bluuebunny/crossref_metadata_2025 huggingface dataset:
SOSIALISASI BAHAYA ROKOK DIKALANGAN REMAJA DENGAN GERAKAN SISWA BEBAS ASAP ROKOK
Authors: Dewi Masita Sari , Fajar Wulan , Desi Rihfa Fauziah  , Annisa Adillah  , Novita Nurpadilah  , Salsa Agustin  , Nuzul Gyanata
Published: January 2024

Latar belakang : Kebiasaan merokok sudah menjadi masalah kesehatan utama yang terjadi di berbagai negara. Secara global, jumlah perokok di seluruh dunia mencapai 1,3 milyar orang dengan 942 juta laki-laki dan 175 juta perempuan yang berusia 15 tahun lebih. Tujuan: Untuk memberikan edukasi serta meningkatkan pengetahuan kepada para siswa  SMK Garuda Nusantara terkait dengan bahaya dari asap rokok dan bagaimana cara mengatasi masalah tersebut. Metode: Edukasi dilakukan dengan cara penyampaian langsung kepada responden dengan alat peraga yang digunakan yaitu power point presentation. Kegiatan dilaksanakan selama 120 menit, terdiri dari 5 menit pretest, 80 menit penyampaian materi dan sesi tanya jawab, dan kemudian diakhiri dengan posttest selama 5 menit. Hasil: Berdasarkan hasil pretest (67,75%) dan posttest (74,19%) terdapat peningkatan pengetahun yang cukup baik bahwa hampir semua siswa yang mengikuti penyuluhan dapat menerima pengetahuan dengan baik setelah diberi edukasi. Kesimpulan: Pengetahuan peserta mengenai bahaya merokok meningkat setelah dilakukan edukasi bebas asap rokok.
PENGABDIAN MASYARAKAT TENTANG BAHAYA RABIES DAN PENCEGAHANNYA
Authors: Marthasari Ketut putri , Rismayanti I Dewa Ayu , Sundayana I Made , Antariksawan I Wayan , Astriani Ni Made Dwi Yunica
Published: January 2024

Pendahuluan: Rabies adalah penyakit infeksi tingkat akut pada susunan saraf pusat yang disebabkan oleh virus rabies. Rabies disebabkan oleh virus rabies yang masuk dalam keluarga Rhabdoviridae dangenus Lysavirus. Penyakit ini bersifat zoonotik, yaitu dapat ditularkan dari hewan ke manusia. Penyakit Rabies menyerang Bali sejak tahun 2008, dan sampai saat ini. Upaya peningkatan pengetahuan tentang pencegahan rabies di masyarakat sangatlah penting. Tujuan dari pengabdian masyarakat ini adalah memberikan Pendidikan kesehatan tentang bahaya rabies dan pencegahannya. Metode Penelitian: Tahap pelaksanaannya adalah dengan memberikan penyuluhan yang di hadiri 30 warga Banjar Kubu Anyar, Desa Kubutambahan. Kemudian tahap evaluasi dengan melihat pemahaman dan perilaku warga dalam melaksanakan pencegahan terhadap penyakit rabies dengan memberikan pertanyaan atau timbal balik dari semua informasi yang diberikan di tahap pelaksanaan. Hasil: bahwa seluruh warga dapat menjawab dan memahami tentang bahaya penyakit rabies.. Kesimpulan: Penyuluhan tentang bahaya rabies dan pencegahannya ini sangatlah penting guna menekan angka kejadian rabies di Bali khususnya di Banjar Kubu Anyar, Desa Kubutambahan Singaraja.
Երկրների ձախողման տնտեսական պատճառները / ECONOMIC CAUSES OF COUNTRIES FAILURE
Authors: VARDANYAN Tatevik
Published: January 2024

The economic prosperity or decline of nations is a complex phenomenon influenced by various
factors and circumstances. Throughout different periods in world history, several countries have experienced economic setbacks, stagnation, and even collapses. Whether within individual countries or on
a global scale, financial and economic crises are inherent phases of the economic cycle, often exposing
a range of socio-economic issues and challenges. Therefore, it is crucial to explore the theoretical
foundations underlying the economic factors contributing to a nation's failure, examine real-world examples, and, most importantly, delve into the root causes of Armenia's economic challenges. Such an
analysis can provide valuable lessons for Armenia in terms of averting economic crises and mitigating
risks.
Աղքատության նյութական և ոչ նյութական հարթությունները Հայաստանի Հանրապետությունում / Material and Non-material Aspects of Poverty in the
Republic of Armenia
Authors: KHACHATRYAN ANI
Published: January 2024

In the current conditions of the
development of the world economy, the development of a viable
policy for overcoming poverty and its consistent implementation
is crucial in the countries with both developing and developed
economies. It requires a quantitative evaluation of the results,
which has a decisive role in highlighting the possibilities of
deepening the policies aimed at overcoming poverty and
reducing inequality, adjusting benchmarks equivalent to reality
and identifying "gaps".
In this paper, we tried to perform a similar analysis using
the tool of cluster analysis and carried out a classification of
countries with different levels of development. The latter
provides an opportunity to observe the results of the socioeconomic policies implemented in different countries in the
context of the relationship between poverty indicators, economic
growth, education and healthcare expenditures. Based on the
obtained results, the groups of countries with the best, worst,
average, higher than average and lower results were
distinguished.
Based on the data of Household’s Integrated Living
Conditions Survey, we have also implemented binar logit and
estimated the probability of not experiencing poverty depending
on the educational level of the head of the household, the
number of members, the place of residence, etc. The results of
the analysis are of practical importance in the context of revision
of social policy, identification of "vulnerable points" and
adjustment of priorities.
Application of the Bernstein protein determination method in the study of the quality of feed raw materials of plant and animal origin
Authors: Suzdaltseva M. A. , Busygin P. O. , Lysov A. V. , Vasilyeva A. N.
Published: January 2024

For the purpose of practical application of the method of determining the mass fraction of protein according to Barnstein for various one-component feed raw materials of animal and vegetable origin, we conducted a series of experiments on four types of raw materials: fish meal, meat and bone meal, corn gluten, sunflower cake. To do this, each sample of feed was divided into 2 samples and an additive was introduced into each (one additive contained protein, and the other non– protein nitrogen). Determination of the protein nitrogen content in feed was carried out under the same conditions as the analysis of the initial samples. When comparing the theoretically calculated value and the practical result for all types of feed samples, reliable results were obtained. The conducted studies have shown the need to control raw materials to establish the true protein content in order to exclude the possibility of contamination with nitrogen-containing substances of non -protein origin.
The effect of prolonged irregular search and rescue operations on the biochemical parameters of the blood of service dogs of medium breeds
Authors: Chelnokova V. V. , Prusakov A. V. , Yashin A. V.
Published: January 2024

The aim of the study is to assess the impact of prolonged irregular search and rescue operations on the biochemical blood parameters of medium–breed service dogs. The study was conducted on dogs of medium breeds: Border Collie, Australian Shepherd, Nova Scotia Retriever. The age of the studied animals ranged from four to ten years, and the body weight from 16.2 to 22.0 kg. Assessment of the condition of animals before and after their participation in search and rescue operations was carried out based on the results of clinical examination and biochemical blood analysis. Biochemical blood analysis was performed on a Stat Fax 4500 analyzer. The following parameters were determined: calcium, phosphorus, potassium, sodium, chlorides, magnesium, urea, creatinine, ALT, AST, alkaline phosphatase, total protein, albumin, globulin. Glucometry was performed at the beginning of the study. Blood sampling was carried out from the peripheral vessels of the ear using scarifiers. Most dogs received treats and were not hungry, which was taken into account when interpreting the results. A CareSens N glucose meter was used to measure blood glucose levels. Based on the data obtained, no signs of any diseases were detected during the physical examination, all animals were clinically healthy. However, the occurrence of obvious stress was noted in all animals. Blood sampling for biochemical analysis was carried out after at least eight hours of a starvation diet, from the lateral subcutaneous vein of the thoracic limb, taking into account compliance with the rules of asepsis and antiseptics. It was found that the loads during prolonged irregular search and rescue operations have an impact on the biochemical picture of the blood, expressed in a decrease in the average value of phosphorus, total protein, globulin, as well as in an increase in the average value of calcium, potassium, sodium, chlorides, magnesium, urea, creatinine, ALT, AST, alkaline phosphotase, albumin, glucose.
Antibacterial vaccines for poultry made on the basis of the adjuvant ICTYOLANETM 11
Authors: Pankratov S. V.
Published: January 2024

The use of antimicrobial drugs and vaccinoprophylaxis are the main ways to prevent and combat most bacterial diseases. However, the unsystematic use of antimicrobials without taking into account the sensitivity of pathogens to drugs often does not allow achieving the desired results. On the other hand, the use of a properly selected vaccine, taking into account the epizootic situation in the farm, is one of the safe and effective tools for controlling diseases of bacterial etiology. In this regard, the results presented in this article of testing samples of vaccines against bacterial diseases of birds, made on the basis of a modern oil adjuvant ICTYOLANETM 11, are interesting and timely. For research three vaccine samples were manufactured based on the oil adjuvant ICTYOLANETM 11. The first sample of the vaccine is against avian salmonellosis, the second is against avian pasteurellosis and the third is against avian respiratory mycoplasmosis. Analysis of the results showed that all vaccine samples made on the basis of the oil adjuvant ICTYOLANETM 11 met the specified parameters in terms of viscosity and stability, ensured the formation of humoral immunity of the required level and fully met the requirements for drugs of this class. But along with good physico-chemical and immunological indicators, vaccines against salmonellosis and pasteurellosis of birds showed reactogenic properties to one degree or another, while the vaccine against respiratory mycoplasmosis of birds was areactogenic. Based on the obtained research results, it can be concluded that the use of the adjuvant ICTYOLANETM 11 in the production of a vaccine against avian respiratory mycoplasmosis makes it possible to obtain a safe and effective immunobiological preparation.
Mobile district veterinary stations, their foundation, equipment and their functioning in the Republic of Tatarstan
Authors: Latypov S. S. , Trofimova E. N.
Published: January 2024

The article presents materials on the foundation, equipping and functioning of mobile district veterinary stations of the Government Veterinary Service Department of the Republic of Tatarstan. Their experience within 2018-2022 on providing veterinary service of agricultural cattle breeding enterprises, farms and private subsidiary farms in the republic settlements was summarized.
Increasing the viability of rooster sperm during cryopreservation through the use of enzymatic antioxidants
Authors: Kurochkin A. A. , Pleshanov N. V.
Published: January 2024

Cryopreservation of rooster semen has found wide application in creation of reproductive cell’s cryobanks. Compare to other farm animal’s semen, quality of frozen/thawed bird semen are often lower. This factor makes choice of individual ejaculates for the purposes of cryopreservation more difficult. In our study, we considered the possibility of improving frozen/thawed semen performance by adding enzymatic antioxidants to diluents. It has been shown that during the vitrification of reproductive cells, the addition of exogenous enzymatic antioxidants reduces the destructive effect of reactive oxygen species, which indicates the possibility of improving method by reducing oxidative stress to cells. When added to the diluent for cryopreservation rooster’s sperm LKS-1 superoxide dismutase in amount of 75 IU cell viability increased by 3,65 %, when was added catalase in amount of 200 μg/ml, cell viability increased by 5,27 %.
OSOBENOSTI DOŽIVLJENOG I POČINJENOG VRŠNJAČKOG NASILJA U OSNOVNOJ I SREDNJOJ ŠKOLI
Authors: Pehlić Izet , Vučkić Ermin
Published: January 2024

Uvidi u konsultirane recentne izvore pokazali su povezanost doživljenog i počinjenog vršnjačkog nasilja. Cilj istraživanja bio je da se na temelju stavova osnovnoškolaca i srednjoškolaca istraže osobenosti doživljenog i počinjenog vršnjačkog nasilja u osnovnoj i srednjoj školi na području Unsko-sanskog kantona.

Od istraživačkih metoda korištene su metoda teorijske analize i deskriptivno-analitički survey metod, a od istraživačkih tehnika anketiranje i skaliranje. Od istraživačkih instrumenata korišteni su Upitnik o socio-demografskim podacima i Skala vršnjačkog nasilja. Istraživački uzorak, koji je po svojoj prirodi bio slučajni stratificirani, činilo je 300 osnovnoškolaca i 300 srednjoškolaca s područja Unsko-sanskog kantona.

Rezultati istraživanja su pokazali: kod djece i mladih statistički značajno je veće doživljeno od počinjenog vršnjačkog nasilja; srednjoškolci su u većoj mjeri počinioci vršnjačkog nasilja od osnovnoškolaca; ispitanici muškog spola su u statistički značajno većoj mjeri doživjeli i veći su počinioci vršnjačkog nasilja od ispitanika ženskog spola; učenici sa slabijim školskim uspjehom su u statistički značajno većoj mjeri doživjeli i veći su počinioci vršnjačkog nasilja od ispitanika s boljim školskim uspjehom; djeca i mladi koji su u većoj mjeri doživjeli vršnjačko nasilje istovremeno su u statistički značajno većoj mjeri i počinioci vršnjačkog nasilja.

Izveden je zaključak kako bi rezultati istraživanja mogli biti podsticaj i osnova za kreiranje i realiziranje socijalno-pedagoških programa koji bi bili u funkciji prevencije vršnjačkog nasilja.
VJERA I ŽIVOT KAO ZAŠTIĆENE, UNIVERZALNE VRIJEDNOSTI U ISLAMU I NJIHOV KORELACIJSKI ODNOS
Authors: Štulanović Muharem
Published: January 2024

Fikhska djela, općenito spominju ciljeve i intencije šerijata u vidu čuvanja i zaštite univerzalnih vrijednosti, posebno vjere i života. Međutim, ne nalazimo samostalne radove, bar ne na našem govornom području, koji stavljaju u korelacijske veze ove dvije univerzalne, zaštićene vrijednosti. Zbog toga smo pokušali definirati prve dvije temeljne zaštićene vrijednosti u šerijatu, a to su vjera islam i život čovjeka, upotrebom deskriptivne metode i metode navođenja tradicionalnih tekstova s analizom, komparacijom i njihovim vrednovanjem kako bi se objasnio njihov međusobni, korelacijski odnos.
Dimensi Ukuran Tubuh Pedet Sapi Bali Betina Polled Dan Sapi Bali Betina Non Polled Umur 6-8 Bulan
Authors: Parmadi Gio , Sonjaya Herry , Firmiaty Sri , Syarifuddin Syarifuddin
Published: January 2024

Penelitian ini bertujuan untuk mengetahui perbedaan ukuran tubuh pedet betina sapi Bali polled dan betina sapi Bali non polled umur 6-8 bulan. Penelitian ini menggunakan pedet sapi Bali betina polled dan pedet sapi Bali betina non polled dengan umur 6-8 bulan milik  masyarakat sebanyak 30 ekor (15 ekor pedet sapi Bali polled  betina dan 15 ekor pedet sapi Bali betina non polled). Alat yang  digunakan dalam penelitian ini yaitu, pita ukur dan tongkat ukur yang  digunakan untuk mengukur panjang badan, lingkar dada, tinggi badan dan lebar kepala. Berdasarkan hasil penelitian mengenai perbedaan ukuran tubuh pedet betina sapi Bali polled betina dan sapi Bali betina non polled  secara statistik tidak menunjukkan perbedaan yang nyata.  Penelitian  menyimpulkan pertumbuhan panjang badan, lingkar dada dan lebar kepala pada sapi Bali polled betina sama  dengan ukuran tubuh sapi Bali betina  non polled.
Construction of Business Model of Unmanned Economy Under Digital Technology
Authors: Xiaoli Li Xiaoli Li
Published: January 2024

In the ever-evolving landscape of modern business, the integration of advanced technologies is paramount for optimizing operations, ensuring efficiency, and staying competitive. The business model for the unmanned economy comprises the challenges related to supply chain and logistics. This paper proposed an architecture of LM-LSTM (Linear Regression and Long Short-Term Memory) model within the context of the Unmanned business model. The proposed model uses the mandami based fuzzy rule for the computation of the unmanned economy. Within the mandami fuzzy linear regression model is adopted for the computation and estimation of the variables related to the unmanned economy. The objective is to provide a comprehensive analysis of the impact of this predictive modeling system on various dimensions of the business. Through the generated rules the LSTM model is utilized for the classification and computation of the features related to supply chain, forecast demand and other parameters in an unmanned economy. The examination of 10 unmanned products in Chinese products are evaluated. The findings of LM-LSTM stated that Sales forecasting, one of the critical aspects of any business, has seen a remarkable improvement in accuracy, with an average Mean Absolute Error (MAE) of 3.00%. This accuracy ensures that products are produced and stocked according to actual demand, preventing costly overstocking or stockouts. The inventory management process has been streamlined, with tailored strategies for each product category. This adaptation has resulted in reduced stockouts, efficient parts sourcing, and minimal overstock situations. Supply chain optimization has significantly reduced lead times, enhancing customer satisfaction through timely product deliveries. Customer behavior analysis, facilitated by LM-LSTM, has led to a notable increase in sales across the product range, with an average increase of 91%. This enhanced customer engagement is coupled with substantial cost savings, with an overall reduction of 118%. Downtime has been minimized, contributing to smoother operations and improved customer service.
Construction of Personalized Movie Recommendation Model Relying on Recurrent Neural Network
Authors: Qin Xie Et al.
Published: January 2024

Recommendation system and classification machine learning techniques have emerged as a major area of investigation into the issue of information overload. Software tools called "recommender systems" aim to help users make informed choices about services. This paper developed a low-dimensional embedding in which a limited number of features can be used to represent the data object. For the purpose of acquiring the low rank latent factors, matrix factorization techniques have received a lot of attention. The proposed model is stated as the Collaborative Filtering Machine Learning (CFML) for the multi-label classification are focused. In most extreme edge lattice factorization plan of cooperative sifting, appraisals framework with different discrete qualities is treated by uncommonly stretching out pivot misfortune capability to suit numerous levels. The comparative analysis is performed for the two-class classifier into a single multi-class classifier. Alternately, multiple two-class classifiers can be arranged in a hierarchical fashion to create a multi-class classifier. To deal with the ordinal rating matrix's completion. The performance of the CFML model is effective for the recommendation system design.  Through automatic feature extraction and pattern recognition, deep learning models excel at capturing intricate relationships and latent factors that underlie user preferences. The paper discusses the multifaceted considerations that these systems can incorporate, such as genre, cast, director, viewer ratings, and individual viewing history. Simulation analysis examine the impact of deep learning-based movie recommenders on streaming platforms and the entertainment industry, demonstrating their ability to not only suggest movies but to curate a personalized journey through the world of cinema.
Fusion Artificial Intelligence Technology in Music Education Teaching
Authors: Liang Zhang Liang Zhang
Published: January 2024

This paper proposed innovative EFDfO (Entropy Features Data Fusion Optimized) framework, a data-driven approach aimed at revolutionizing music education teaching. EFDfO combines data fusion, feature extraction, and optimization techniques to customize teaching strategies to individual students' unique learning profiles. The data related to students are collected with different sources and data were fused. The fused data are optimized with the Whale optimization technique to estimate the performance of the students. Simualtion analysis of the EFDfO demonstrates its potential to enhance student performance, with an average improvement of approximately 18% to 20% observed in pre-test and post-test scores. Moreover, the classification results indicate that, in most cases, EFDfO accurately categorizes students based on their performance and learning characteristics, although further refinement is needed to reduce misclassifications. Additionally, with the proposed EFDfO model the performance of the students are improved.  EFDfO offers a promising avenue for personalized music education, ultimately enhancing students' learning experiences and outcomes.
Design Of Intelligent Countermeasure System for Power System Network Security Defense
Authors: Feilu Hang Et al.
Published: January 2024

In an increasingly interconnected world, the convergence of power system networks and biometric-based biomedical applications presents unique challenges for data protection and privacy. This research endeavors to conceptualize and design an intelligent countermeasure system that serves as a robust defense mechanism for enhancing security in this complex ecosystem. The proposed system incorporates biometric authentication techniques to fortify user access controls, implements advanced encryption methods for safeguarding sensitive biomedical data, and intrusion detection and prevention mechanisms to thwart cyber threats. This paper proposed an Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) for data protection and privacy in biometric data for power system devices for biomedical applications. The IPRCC combines probabilistic regression techniques for data analysis with cryptographic methods to fortify the security and privacy of biometric data used within power system devices for biomedical applications. To secure biometric data, IPRCC integrates cryptographic techniques. Cryptography involves encoding information in a way that only authorized parties can decode and understand it. IPRCC incorporates a classifier as part of its security framework. The classifier is used to make decisions or classifications based on the analyzed biometric data. The IPRCC includes enhanced data protection, improved privacy, and increased security for biometric data. The Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) is a sophisticated security system that combines probabilistic regression modeling and cryptographic techniques to protect biometric data used in biomedical applications, especially when integrated with power system devices. Simulation results demonstrated that the proposed IPRCC model exhibits an improved attack detection rate of 99%.
Off-Road Terrain Identification And Analysis
Authors: Nagula Sai Sathvik Et al.
Published: January 2024

Background: The role of the Terrain is paramount for any autonomous vehicle to drive safely on any type of surface. The Autonomous vehicles should have the capability of identifying the terrain and should adapt to the environment. With the evolution of robotics and Artificial Intelligence, and understanding diverse terrains, the techniques for terrain identification are also advancing with a major focus on safety.

Methodology: To make Terrain Detection and Identification more reliable we used instance segmentation which is a more sophisticated type of segmentation that goes a step ahead of semantic segmentation by performing both object detection and segmentation at the same time. In order to perform Instance segmentation, we used the YOLOv8 architecture which is considered to be the state-of-the-art CNN (Convolutional Neural Network) architecture. The YOLOv8 model was trained on an Off-road Terrain Dataset.

Results: Our findings indicate that the state-of-the-art YOLOv8 instance segmentation model provided the best results for terrain detection and segmentation with a threshold confidence of 0.60, and the results provide a maximum confidence of 0.92 which indicates an accurate segmentation model for the given terrain detection problem.

Conclusion: The present work motivates for a more viable hardware model that makes use of trained computer vision models and cutting-edge sensors that can be tested on different soils and terrain. The results obtained can be used to study about the different Terrains and select the most suitable model, this in turn drives for further research in the subject of Terrain Identification and Detection.
Breast Cancer Classification and Predicting Class Labels Using ResNet50
Authors: T Sunil Kumar Et al.
Published: January 2024

Numerous studies have been conducted using Deep Learning paradigms to detect Breast Cancer. Breast cancer is a medical condition where abnormal cells in the breast grow uncontrollably, forming tumors. If not treated, these tumors can metastasize and spread to other parts of the body, potentially leading to life-threatening consequences. In the year 2020, there were approximately 2.3 million cases of breast cancer diagnosed in women, leading to around 685,000 deaths worldwide. By the close of 2020, there existed 7.8 million women who had been diagnosed with breast cancer within the preceding five years, solidifying it as the most widespread form of cancer globally. Breast cancer is observed across all countries, affecting women at various ages post-puberty, with incidence rates tending to rise in older age groups. The aim of this paper is to classify and predict the class labels of breast cancer. To achieve this, a ResNet50 model is utilized and mammography images are employed to locate cancer within the image and classify it to emphasize the affected area. The ResNet50 identifies mass regions and classifies them as either ductal carcinoma, inflammatory, triple negative or invasive cancer. The experimentation is carried out on breast cancer dataset and achieved 90.6% accuracy both for classification as well as prediction
Neural Networks and Cyber Resilience: Deep Insights into AI Architectures for Robust Security Framework
Authors: Sukhvinder Singh Dari Et al.
Published: January 2024

With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models.The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance.
Digital Investigation Forensic Model with P2P Timestamp Blockchain for Monitoring and Analysis
Authors: Layth Almahadeen Et al.
Published: January 2024

Forensic analysis of Blockchain data is a new field in police work. It's now one of the largest problems facing law enforcement. The paper discussed the worldwide need for digital forensics in law enforcement and Blockchain forensics to counteract crimes committed using Blockchain technology. It's been said that we've entered a new age of technology that's heavily dependent on the principles of Blockchain. The research produced a set of guidelines for Digital Investigators. on addition, a theoretical framework grounded on the concept of regular activity has been developed, and a legislative framework has been proposed to ensure that its illegal purpose will always be punished severely.
Improving Rainfall Prediction Accuracy Using an LSTM-Driven Model Enhanced by M-PSO Optimization
Authors: Bhushankumar Nemade Et al.
Published: January 2024

Precipitation, particularly rainfall, plays a crucial role in the economic productivity of the agricultural sector. In regions characterized by unpredictable rainfall patterns, accurately predicting future precipitation is vital for designing effective rainwater harvesting systems and formulating strategies to address potential challenges. The contemporary meteorological community faces a significant dilemma when it comes to forecasting heavy rainfall, as it has far-reaching implications for economic stability and human survival. Moreover, heavy rainfall often serves as a primary trigger for recurring natural disasters like floods and droughts, which impact regions worldwide on an annual basis. The constantly changing nature of our climate presents a formidable barrier to achieving highly precise forecasts of precipitation using traditional statistical methods. Current models used for forecasting rainfall demonstrate less than optimal performance when dealing with complex and non-linear datasets. This study presents a novel method that evaluates the effectiveness of combining Long Short-Term Memory (LSTM) with Modified Particle Swarm Optimization (M-PSO) compared to established rainfall forecasting systems. Experiments conducted using this proposed LSTM-M-PSO model have yielded significant improvements in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) when predicting monthly rainfall. Consequently, the proposed LSTM-M-PSO method showcases its suitability for applications in global climate projection, particularly when working with extensive datasets. Its improved accuracy in forecasting rainfall holds promise for addressing the critical challenges posed by unpredictable precipitation patterns and their significant impacts on agriculture and society.
English Teaching Quality Assessment Based on Fuzzy Comprehensive Evaluation Model
Authors: Wei Jia Et al.
Published: January 2024

English Teaching Quality Assessment is a crucial aspect of evaluating and enhancing the effectiveness of English language education programs. To achieve a comprehensive understanding of teaching quality, a Fuzzy Comprehensive Evaluation Model is employed. This model combines qualitative and quantitative assessments, considering various factors such as teaching methods, student engagement, curriculum design, and instructor proficiency. Based on the multi-level model of Fuzzy mathematics, this paper designs a set of teaching Quality Assessment training suitable for English Teaching for Medical Students and applies it to teaching courses to improve their understanding of comprehensive functions and provide reference for future online teaching. In terms of curriculum structure, this paper uses Fuzzy analysis method and literature analysis method. Based on the literature, this paper discusses the training module, training theme, training content, training time and training mode. Summarize and organize the consultant's opinions, and finally formulate the training program. According to the teaching syllabus of comprehensive Quality Assessment student, teaching courseware was prepared, teachers conducted teaching demonstrations and recorded them as high-definition videos, and then cloud classroom teaching platform was built to teach community English. In terms of teaching practice, this subject took the tertiary medical institutions as the object, and investigated and evaluated the qualified primary care staff. Before training, the Fuzzy mathematics multi-level knowledge questionnaire, English Teaching Quality Assessment knowledge questionnaire, English Teaching Quality Assessment attitude, behavioral intention and self-efficacy scale were used to investigate the English Teaching. After completion, the methods were used to investigate English Teaching, and the different degrees of student effects were compared. Finally, a self-designed questionnaire was used to investigate English Teaching to understand the comprehensive evaluation of the satisfaction degree of student courses.
Current Situation and Innovative Methods of Brass Music Teaching Based on Network Information Technology
Authors: Li Liu Li Liu
Published: January 2024

The intersection of education and technology has witnessed a significant transformation in recent years, with a particular focus on optimizing remote learning experiences. Maintaining network resources for brass music education is crucial to ensure a seamless and effective learning experience. Hence, this paper proposed SDNcOIT (Software-Defined Networking cloud Optimal Information Technology) model in the context of brass music education, a discipline that demands real-time audio and video interactions. The proposed SDNcOIT model uses the cloud environment integrated SDN architecture for the evaluation of brass music education. The constructed network comprises Software-Defined Networking, cloud technology, and a rule-based model, the SDNcOIT system presents a compelling case for enhancing the delivery of music education to students worldwide. The SDNcOIT model implemented the optimization model within the SDN environment for the analysis of the evaluation of the user experience in the cloud environment. The proposed SDNcOIT model uses the gradient descent optimization model for the computation of the brass music in the cloud environment. Through the implementation of the optimization process within the SDNcOIT the features are optimized in the cloud with SDN architecture for the analysis of user experience. The findings from this study reveal substantial performance improvements over time, exemplified by reduced latency, increased throughput, minimized packet loss, and elevated user satisfaction. These improvements are pivotal for a discipline where the quality of audio and video content is paramount. The scalability of the cloud infrastructure ensures that the system can accommodate varying dataset sizes, adapting seamlessly to the dynamic requirements of a growing brass music program. Furthermore, the rule-based model's growing adherence to network behavior results in an exceptionally efficient and well-optimized network, aligning harmoniously with the educational objectives of brass music instruction.
Digital Money in the Indian Context: An In-depth Exploration of its Evolution, Implications, and Future Prospects within the Framework of Central Bank Digital Currency
Authors: August Keshav Et al.
Published: January 2024

This study conducts a thorough investigation of digital Money in the distinctive context of India, clarifying its development, consequences, and future possibilities, specifically in relation to Central Bank Digital Currency (CBDC), analyzing its potential in the Indian context, its crucial role in the current digital revolution, and the advantages it offers to our country, enterprises, and various user groups.The study proposes aligning our economy with the ongoing digital transition in the global technology scene. In an intangible domain characterized by inexorable advancement, the shift towards a digital economy becomes essential to enhancing our worldwide position.This research, conducted as an exploratory study, utilizes ideas derived from current literature, case studies, books, and electronic resources. this comprehensive approach offers a clear understanding of key aspects of digital money, such as the idea of blockchain, its many forms and sectors, including wholesale and retail, and the examination of real-world applications in different nations. The study examines investigating phenomena such as initial coin offers (ICOs) and the revolutionary possibilities of Central Bank Digital Currency (CBDC) and examines important literature issued by the Reserve Bank of India, the Bank of England, and existing works on centrally backed currency, as well as by the Bank for International Settlements (BIS).The data is enhanced through documentary analysis. The article concludes with a detailed and thoughtful discussion, providing valuable ideas and outlining potential future paths. As a result, it is essential to thoroughly categorize user groups for virtual currencies, especially those that have central bank support. this study provides a comprehensive grasp of the complex processes surrounding digital money and its potential to revolutionize the Indian socio-economic system. It is a significant resource for policymakers, industry experts, and researchers.
Deep Learning Driven QoS Anomaly Detection for Network Performance Optimization
Authors: Madhuri Ghuge Et al.
Published: January 2024

In modern, ever-changing network environments, QoS must be high to provide reliable and efficient services. This study tests Deep Learning (DL), specifically CNN, LSTM, and a hybrid CNN-LSTM model, to identify abnormalities using QoS measurements like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The study evaluates DL-based QoS management using UNSW-NB15 data. The hybrid CNN-LSTM model excels at QoS management, identifying anomalies in key metrics with few false detections. This method captures intricate network data patterns and interrelationships using deep learning, improving anomaly detection accuracy and efficiency. A hybrid model is used to quantify QoS parameters like Availability, Bandwidth, Latency, Jitter, and Packet Loss. The results show high values for Packet Delivery Ratio (PDR), Throughput, Round-Trip Time (RTT), Variation in RTT, and Packet Loss Rate (PLR), proving the proposed approach's effectiveness in maintaining QoS. The CNN, LSTM, and suggested hybrid model evaluation metrics include Accuracy, Precision, Recall, and F1-Score. The hybrid model outperforms the individual models with 98.67% accuracy, precision, recall, and F1-Score. This proves its anomaly detection resilience. False Positive Rate and True Positive Rate show that the hybrid model performs best, with a 0.01 false positive rate and 0.98 true positive rate. Graphical representations help visualize DL model parameter comparisons and False Positive/True Positive rates. DL-based methods, particularly the hybrid CNN-LSTM model, are crucial for QoS anomaly detection in this study. Measurable results show the model improves network dependability, resource allocation, and user satisfaction. The study also suggests researching advanced deep learning methods for real-time network processing and scalable solutions.
Elevating Security Measures in Cyber-Physical Systems: Deep Neural Network-Based Anomaly Detection with Ethereum Blockchain for Enhanced Data Integrity
Authors: Jagdish F. Pimple Et al.
Published: January 2024

The rapid development of physical device-based data collection in emerging technology needs smart, secure, and intelligent transmission. Cyber physical systems compete with the requirement of intelligent transmission of data. In cyber physical systems, security is a very challenging task due to the heterogeneous connections of devices in real time. This paper proposes a novel methodology for cyber-attack finding in cyber physical systems. The proposed system employed a DNN-deep neural network for the categorization of normal and attack data. The employed deep neural network design for 4 hidden layers for the detection of anomalies. For the secured transmission, we employed the blockchain process in Ethereum. The process of Ethereum generates blocks of blockchain with headers and transmits data over the cyberworld to the physical world with the alteration of data. For the authentication of the projected algorithm tested on two real-time datasets, such as NSL-KDD15 and CIDDS_001. The working of proposed algorithm is very promising in compression of existing algorithms of deep learning like RNN-recurrent neural networks, DBN, and DNN.
Realtime Anomaly Detection in Healthcare IoT: A Machine Learning-Driven Security Framework
Authors: Vaishali V. Raje Et al.
Published: January 2024

Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of  Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. Feature engineering is essential for accurate anomaly detection. Integrating HMM and SVM capabilities is the framework’s core. HMM accurately represent concealed states within divided data sequences and analyze patterns of change over time. After that, an independent-trained SVM examines each state. Using proximity to a decision hyperplane in feature space, this SVM can classify data points as normal or anomalous. This method augments HMM temporal capabilities with SVM classification efficiency, increasing sensitivity to anomalous patterns and reducing false positives. The framework’s exceptional performance is shown by extensive evaluations on the PhysioNet Challenge 2017 dataset, which includes diverse ECG recordings with labeled anomalies. HMM-SVM outperforms Naive Bayes, LSTM, and Random Forest with 98.66% accuracy. The framework also has high precision, recall, and F1-score, indicating a refined ability to detect real anomalies and reduce false alarms. The framework prioritizes real-time understanding and application alongside its remarkable precision. HMM-SVMs reveal hidden data state changes, revealing context and possible causes of anomalies. Modular design and efficient algorithms enable seamless integration of real-time functionality in low-resource IoT devices, enabling quick and effective security responses. To conclude, this study introduces an HMM-SVM framework for fast Healthcare IoT abnormality detection. The framework emphasizes comprehensibility and real-time applicability while achieving high accuracy. This framework can protect patient data, improve device security, and create a more reliable healthcare IoT ecosystem.
DBSCAN Based inductively coupled DSTATCOM for PQ improvement
Authors: Kundala P.K.Y. Et al.
Published: January 2024

This research study explains the feasibility of implementing machine learning (ML) oriented inductively coupled distributed static compensator (IC-DSTATCOM) for power quality (PQ) enhancement. The issue involved in declining the power quality (PQ) using direct coupled static compensator (DC-DSTATCOM) was identified as a hazardous disappointment. Hence, to improve the quality, coupling transformer is served in unification with DC-DSTATCOM. Also, the recent growth of machine learning (ML) systems and progression of computational resources, with unpredicted data obtainability, has inspired the researchers. In this study, density based spatial clustering of application with noise (DBSCAN) is employed by using its own learning mechanism (LM) using MATLAB/ Simulink. This controller contains six subnets. Among them, six subnets are employed for active tuned weight extraction whereas other three subnets are used for reactive part. Moreover, the abovesaid devices are triggered with the help of generated reference supply current. A case education is reviewed in detail to demonstrate the operation of both DC-DSTATCOM & IC-DSTATCOM. Finally, the IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, upgrading in power factor, load balancing, and potential regulation etc. To examine the effectiveness, simulation outputs of the IC-DSTATCOM is presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 grid code.
Chondroitin Sulfate Proteoglycans Are De Facto Cellular Receptors for Human Papillomavirus 16 under High Serum Conditions
Authors: Fons Nathan
Published: January 2024

Heparan sulfate proteoglycans (HSPGs) have previously been identified as the predominant host attachment factors used by human papillomaviruses (HPVs) prior to infection. Here, we demonstrate that in vitro, HPV binding to HSPGs is strongly dependent on the surrounding experimental conditions, including the concentration of fetal bovine serum (FBS). We found that high concentrations of FBS can block HSPG-binding sites and cause a dependence on sulfated chondroitin sulfate proteoglycans (CSPGs) as alternative initial viral receptors. Further, we demonstrate that use of a human-derived alternative to FBS, human platelet lysate, also occludes HSPG-dependent binding, causing a shift toward CSPGs for viral attachment. As HPV infection of basal epithelial cells is thought to occur at sites of microtrauma with exposure to high serum levels and platelet factors, these unexpected findings highlight a possible role for CSPGs as important cellular receptors for the binding and infectivity of HPV in vivo.
The social supply of cannabis among young people in Australia
Authors: Lenton Simon , Grigg Jodie , Scott John , Barratt Monica , Eleftheriadis Dina
Published: January 2024

Cannabis is the most prolifically used illicit drug in Australia, however, there is a gap in our understanding concerning the social interactions and friendships formed around its supply and use. The authors recruited cannabis users aged between 18 and 30 years throughout Australia, to explore the impact of supply routes on young users and their perceived notions of drug dealing in order to provide valuable insight into the influence that reciprocal relationships have on young people’s access to cannabis. Findings reveal that the supply of cannabis revolves around pre-existing connections and relationships formed through associates known to be able to readily source cannabis. It was found that motivations for proffering cannabis in a shared environment were related more to developing social capital than to generating financial gain. Given this, often those involved in supply do not perceive that they are breaking the law or that they are ‘dealers’. This social supply market appears to be built on trust and social interactions and, as such, presents several challenges to law enforcement. It is suggested that there would be benefit in providing targeted education campaigns to combat social supply dealing among young adults.