bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluasi Kinerja Keuangan dan Prediksi Kebangkrutan Koperasi Menggunakan Altman Z-Score: Studi Kasus pada KPRI Gajayana Malang
Authors: Firmansyah Rizky , Tyas Diva Retnaning , Rafi Muhammad , Cahyanisa Zalwa
Published: March 2024

Analisis kinerja keuangan digunakan untuk menyajikan hasil olahan data yang relevan, tepat waktu, dan akurat kepada pemangku kepentingan pada lingkup internal maupun eksternal. Analisis kinerja keuangan dapat membantu Perusahaan dalam mengetahui kelebihan dan kekurangan aspek finansialnya, mengidentifikasi dan mengatasi peluang maupun ancaman yang dapat berujung kebangkrutan, mengevaluasi kinerja, serta merencanakan strategi masa depan entitas bisnis. Maka dari itu, tujuan dari penelitian ini adalah menganilisis kinerja keuangan dan memprediksi kebangkrutan pada KPRI Gajayana Malang yang mengacu pada teori Altman Z-Score. Metode yang digunakan dalam penelitian ini adalah Deskriptif kuantitatif dengan menggunakan pendekatan studi kasus untuk menganalisis dan memperkirakan kebangkrutan pada koperasi tersebut. Analisis ini menghasilkan kesimpulan bahwa peningkatan nilai Z-score KPRI Gajayana Malang disebabkan oleh peningkatan dalam kinerja keuangan solvabilitas. Hal ini mengindikasikan bahwa KPRI Gajayana Malang mengalami kesulitan dalam mengelola keuangan khususnya rentabilitas sehingga dapat meningkatkan risiko kebangkrutan. KPRI Gajayana Malang berada dalam zona abu-abu, yang berarti berada pada risiko kebangkrutan namun masih bisa diatasi dengan perbaikan kinerja keuangan. Faktor penyebab peningkatan nilai Z-score pada KPRI Gajayana Malang adalah peningkatan variabel solvabilitas yang lebih tinggi daripada penurunan variabel pada rentabilitas, likuiditas, dan aktivitas perusahaan.
Pengaruh Budaya Organisasi Dan Kualitas Kehidupan Kerja Terhadap Komitmen Organisasi Karyawan Hotel 21 Gisting Kabupaten Tanggamus
Authors: Meizary Ary , Magdalena Betty
Published: March 2024

Penelitian ini bertujuan untuk mengetahui pengaruh budaya organisasi dan kualitas kehidupan kerja terhadap komitmen organisasi karyawan Hotel 21 Gisting Kabupaten Tanggamus. Jenis penelitian dalam penelitian ini adalah kuantitatif dengan metode assosiaitif. Sumber data yang digunakan adalah data primer berupa questioner tentang variabel budaya organisasi, kualitas kehidupan kerja, dan komitmen organisasi. Metode pengumpulan data adalah field reseach yaitu dengan penyebaran questioner. Populasi dalam penelitian ini adalah seluruh karyawan berjumlah 30 Karyawan yang dijadikam sampel. Tehnik pngambilan sampel adalah Sampling Jenuh. Uji persyaratan instrumen adalah Uji Validitas dan uji Reliabilitas. Uji persyaratan analisis data adalah Uji Normalitas, Uji Linieritas, Uji Multikolonieritas. Metode analisis data yang digunakan adalah analisis Regresi Linear Berganda. Uji Hipotesis yang digunakan adalah Uji t dan uji F. Hasil penelitian ini adalah Budaya organisasi (X1)  dan Kualitas kehidupan kerja berpengaruh terhadap komitmen organisasi Karyawan Hotel 21 Gisting Kabupaten Tanggamus.
Advancements in Deep Learning Algorithms
Authors: Vaissnave Dr. V. , Nandhini Ms. S. , Davamani Ms. K. Anita , Malathi Ms. P. , Pothumani Mrs. S.
Published: March 2024

Advancements in Deep Learning Algorithms is a comprehensive exploration of the cutting-edge developments in deep learning, a subset of artificial intelligence that has revolutionized the way machines learn from data. This book starts with the basics, introducing the reader to the fundamental concepts and terminologies of deep learning, before delving into the core algorithms that form the backbone of this field, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). It further explores advanced architectures and techniques such as attention mechanisms, deep reinforcement learning, federated learning, and autoencoders, providing a deep dive into the mechanisms that enable machines to mimic human-like learning processes. The book also addresses critical aspects of data handling and preprocessing, optimization and regularization techniques, and the practical applications of deep learning in various industries, highlighting real-world case studies. Additionally, it discusses the challenges, ethical considerations, and future implications of deploying deep learning technologies. With an eye towards recent trends and the future directions of deep learning, this book aims to equip researchers, practitioners, and enthusiasts with the knowledge to understand and leverage the potential of deep learning in solving complex problems. Keywords: Deep Learning, Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Attention Mechanisms, Deep Reinforcement Learning, Federated Learning, Autoencoders, Data Preprocessing, Optimization Techniques, Artificial Intelligence, Industry Applications, Ethical Considerations, Future Directions.
Effect of silver colloid dressing over conventional dressings in diabetic foot ulcer: A prospective study
Authors: Tajdar Yasir , Singh Sakshi , Raj Ankit , Raj Ayush , Bhushan Vibhuti
Published: March 2024

Objective: Topical silver treatments and silver dressings are increasingly being utilized for the local treatment of wounds; nevertheless, the evidence for their usefulness is unclear. The aim of this study was to investigate the impact of conventional dressings and silver colloid dressing on diabetic foot ulcers (DFU) with and without compression therapy.

Material and Methods: This prospective, double-blind experiment included 50 patients with non-ischemic DFUs, split into two groups of 25 patients each. The study was conducted for a period of six months. The primary endpoint was to evaluate the entire epithelialization (total healing) of all ulcers on the study leg.

Results: The ulcer area significantly decreased in the colloidal silver group (67.77 ± 17.82%) compared to the conventional saline group (21.70 ± 23.52%). When compared to the conventional group, the colloidal silver group required considerably fewer days to reach the endpoint (23.15 ± 8.15 days vs. 48.35 ± 18.07 days), and by day 14, ulcer area reduction (from 100%) was greater (48% in the silver group vs. 89.69% in the conventional group).

Conclusion: In managing DFUs, unstructured hydrogel wound dressings using silver colloids based on ionic silver are more effective than regular saline dressings since they heal wounds more quickly in fewer days while also drastically reducing ulcer areas over time.
Abdominal versus pelvic gunshot injuries in terms of postoperative outcomes: A cohort representing the experience of 406 cases in seven years
Authors: Mansor Salah , Ziu Naman , Almissmary Hannibal , Alawami Mohammed , Bujazia Ayoub , Eltarhoni Ahmed
Published: March 2024

Objective: Gunshot injuries have become a significant problem globally. This study aimed to assess the differences between abdominal versus pelvic gunshot injuries in terms of postoperative outcomes to determine which type of injury is more lethal.

Material and Methods: This was a cohort study carried out to compare patients who had abdominal versus pelvic gunshot injuries, to analyse the differences in the impact of the anatomical site of injury on morbidity, mortality, and disabilities in all patients who had been admitted to the hospital due to torso gunshot injuries from February 2011 to December 2018.

Results: During the study period, 406 patients suffered from torso gunshot injuries. 391 were males and 15 were females; 343 (84.4%) patients had abdominal gunshot wounds, which were considered the first group, while in the second group, there were 63 (15.6%) patients who had pelvic gunshot wounds In the first group, 328 (95.6%) patients required urgent explorative laparotomy, complications were observed in 83 (24.2%) patients, while re-operations were reported in 51 (14.9%) patients and 11 (3.2%) patients had permanent functional disabilities, and 46 (13.4%) patients passed away. In the second group, all patients were treated with urgent explorative laparotomy, 17 (27%) patients suffered from complications, re-operation occurred in 13 (20.6%) patients, permeant functional disability occurred in 17 (27%) patients, and mortality was seen in 16 (25.4%) patients.

Conclusion: Our clinical experience has shown that mortality rates and long-term disability occur at a higher rate in pelvic gunshot injuries therefore early senior surgeon input is mandatory for the potential poor outcome to be minimized.
Colorectal cancer screening; colonoscopy and biopsy results in people undergoing colonoscopy due to positive fecal occult blood test
Authors: Yücel Metin , Taha Demirpolat Muhammed , Kadir Yıldırak Muhammed
Published: March 2024

Objective: Screening programs are important for the early detection of colorectal cancer, which is one of the causes of high morbidity and mortality. In this study, we investigated the colonoscopy results, the incidence of adenoma and cancer, and the relationship between test results and cancer in individuals with a positive fecal occult blood test for colorectal cancer screening.

Material and Methods: Within the scope of the colorectal cancer screening program, colonoscopy was requested for individuals aged 50-70 years who applied to our outpatient clinic with a positive fecal occult blood test. The results were collected and analyzed.

Results: The results of the colonoscopy could be obtained in only 237 (56.43%) of the 420 patients who were referred for a colonoscopy because of a positive fecal occult blood test. Colonoscopy results were normal in 15 (6.33%), benign anal disease in 64 (27%), benign colonic disease in 12 (5.06%) and polyp + adenocarcinoma in 146 (61.61%). Pathology results were benign polyp in 37 (15.61%), adenomatous polyp in 86 (36.29%) and adenocarcinoma in 23 (9.71%). Quantitative test results were higher in the adenomatous polyp + adenocarcinoma group and statistically significant (p= 0.03).

Conclusion: Individuals with positive fecal occult blood tests, especially those with high quantitative test results, should be encouraged to have a colonoscopy, and they should be warned about the high probability of adenomatous polyps and colorectal cancer.
Liver resection versus ablation in geriatric populations - Does one method impart improved in-hospital mortality?
Authors: Gajdos Csaba , Ryan Carrie , Savulionyte Goda , Schwaitzberg Steven , Nader Nader
Published: March 2024

Objective: This study aimed to compare surgical resection versus ablation for managing liver malignancies in patients 65 and older.

Material and Methods: Cases with liver tumors were extracted from the NSQIP database for patients aged ≥65 years. Following propensity score matching, multivariate Cox regression was used for 30-day morbidity and mortality for liver resection and ablation.

Results: Following a propensity score matching, 1048 patients were 1:1 matched for comorbid conditions. Patients stayed in the hospital three days longer after resection (p<0.001). Mortality was lower after ablation (p= 0.013). This difference was more prominent in patients with primary liver tumors (p= 0.008). Group A had a 10-fold lower risk of developing an abdominal abscess, a fourfold decrease in hospital-associated pneumonia (p= 0.001) and reintubation, a 10-fold reduction in bleeding requiring transfusion (p<0.001), and a three-fold decrease in risk of developing sepsis (p<0.001).

Conclusion: Despite being a generally sicker patient population with worse underlying liver function, ablative techniques were associated with a lower risk of adverse outcomes when compared to more aggressive resection of primary malignant tumors of the liver.
The Value of Corporate Bond Listing
Authors: Cole Brittany M. , Gullett Nell S.
Published: March 2024

We study the impact of bond exchange listing in U.S. publicly traded corporate bond markets. We find that listed corporate bonds have lower estimated bid-ask spreads than unlisted corporate bonds. Specifically, we show that listed bonds have estimated spreads of $0.14 lower than unlisted bond spreads. We find that execution venue matters for listed bonds, and that listed bond executions on the NYSE have higher trading costs than listed bond executions off-NYSE. We show that listed bonds are more volatile than unlisted bonds. Last, we study bond trading around earnings announcements and find a slight increase (decrease) in overall (institutional) volume on earnings announcement days compared to non-announcement days.
Academic Literacy and Research Supervision of Teacher Trainees in Luanda/Angola
Authors: Sambeny Celeste Ana da Glória Eduardo
Published: March 2024

Introduction The teaching and learning process of any group or level of students is always dynamic and can change its characteristics depending on the context in which it takes place. Accordingly, the process of training teachers is also characterised by several changes which are dictated by the process itself and the context and conditions inherent in the same process. McLeod & Reynolds (2007: 1) contend that “… we are teaching and learning in times of overwhelming change – changes in the way we know, changes in the way we teach and changes in what is expected of us as teachers and learners”.
Dispositions of Health Facility In-charges Towards Decentralization and Integration of Mental Health Services into Primary Health Care: A Case of Lusaka District.
Authors: Mulungu Choongo , Siwela Emmanuel , Zulu Idah , Lubasi Hendrix , Lungu Gabriel
Published: March 2024

Introduction: Worldwide, the burden of mental illnesses poses serious public health challenges, with approximately 7.4% of the global burden attributed to mental disorders. However, many primary healthcare systems in countries around the world focus mainly on physical care and neglect to provide mental health care to their populations. In Zambia, the prevalence of mental disorders is approximately 20%, with common mental disorders including acute psychotic episodes, schizophrenia, alcohol-related problems, and organic brain syndromes. These are mainly seen in Chainama, the only mental institution in the country. The institution is highly stigmatized, and previous studies have shown that the general population does not access services at this facility as intended, leading to considerations of decentralizing these services to be integrated into primary health care. This study sought to understand the dispositions of primary health facility in-charges towards decentralization and the integration of mental health services into primary health care. Methods: This study was cross-sectional and employed both quantitative and qualitative techniques. A total of 76 primary health care facility in-charges from seven subdistricts of Lusaka district provided data through a questionnaire, and 56 participated in focus group discussions. Findings: The study found that in-charges had a positive disposition (52 in-charges: 68.3 percent) despite having inadequate knowledge (56 in-charges: 73.7 percent) of the subject matter and the decentralization process. While their current practices towards mental patients were found to be acceptable, the study also found that the majority (52.6 percent) of the in-charges foresaw more barriers to the integration of mental health, citing skilled staff, infrastructure, and drugs as main barriers. Conclusion: The study concludes that integration requires more investment and sensitization for its success.
MAPEAMENTO DE OCORRÊNCIA DAS ÁREAS VERDES COMO INDICADORES DA QUALIDADE AMBIENTAL NA CIDADE DA BEIRA-MOÇAMBIQUE
Authors: Chitula Gerson Moura , Armindo Adalberto Falso , Cherinda Nhelete , Ussene Lelia Roberto
Published: March 2024

Este estudo teve como objetivo principal fazer o mapeamento de áreas verdes e a suscetibilidade a inundações dessas áreas dentro da cidade da Beira. E para a realização deste estudo usou-se o método cartográfico, trabalhado em ambiente ArcGIS 10.5 e Google Earth e de revisão bibliográfica na qual fez-se o mapa de uso e ocupação do solo através do índice de vegetação (NDVI) de modo a mapear os locais de ocorrência de vegetação e as áreas verdes e de seguida fez-se o mapa de suscetibilidade a inundações destas mesmas áreas através do método AHP. conclui-se que a cidade da Beira possui uma intensa urbanização, grande parte da área que poderia ser vegetada está impermeabilizada por conta da presença de construções de infraestruturas e pavimentações, tornando a cidade mais vulneráveis aos problemas ambientais e como resposta disso e necessário a valoração e manutenção das áreas verdes urbanas. Grande  parte das áreas verdes mapeadas na cidade da Beira, estão totalmente suscetíveis a fatores de inundação e com o crescente aumento da população urbana na cidade de Beira proporciona de grande modo a ocupação desordenada do solo urbano e consequentemente o não acompanhamento na implantação e manutenção de infraestrutura de base.
DİJİTALLEŞMENİN ULUSLARARASI TİCARET ÜZERİNDEKİ ETKİSİ
Authors: KONAK Ali
Published: March 2024

Günümüzde dijitalleşme hemen hemen her alanda hızlı bir gelişim göstermektedir. Bu gelişim süreci ise uluslararası ticari faaliyetleri önemli ölçüde etkilemektedir. Özellikle bilgi teknolojilerinin gelişmesi, birçok faaliyetin dijital ortamda gerçekleşmesine olanak sağlamaktadır. Bu süreçte geleneksel alışveriş alışkanlıkları yerini dijital ortamda gerçekleştirilen alışverişe bırakmış, bunun akabinde de farklı yenilikçi yaklaşımlar ortaya çıkmaya başlamıştır. Dijital ortamda gerçekleştirilen alışveriş imkânı sayesinde tüketiciler, ihtiyaç duydukları mal ve hizmetlere ilişkin çok sayıda alternatifi hızlı bir şekilde karşılaştırma ve seçim yapma olanağına sahip olmuşlardır. Bu gelişme ise işletmelerin gerek yurtiçinde gerekse de yurtdışında daha rekabetçi olmalarına neden olmaktadır. Üretim ve pazarlama aşmalarında dijitalleşme çağını yakalayabilen işletmelerin, diğer işletmelere göre piyasada daha avantajlı konumda olacağını söylemek mümkündür. Bu bağlamda piyasada rekabet gücünü elde edebilmek amacıyla gerek üretim gerekse de sonraki aşamalarda dijitalleşmeye yönelik yatırımların yapılması son derece önem arz etmektedir. Özellikle teknolojik gelişmelerin de etkisiyle hızlanan küreselleşme, günümüzde pek çok salgın hastalığın hızla yayılmasına neden olmakta ve bu durum karşısında işletmeler, dijital teknolojiler sayesinde müşterilerine ulaşabilmektedir. Son yıllarda ileri teknolojinin uluslararası ticaretteki rolü de önemli ölçüde artmıştır. Bu süreçte işletmeler, mal ve hizmetlerini daha çok geliştirme, geniş bir pazara sunma ve uluslararası piyasalarda söz sahibi olabilmek için dijital teknolojilere yönelik yatırımlarını artırmaktadırlar. Bu bağlamda dijitalleşme ekonomik bir olgu olduğunu söylemek mümkündür. Dijital ekonominin büyümesi, uluslararası ticaretin gelişmesi açısında yeni fırsatlar sağlamaktadır. Gerek ülke ekonomileri gerekse de işletmeler açısından bu denli önemli olan dijitalleşmenin uluslararası ticaret üzerindeki olumlu ve olumsuz etkilerini değerlendirmek amacıyla hazırlanmış olan çalışmada, veri analiz yönteminde yararlanılmıştır. Yapılan incelemeler neticesinde dünyada meydana gelen hızlı dijitalleşme sürecinin, gerek ulaşım gerekse de haberleşme alt yapısında meydana getireceği olumlu gelişmelerin genel olarak dış ticaret faaliyetlerini hızlandırdığı tespit edilmiştir
DİJİTAL ÜRETİM VE PAZARLAMA SÜRECİNDE YAPAY ZEKÂNIN ÖNEMİ
Authors: YAGUBOV Rahib , KONAK Ali
Published: March 2024

Artificial intelligence is a technological structure with an algorithmic system network that is changing today's technology in many ways. It is estimated that artificial intelligence will continue to enter our lives rapidly over time and that it will make more changes than expected 1 Doktora Öğrencisi, Karabük Üniversitesi Lisansüstü Eğitim Enstitüsü, İşletme A.B.D., rahib2023@gmail.com orcid: 0000-0003-1401-5572 2 Doç. Dr., Karabük Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü, alikonak@karabuk.edu.tr orcıd: 0000-0003-1804-8339 50 in the lives of many people in the process of globalization. Artificial intelligence provides machines with the ability to see, hear, taste, smell, touch, speak, walk, fly and learn. With the use of these functional qualities of artificial intelligence in the production process, it is seen that there is an increase in the production volume and the quality of the products, and it may make the countries more competitive in international markets. Therefore, countries and businesses that want to gain competitive power in international markets focus on the development of artificial intelligence by increasing their investments in R&D expenditures. Thanks to the developments in artificial intelligence, businesses can communicate with their customers more effectively and offer smart products, services and automatic shopping experiences to them in order to increase their commercial performance. In addition, artificial intelligence emerges as a new concept in digital marketing fields. With artificial intelligence starting to play a role in digital marketing, dealers have begun to achieve very positive and effective results in personalizing services. As digital marketing strategies are supported by data-driven artificial intelligence systems, they have naturally succeeded in influencing industries on a large scale. Data analysis method was used in the study, which was prepared to evaluate the use of artificial intelligence in both production and marketing stages from a broad perspective and to examine the benefits of this technological structure and its role in digital transformation. As a result, it has been determined that artificial intelligence has significant effects on both production and marketing processes, and therefore, the importance of investments required for the development of artificial intelligence has been pointed out
DİJİTALLEŞMENİN TÜKETİCİ DAVRANIŞLARI ÜZERİNE ETKİSİ
Authors: ERGÜL Murat , KONAK Ali
Published: March 2024

Notably after the 90s, it is seen that digitalization has an important role in consumers social lives and has remarkable effects on the behaviors of different economic units. It is a fact that both manufacturers and consumers are under the intense influence of digitalization during the epidemic process that had a global impact in 2019. Retail electronic commerce that reached a large volume of approximately 5 trillion US dollars in the world in 2021, has experienced an increase of approximately 70% in our country compared to the previous year, according to the data of the Ministry of Commerce. In this sense, it has been observed that large-scale changes have been experienced in the decision-making processes and consumption habits of consumers with electronic shopping, which is one of the most concrete results of digitalization. Although these changes can be clearly seen with various statistical data, comprehensive studies on how consumer behaviors follow in digital environments have been limited. First, with online shopping, consumers have gained many advantages such as making price comparisons of the products they want to buy more easily and evaluating options more accurately. In addition, since the international distribution of the products produced with online trade activities becomes easier, electronic commerce also overcomes some existing trade barriers and provides great convenience in delivering these products to consumers. In the light of these reasons, it has become inevitable for traditional purchasing habits to evolve into electronic purchasing habits. From the perspective of consumers, for instance, the user-friendliness and high reliability of the websites used in the shopping process are among the most important factors sought in the decision-making process. Companies that are aware of the process and make online sales also make great efforts to meet these expectations of consumers. Under the leadership of these determinations, it is underlined that digitalization and electronic purchasing activities in particular, have made commercial activities easier and brought about significant changes in consumption habits with the growth experienced in the world and in our country.
-Marketing in The Corona Pandemic and its Role in Activating E-Commerce in Algeria
Authors: بن داود أسية
Published: March 2024

The outbreak of the health crisis of Corona virus, also known as (Covid-19 pandemic) has helped in the emergence of the economic concept of e-marketing despite the various negative repercussions that the pandemic had on various other aspects of human life, namely, the imposition of strict preventive policies such as home quarantine and social distancing. These regulations changed the nature of the Algerian consumer behavior and the way he purchases his products, goods and services. Accordingly, and to achieve positive results that help bridging the gap between the producer and the consumer in light of social distancing adopted during the pandemic, marketers started promoting and selling their products and services on the internet, paving the way for the emergence of e-commerce. The latter has proven its viability and practicality in facilitating electronic commercial transactions by improving consumers’ experiences on the web. Hence, the present research study aims to investigate the role and importance of e-marketing in light of the spread of Covid-19 pandemic and its impact on shedding light on e-commerce as a being a priority that Algeria has become more in need of to execute various commercial and economic transactions.
FEATURES OF THE INDIVIDUAL HERO IN THE POETRY OF ANTARA BIN SHADDAD
Authors: MEHMETALI Bekir
Published: March 2024

Championship occupied a significant space among Arab poets in the pre-Islamic era, and the literary and historical eras that followed, and perhaps this is due to the fact that the harsh, Bedouin life, full of conflicts, invasions, and wars between Arab tribes required that each tribe have knights, heroes, They defend it, secure its existence, and even impose its dominance, control, and power over other tribes. Among these heroes were poets such as Duraid bin Al-Summa, Amer bin Al-Tufail, Amr bin Maadikarb, Antara bin Shaddad Al-Absi, and many others. The motives for this tournament were multiple, they may be social, economic, personal or otherwise, and all of the previous motives may meet; to motivate someone; To be a hero in his tribe. Since the conflicts and wars between people, individuals, and societies do not end, and heroism is a necessity in life; To ensure the existence of this or that society in a world that only recognizes the right of the powerful, I thought that I should address this topic in this study, and limit it to the features of the individual hero in the poetry of Antara bin Shaddad; Because his heroism, in my opinion, has two main reasons: his tenderness and his love for Abla; And because he became an example of heroism and courage, his heroism shaped the image of the individual, the hero, the model in Arab thought and culture. The research aims to memorize the features of this heroism in a contemporary reading, and the importance of the research is reflected in its goal and the personality that it studies. The researcher will follow the methods of description, analysis, and induction; To achieve the purpose of writing it
DETECTION OF FOREIGN WORDS IN THE STORY OF EFRUZ BEY
Authors: TÜRK Osman
Published: March 2024

Ömer Seyfettin, who is one of the important representatives of Turkish literature, has produced works in many fields such as novel, poetry and theater, especially story. In addition to his literary personality, he is one of the rare personalities who make his presence felt in the fields of culture and art. When you look at all this, Ömer Seyfettin; He was a storyteller, poet and intellectual. He left behind tens of pages of works, each of which is the pearl of the Turkish language, after his short life of thirty-six years. In the study, Efruz Bey, who is in the stories of the author, is discussed. The words in the work have been determined in terms of their origin, and the uses of these words have been concretized with examples. The words in the text were then classified according to the languages they were found in. Dictionary study word frequency studies are studies that reveal the frequency of use of words used in a language. Dictionaries are resources that contain the words of a language alphabetically, including idioms and example sentences of that language
EMOTIONAL OPPRESSION AND ITS RELATIONSHIP TO SELF –AFFIRMATION AMONG KINDERGARTEN STUDENTS
Authors: ALI KADDOURI Lubna
Published: March 2024

The current society brings up responsible university students who may be relied upon by building their personalities and psychological formation through providing them with symbols, meanings and values that govern their emotional behavior, in addition to teaching students to assert themselves through the skills of dealing with others, anticipating their behavior, predicting their responses, and interaction with them positively. Universities are among the most important educational institutions in society because it plays an important role in the process of socialization of students, so the researcher identified the research problem with the following question: What is the level of the concept of emotional suppression and self-affirmation for kindergarten students? 1- Recognizing the level of emotional suppression among kindergarten students. 2- Recognizing the level of self-assertion of kindergarten students. 3- Recognizing the relationship between emotional suppression and self-assertion among kindergarten students. 4- Identifying the extent of the difference in the relationship between emotional suppression and self-affirmation according to the stage variable. The current research was limited to the female students of the Kindergarten Department for the academic year (2021-2022) in the province of Baghdad, which the research sample included (150) female students from the College of Basic Education, Kindergarten Department. And to achieve the objectives of the research, the researcher adopted the emotional suppression scale for (Taleb, 2013), which consisted of (22) items, and the self-assertion scale of (Rattos 1979) after dividing it into seven domains, which consisted of (28) items. The researcher will reach the results of her research after applying the research tools. 1-There are Statistically Significant differences between the mean Scores of the Sample and the hypothetical mean, and this difference is in favor of the hypothetical mean of the scale this means that the Kindergarten students are not characterized by emotional suppression 2-There are Statistically Significant differences between the mean Scores of the Sample and the hypothetical mean of the scale this and this difference is in favor of the mean Scores of the Sample. 3-There are differences in the relationship between emotional suppression and selfassertion among female kindergarten students according to the second and fourth academic stages, and this difference is in favor of the fourth stage. 4-There is a relationship between emotional suppression and self-affirmation, Based on this, it can be said that the higher the self- assertion of the students, the less their emotional suppression
A review on monitoring activities through human activity recognition
Authors: Arora Yogita , Gupta Neha , Yadav Sarita , Agarwal Neera , Chugh Tripti , Arora Vipul
Published: March 2024

field of human activity recognition (HAR) is advancing with the integration of cutting-edge technologies such as artificial intelligence and sensor networks. These developments enable accurate detection and interpretation of human behavior, driven by advances in AI and machine learning. A growing number of electronic devices and applications contribute to a comprehensive understanding of the three pillars of HAR – AI, various devices and methodologies and their applications. This review consolidates HAR research and highlights its processes, applications, and device comparisons. Key findings include the importance of non-invasive, real-time sensor-based approaches, particularly in healthcare for the detection of behavioral abnormalities in the elderly. The study highlights Adam as an outstanding optimizer in HAR applications, who predicts continued expansion and development in various industries. particularly in the healthcare sector.
An artificial intelligence based authentication mechanism for wireless sensor networks using blockchain
Authors: Devi Suman , Kumar Avadhesh
Published: March 2024

Blockchain Networks (BNs) are widely used in a variety of applications around the globe. BN exchanges data and information using various inter-network and intra-network protocols. The sharing of this information across multiple platforms poses a significant security threat to the data sensed by the devices. This paper uses a combination of blockchain technology with AI algorithms to propose a novel security framework for BNs that acts as a security check gateway for any third party trying to access BN data. This paper proposed a hybrid security framework using blockchain. In order to provide a clear and efficient authentication scheme, global blockchain and local blockchain are separately used for BN authentication. The proposed work uses an artificial intelligence (AI) approach - Student Psychology Based Optimization (SPBO) is implemented in Matlab and Python. This approach is compared with some famous security algorithms of BNs, and it performs better than all the existing security algorithms used for BN.
Using kernel functions to estimate the probability density function for segmentation images
Authors: Eesa Aseel Muslim , Alhasnawi Mohammad Kaisb Layous , Hamza Zainab Falih
Published: March 2024

This paper used nonparametric kernel functions to segment images using the thresholding method. This was done by transforming the image to grey and then estimating the probability density function from the grey image data and taking the highest value of the core function vector as the threshold limit. The study also found that the bandwidth parameter affects the segmentation process because it affects the estimate of the probability density function. The bandwidth parameter smooths the curve and brings it closer to the true curve because of its significant effect on bias and variance. The beam width parameter is known to depend on the size of the sample. Thus, it was found that when the parameter is used to determine the image size into equal rows and columns, it gives good, satisfactory results for segmented images that contain the most important areas of interest with removing unhelpful or important areas. Finally, the uniform Gaussian, cosine, triangular, and logistic Silverman kernel functions proved their efficiency in extracting all image features.
Reduced bias estimation of variance for repeated measurements model
Authors: Mustafa Hadeel Ismail , AL-Mouel Abdulhussein Saber
Published: March 2024

This paper discusses two important points; the first part describes the one-way repeated measurements model, which had two random effects in addition to the random error and two fixed factors. The model was also written as a set of matrices, which showed how the random effects and fixed factors interacted. The second part shows the maximum likelihood method for determining the variance components for a one-way repeated measures model. In this method, it was seen that the estimates of the variance combinations have some bias. The goal of this study is to use the Mean bias reduction method of the one-way repeated measurements model to reduce the bias in the estimators of the variance components.
Deep learning for image-based diagnosis : Applications in medical imaging for drug development
Authors: Sawale J. S. , Barekar Praful V. , Gandhi J. M. , Gujar Satish N. , Limkar Suresh , Ajani Samir N.
Published: March 2024

This research delves into the application of Deep Learning (DL) to medical imaging for drug discovery, highlighting DL’s ability to enhance diagnostic precision and efficiency. The research looks into the use of DL methods like CNNs and RNNs for automating feature extraction, pattern recognition, and classification in large, complex medical datasets. Effective drug development relies on fast and accurate diagnosis of disease to determine treatment’s therapeutic efficacy and identify therapeutic targets. When applied to huge datasets, DL excels at uncovering subtle patterns and correlations that may be missed by more conventional methods. In addition to improving early disease identification and tailored medicinal therapies, the technology also helps in biomarker discovery. In this study, we discuss some of the obstacles of deploying DL ethically in medical imaging, such as protecting sensitive patient information, ensuring that models can be easily interpreted, and using a wide variety of data. The merger of DL and medical images offers great potential to promote image-based diagnosis in drug discovery, contributing to a more personalized and precise approach in healthcare, ultimately improving patient outcomes and changing the future of modern medicine.
Enhancing agriculture productivity with machine learning in crop yield optimization
Authors: Bhoyar Dinesh , Jain Sachin , Kene Jagdish D. , Suryawanshi Y. A. , Patil A. R. Bhagat , Karale Shivkumar J.
Published: March 2024

The majority of the GDP of an agriculture-based economy comes from farming. This initiative was inspired by the rising suicide rates among farmers, which may be related to poor crop yields. The field of agriculture is now seriously threatened by changes in the climate and other environmental factors. For this problem to be solved effectively and practically, machine learning is a crucial strategy. Estimating agricultural output based on historical information such as Ph, humidity temperature, rainfall, N, P, K. We used Machine Learning method to achieve this. We constructed and compared a number of various machine learning algorithms and ultimately settled on the Random Forest Algorithm, which provided an accuracy of 97.87%.
Performance evaluation of machine learning algorithms for Parkinson’s disease identification model
Authors: Karale Shivkumar J. , Mangrulkar Nikhil , Badhiye Sagarkumar S. , Patil A. R. Bhagat , Bhoyar Dinesh , Khobragade Rajesh
Published: March 2024

Though Medical sector is referred to as one of the most advance sector in the Country, there are some implications which shows disorientation of medical sector and early prediction of various ailments. There are some diseases which does not have any preventive measures and are incurable till date. Parkinson’s disease is one of those disease that is incurable till date and its clinical diagnosis requires early detection of symptoms which may vary according to age parameter. Parkinson’s disease is one of the incurable diseases which affects the Central Nervous System (CNS) of Human brain which leads to various symptoms like barely noticeable tremor on limbs, slowness in movement, improper balance and posture of body, Loss of automated movement and sometimes change in vocal frequency. With theadvent of Machine Learning (ML) and Artificial Intelligence (AI) technique are used for diagnosis of brain diseaseswhich is helping the doctor for early detection of the diseases.Considering the performance evaluation parameters, we have given equal importance to every performance measures used. The dataset for the Parkinson’s disease detection is taken from Oxford UCI Machine repository. Analysis and Comparative study show that all the algorithms can detect the person is affected by the Parkinson’s disease or not. Out of them random forest algorithm yielded highest performance measures.
Predictive analytics for patient adherence : A machine learning approach in medication management
Authors: Patil A. A. , Niranjane Vaishali , Dhadde S. B. , Kumar Anil , Borde Santosh , Chandre Pankaj
Published: March 2024

In this paper, we investigate the use of predictive analytics and machine learning to improve patients’ adherence to prescribed medications. By analyzing past patient data, we are able to create a model that may foresee adherence behaviors based on characteristics like age, gender, health status, and income. In order to analyze and identify major drivers of non-adherence and enable personalized therapies, our method incorporates cutting-edge machine learning techniques. The methodology takes into account each individual’s situation while making suggestions, leading to a more proactive and specific strategy for improving drug adherence. Predictive analytics allows healthcare providers to foresee adherence issues, intervene at the right time, and boost patient outcomes. This research contributes to the developing landscape of healthcare by utilizing the potential of machine learning for precision medicine in drug management, addressing a vital part of patient care and public health.
Several implications for forecasting a brain attack utilizing mathematical techniques
Authors: Pidurkar Smita R. , Raut Seema
Published: March 2024

The human brain is unquestionably the most complex organ in the body and is acknowledged as the body’s control station. The wellness of this control station is depends on several human related factors. The uncontrolled behavior of this factors may cause brain stroke. In this study, survey data from 24,400 brain stroke patients are considered, along with important possible confounders such as age, gender, hypertension, heart disease, sugar, BMI, and smoking. Initially the association with this parameters with stroke were tested. Regression analysis was then performed to produce the projected model based on the data and its accuracy was tested.
Analysis of different noise filtering techniques for object detection and tracking from video with varying illumination
Authors: Barekar Praful V. , Singh Kavita R.
Published: March 2024

In order to improve object tracking and detection in films with different lighting conditions, this paper provides a thorough review of noise filtering methods. Computer vision systems face difficulties due to the variety of illumination conditions found in real-world settings, which calls for effective noise reduction techniques. We explore and evaluate several filtering techniques, such as deep learning-based methods, adaptive algorithms, guided bilateral filter and spatial and temporal filters, to determine how well they preserve object characteristics while cutting down on noise. We simulate real-world conditions with our studies on video datasets that have dynamic illumination changes. The outcomes show how well guided bilateral filter methods work in difficult illumination situations to preserve precise object trajectories and boundaries. To help practitioners choose the best noise filtering algorithms based on application-specific needs, we also go over the computational consequences and trade-offs related to each strategy. This work addresses a crucial component of real-world object detection and tracking in movies with variable lighting, offering insightful new information to the field of computer vision.
Data-driven optimization strategies for enhanced cardiovascular risk assessment
Authors: Hardas Bhalchandra M , Aush Mithun G. , Raut Vaishali
Published: March 2024

Cardiovascular diseases (CVD) remain a significant global health challenge, requiring the improvement of risk assessment methods using advanced data-driven optimization strategies. This study explores the field of ML by comparing the default implementations of Random Forest (RF) and Support Vector Machine (SVM) in the context of predicting cardiovascular risk. The study examines the potential enhancements achieved by optimizing these models using Particle Swarm Optimization (PSO), a metaheuristic algorithm known for its capacity to improve complex problem-solving. The comparative analysis assesses the predictive accuracy of default RF and SVM models in identifying cardiovascular risk factors. Subsequently, a PSOtechnique is employed to optimize the parameters of both RF and SVM models, with the objective of maximizing their predictive capabilities. Remarkably, the Random Forest model, when enhanced with PSO stands out as the most precise, attaining an impressive accuracy rate of 97.4%. To strengthen the analytical process, the research includes a thorough exploratory data analysis (EDA) phase. This involves a thorough analysis of the dataset to reveal hidden patterns, correlations, and possible variables that may cause confusion. The knowledge acquired from EDA not only enhances the optimization process but also facilitates a more profound comprehension of the complex interconnections among different cardiovascular risk factors. The study highlights the crucial significance of data pre-processing in guaranteeing the dependability and resilience of the models. The application of thorough techniques for handling missing data, outliers, and normalization significantly improves the quality of input data, thereby enhancing the performance of ML models. The utilization of various methods such as comparative analysis, optimization with PSO, exploratory data analysis, and rigorous data pre-processing highlights the possibility of creating cardiovascular risk assessment tools that are both highly accurate and dependable. The results of this study add to the ongoing discussion on utilizing sophisticated data analysis techniques to enhance patient outcomes and modify healthcare interventions in the field of cardiovascular health.
AI-driven pharmaceutical manufacturing : Revolutionizing quality control and process optimization
Authors: Jadhav N. R. , Bhutada Sunil , Sagavkar S. R. , Pawar Rohit , Kanwade Archana Bajirao , Mange Purva
Published: March 2024

The Integrating AI into pharmaceutical production processes represents a paradigm change for the pharmaceutical sector. This article examines the role of AI in pharmaceutical production, focusing on its potential to improve productivity, cut costs, and guarantee the highest standards of product quality and safety in quality control and process optimization. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly being employed to analyze large volumes of data generated throughout the pharmaceutical manufacturing lifecycle. Risks related to production anomalies can be reduced and regulatory compliance can be ensured with the help of these smart systems’ real-time monitoring, early identification of deviations, and predictive maintenance. AI-driven technologies are revolutionizing quality control processes by allowing for the automated screening of pharmaceutical items at lightning speeds and with pinpoint accuracy. Better product quality and fewer cases of batch rejection are the results of AI systems’ superiority over conventional approaches for detecting tiny faults, ensuring uniformity, and identifying potential contamination. In addition, AI is improving manufacturing processes by analyzing large data sets for trends, tweaking settings, and maximizing output. Pharmaceutical companies save money as a result of streamlined production processes, shorter cycle times, and better resource utilization.
Mathematical enhancement of unsupervised machine learning algorithms for optimal lifeline donor knowledge extraction
Authors: Naidu P. Ramesh , Gite Pratik , Deka Vaskar , Prasad K. D. V. , Gowda V. Dankan , Rahman Mirzanur
Published: March 2024

Lifeline Donor groups are crucial in the field of humanitarian aid because they provide lifesaving aid in times of crisis. For effective resource management and aid coordination, it is crucial to draw relevant insights from donor data in a timely manner. Scalability, precision, and flexibility in responding to changing donor dynamics are all areas where traditional techniques of knowledge extraction fall short. This study offers a fresh method for optimizing knowledge extraction from Lifeline Donor databases by utilizing improved unsupervised machine learning methods. In order to extract, categorize, and prioritize donor knowledge from disparate data sources, this research presents a multi-faceted framework that includes cutting-edge machine learning algorithms. By merging cutting-edge developments in NLP and data mining, the proposed framework improves upon classic clustering and topic modelling methods. The program is able to capture subtle semantic links and underlying patterns in donor communication data by using methods like word embedding models and graph-based clustering.