various sections of the search space is made possible by the perturbation process. This method solves the problem of different truck loads on every client visit because it does the pickup and delivery at the same time, which makes the Navigation strategy more effective.
Face Recognition from Video by Matching Images Using Deep Learning-Based Models
Authors: Latif Muhammad , Ebrahim Mansoor , Abro Abdul Salam , Ahmed Maaz , Abbasi Muhammad Daud , Tunio Imran Aziz
Published: January 2025

This paper explores the intersection of video recognition, computer vision, and artificial intelligence, highlighting its broad applicability across various fields. The research focuses on the applications, challenges, ethical dilemmas, and outcomes of artificial intelligence, which continues to grow in significance in the 21st century. We propose a systematic approach that incorporates models for face detection, feature extraction, and recognition. Our methodology includes the accurate segmentation of 100 human faces from video frames, with each face averaging 150x150 pixels. The feature extraction process yielded 1,000 face feature vectors, with an average size of 128, representing key characteristics for recognition. By applying a cosine similarity threshold of 0.7, we filtered irrelevant data and determined whether the two images matched. Our recognition system achieved 85% accuracy, demonstrating the effectiveness of the models and techniques employed. Additionally, ethical considerations were addressed, emphasizing the importance of data privacy, informed consent, cybersecurity, and transparency. This research advances the understanding of face recognition from video data and highlights the need for further exploration in this domain.
Optimizing Machine Learning Classifiers for Credit Card Fraud Detection on Highly Imbalanced Datasets Using PCA and SMOTE Techniques
Authors: Zeeshan Ali Haider  , Khan Fida Muhammad , Zafar Abu , Nabila  , Khan Inam Ullah
Published: January 2025

Card fraud detection refers to the process of identifying unauthorized or suspicious transactions made using credit or debit cards. It employs machine learning models, rule-based systems, and anomaly detection techniques to detect patterns indicating potential fraud. There is a growing need for systems that can accurately predict and prevent fraudulent transactions. Reducing financial loss by Implementing advanced detection models to safeguard it from fraud or malicious transactions. Therefore, we proposed machine learning models that will predict credit card fraud at an early stage. Also, the study used feature scaling, Principal Component Analysis (PCA), and the Synthetic Minority Over-sampling Technique (SMOTE) to deal with the class imbalance on the dataset. Moreover, SMOTE is applied to balance the classes by synthesizing examples of the minority class, making classifiers more robust. The results show that LR, SVM, KNN, and XGBoost models correctly predict 97\% of fraudulent and non-fraudulent cases. The Decision Tree and the Random Forest models are capable of achieving at least 96\%, respectively. This research combines advanced machine learning methodologies with real-time processing to give insights into predictive analytics in financial fraud detection, which may enhance accuracy and efficiency in financial security systems.
U-DENSENET Deep Learning Model for Medical Image Segmentation
Authors: Sarparrah Ali Gul , Ahmed Noor , Dawood Muhammad , Husain Shumaila , Mohammad Sher , Dehwar Mumtaz Ahmed
Published: January 2025

Medical image segmentation, particularly in brain MRIs, is critical for identifying neurological disorders and planning treatment. This paper presents a novel deep learning U-DenseNet model that combines the strengths of DenseNet and UNet architectures for improved brain tumor segmentation and skull-stripped segmentation. Evaluated on both skull-stripped and brain tumor datasets, the U-DenseNet model achieves a Dice coefficient of 0.9125 and accuracy of 99.81\% for brain tumor segmentation and a Dice coefficient of 0.9902 and accuracy of 98.49\% for skull-stripped segmentation. The architecture of U-DenseNet model effectively captures fine anatomical details while ensuring computational efficiency, making it suitable for clinical applications.
A Fine Grained Sentiment Analysis of Arabic Language
Authors: Tanveer Asim , Khan Mohibullah , Sarwar Rehan , Aslam Naeem , Fuzail Muhammad
Published: January 2025

This work focuses on fine-grained sentiment analysis of Arabic text using recent Natural Language Processing methods. Arabic is a language rich in variation, spoken by over 400 million people, yet there is a significant lack of resources for sentiment analysis. To address these challenges, this study employs AraBERT, a model specifically fine-tuned for Arabic text. A corpus of one hundred thousand Arabic reviews across categories such as hotels, books, and movies was scraped and cleaned. These reviews were then categorized into positive, negative, and mixed sentiments. AraBERT was compared with traditional machine learning methods, including Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest. AraBERT achieved superior accuracy of 88\%, along with higher precision, recall, and F1 scores for both positive and negative sentiment classes compared to the other models. This work demonstrates that AraBERT effectively analyzes the syntactic and semantic structure of Arabic, making it a valuable tool for Arabic sentiment analysis across various applications. Future work will extend the model to handle neutral sentiments and include additional dialects to further improve its performance.
Next-Gen Rentals: Smart Applications for Urban Living
Authors: Abbasi Muhammad Asad , Khan Abdullah Ayub , Laeeq Kashif , Bakhsh Waseem , Awan Shafique Ahmed , Aziz Asif
Published: January 2025

This study investigates the revolutionary impacts of "Karayedar.pk," a state-of-the-art smartphone application designed to streamline the real estate rental process. Both owners and renters are searching for better options because of the market's long history of inefficiencies and opaque tactics. With a sizable property database, advanced search capabilities, comprehensive property listings complete with images and descriptions, secure in-app messaging, and a system for user ratings and reviews, "Karayedar.pk" appears to be a feature-rich platform. This essay's objective is to assess "Karayedar.pk" key features and competencies in light of their ability to address real estate rental concerns. This study offers a comprehensive analysis of how "Karayedar.pk" can revolutionize and improve the rental property market, providing useful data to mobile app developers, owners, and tenants alike.
Reducing the Risk of Cyber Attack in SDN Network by using Blockchain
Authors: Khanzada Khaliq Ahmed , Liaquat Syed Faraz , Farooqui Muhammad Najmul Islam , Nadeem Muhammad , Ahmed Mishaal , Ahmed Manzar
Published: January 2025

In the contemporary digital landscape, Software-Defined Networking (SDN) has emerged as a transformative approach that decouples network control from hardware, enabling greater flexibility and centralized management. However, this innovation has also introduced new vulnerabilities, making SDN networks susceptible to cyber-attacks. To reduce the risk of cyber-attack blockchain can play vital role. In paper we propose a novel framework that leverages blockchain technology to enhance the security and resilience of SDN environments against potential threats. By integrating blockchain’s decentralized and immutable characteristics, our approach facilitates secure data transactions, enhances network visibility, and fosters trust among network participants. We present a comprehensive analysis of the vulnerabilities inherent in traditional SDN architectures and outline method, how blockchain can mitigate these risks through secure authentication, data integrity, and enhanced access controls.Furthermore, we conduct a series of experiments to evaluate the performance impact and security benefits of our proposed solution. The results demonstrate a significant reduction in the likelihood of cyber-attacks, showcasing the viability of blockchain as a potent tool for safeguarding SDN networks. Our findings underscore the importance of interdisciplinary approaches in addressing the evolving challenges of network security, paving the way for more resilient and secure SDN infrastructures in the future.
Sindhi Text-Based Students Sentiment Analysis Using Convolutional Neural Network
Authors: Mahar Shahid Ali , Mushtaque Muhammad Imran , Mahar Mashooque Ali , Mahar Javed Ahmed , Magsi Aurangzeb
Published: January 2025

Current generation especially the teenager students are using Social Media (SM) platforms at an extreme level even the sentimental angles are too discussed there. In the province Sindh, students mostly prefer to text the message in origin of their mother tongue i.e. Sindhi lexicon for sharing their views regarded politics, religions, sports, education etc.All these sentimental conveys are important for enhancing the academic capabilities.In this research paper, approach is broken down into multiple phases comprising of number of WhatsApp chat, lexicon generation, dataset tokenization, Convolutional Neural Network (CNN); all based on respective sentiments.To validate the experimentation process at standard level. 100 WhatsApp data chats were collected from different levels of students and divided into four categories.The CNN Model is used for sentimental classification. Accuracy, Precision, Recall and F-Score are the four parameters used for model evaluation. The model provides 0.874% accuracy, 0.883% recall, 0.863% precision and 0.745% F-Score.
A Review of Classification Approaches in Educational Data Mining for Predicting Student Performance
Authors: Kumari Veena , Meghji Areej Fatemah , Shaikh Farhan Bashir , Qadir Rohma , Oad Urooj
Published: January 2025

With the rapid increase in student data, and the growing interest in finding insights into student learning patterns,Educational Data Mining (EDM) methods are increasingly being used by educational institutes. Classification, a popular EDM method, enables the in-depth, efficient, and thorough analysis of student data while providing insights that directly assist in understanding student learning patterns and identifying elements that influence academic success. This review seeks to identify common trends and assess the effectiveness of four popularly explored classification approaches for predicting student performance. To assure the selection of research that specifically addresses the use of classification approaches for predicting student academic achievement, this review follows a systematic approach. A quality evaluation step was also included to help ensure that only reliable and credible studies were included in the review. According to the review findings of thirty two studies, most researchers used assessment results, academic performance index, and demographics to predict student performance. Decision Trees and Probabilistic classifiers were found to be the most popular and commonly used classification approaches for predicting student performance. The review also focuses on the challenges often faced while undertaking classification tasks in EDM and outlines future research directions in the context of analyzing student data.
QoS Authentic Routing protocol for Home-based IoT-based System
Authors: Khan Bushra , Ahmed Mukhtiar , Lakho Shamshad , Zardari Baqir Ali , Siraj Saima
Published: January 2025

In recent years, IoT has emerged as one of the most transformative technologies of the 21st century. By connecting everyday objects such as home automation systems, manufacturing tools, agricultural equipment, healthcare devices, insurance platforms, transportation systems, kitchen appliances, cars, thermostats, security cameras, baby monitors, and more to the internet, IoT enables seamless communication between people, processes, and devices.Current trends in IoT technology are focused on designing and developing local trust values for routing nodes through enhanced K-means evaluation systems. Specifically, the goal is to improve routing evaluation by refining K-means clustering to create a more reliable network. In home-based IoT networks, the focus is on local trust values for routing nodes, addressing existing weaknesses by designing an advanced K-means algorithm to improve network reliability and security.In IoT networks, devices communicate with each other, transferring information from source to target nodes for processing, storage, and analysis. The proposed QoS-ARP protocol leverages this advanced K-means-based global trust value algorithm. By incorporating metrics such as packet delivery ratio (PDR) and monitoring for malicious nodes, the protocol aims to enhance network efficiency, reduce power consumption, and extend the network's lifespan.
Bit Pattern based Sindhi Character Recognition using Neural Network
Authors: Jia Deepa , Soomro Saima siraj , Lakho Shamshad , Ali Waqas , Ali Baqir , Hussain Akhtar
Published: January 2025

In this paper, bit pattern based character recognition for Sindhi language has been presented. The characters of sindhi language are very much complexed to recognize for particular domain. Although there are many studies that have already been done in this recognition but all those are based on image recognition, to give novelty in the idea our system uses bit patterns for characters and provide outcome on the basis of that input pattern. A data set with nine no. of inputs and six outputs for each character is created. We have used patterns due to the computational complexity constant that are 3X3 matrix for input patterns that are uniquely created for all characters and output will be generated in form of binary pattern for the particular character sequence numbers. This system reads the 3X3 matrix in clock wise pattern to get input pattern and match it to created data set. To train the data we have used a Neural Network Model, Multi-Layer Perceptron (MLP) with significant number of hidden layers to get measurable results. The accuracy of 82.6% has been achieved by the experiment.
Optimized Music Classification with a Hybrid VGG16-RNN Using Mel-Spectrogram and MFCC Features
Authors: Ashraf Mohsin , Ashraf Saima
Published: January 2025

Music classification using deep neural networks has gained a lot of attention in recent years. This is due to the difficult task of capturing every essential aspect of music in features and interpretability of classifiers. There is limited research on the integration of VGG16 and RNNs, but the researchers found that few classifiers accurately capture intrinsic musical characteristics. Previous work in this field has primarily focused on spectral features, which has constrained overall performance. To address this issue, we proposed a novel hybrid neural architecture based on Visual Geometry Group 16 (VGG16), which is highly effective in extracting important features from musical variations. We combined VGG16 with several recurrent neural network (RNN) variants, including Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Additionally, we compared their performance for the GTZAN dataset using both Mel-Spectrogram and Mel-Frequency Cepstral Coefficients (MFCC) features. Our results indicate that the VGG16+GRU model achieved the highest accuracy of 89. 60% with Mel spectrograms and 82. 70% with MFCC features. These findings demonstrate the effectiveness of combining advanced feature extraction techniques with deep learning models for music genre classification.
Distance and energy aware DEAODV routing protocol in disastrous situation
Authors: Soomro Mohammad Ali , Channa Mohammad Ibrahim , Nizamani Shah Zaman , Bhutto Mohammad Aamir , Ghoto Abbas Ali
Published: January 2025

In the disastrous or emergency situation a reliable and robust ad hoc mobile network communication is important. The natural disaster when occurs in the urban populated areas which damage Information technology infrastructure and other valuable assets within a movement. In such emergency situation when the disaster victims stuck in debris, lifesaving of people are important. The purpose of this study is to enhance lifetime and improve network performance when the lives of disaster victims are matters. Distance and Energy aware AODV (DEAODV) reactive routing protocol enhance the network performance and ensure the reliable data transmission during crisis situation. The DEAODV reactive routing protocol considered node Energy and shortest distance as a routing metrics during route decision process. DEAODV routing protocol is compared with traditional AODV routing protocol to evaluate the performance of network. The proposed routing protocol is more efficient in emergency situation than AODV in terms of Packet Delivery Ratio, End to End Delay and Consumed Energy.
A Convolutional Neural Network (CNN) Based Framework for Enhanced Diagnosis and Classification of COVID-19 Pneumonia
Authors: Muhammad Suliman  , Malik Fazal , Khan Muhammad Qasim , Ashraf Ullah  , Rahman Noor , Shah Said Khalid
Published: January 2025

COVID-19 pneumonia is a persistent worldwide health problem that usually affects the most vulnerable groups in society: the newborn and aged populations. Most of the current endeavors toward handling diagnosis and classification of pneumonia have used numerous techniques for machine learning and deep learning, with a particular focus on COVID-19 pneumonia. However, most of these techniques have raised concerns with regard to diagnostic precision as a result of the limited application of advanced algorithms, datasets whose validation is mostly inadequate and predictive capability. To address these limitations, our research introduces a deep learning-based approach by Convolutional Neural Networks (CNNs), which enhances the performance in classifying COVID-19 pneumonia. Salient features of the proposed method include a four-step process: first, data acquisition from a comprehensive chest X-ray dataset on GitHub; second, processing and analyzing the data through visual means like histograms and scatter plots; third, using CNNs supplemented with techniques for data augmentation in supervised learning; lastly, performance evaluation to benchmark against existing models. The present study uses features from X-ray images with the help of CNN's advanced pattern recognition capabilities in pursuit of achieving better generalization and precision in classification. The model achieved an accuracy of 85.70\% and precision of 88.6%, which surpasses the existing techniques and thereby built the promise of improving the accuracy of the diagnostic process, hence, leading to improved care for the patients, and more so forms the foundation on which future AI-powered healthcare diagnostics are expected to take off.