bluuebunny/crossref_metadata_2025 huggingface dataset:
HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities
Authors: Mahesh T. Dhande Et al.
Published: January 2024

In the rapidly evolving landscape of cyber security, the incessant advancement of malicious activities presents a formidable challenge, necessitating a paradigm shift in detection methodologies. Traditional methods, primarily reliant on static rule-based systems, exhibit palpable limitations in grappling with the dynamic and sophisticated nature of modern cyber threats. This inadequacy underscores the urgent need for innovative approaches that can adeptly adapt and respond to the ever-changing threat environment. Addressing this exigency, the present research introduces a novel hybrid machine learning model, ingeniously crafted to transcend the constraints of existing malicious activity detection frameworks. The proposed model synergizes the strengths of diverse machine learning strategies, including anomaly detection techniques including Isolation Forest and One-Class SVM, and validates the results of these classifiers using Random Forest and Gradient Boosting operations. The validated malware instances are classified into malware types using fusion of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) based Recurrent Neural Networks (RNNs) under real-time network configuration sets. This eclectic amalgamation not only leverages the unique capabilities of each algorithm but also harmonizes them to forge a more robust and precise detection mechanisms. The strategic integration of these algorithms facilitates a comprehensive analysis of network traffic and system logs, thereby significantly enhancing the detection accuracy. Furthermore, the model's adaptive learning component ensures its relevance and efficacy in the face of evolving cyber threats, a quintessential feature for contemporary cyber security solutions. Empirical evaluations, conducted using multiple malware datasets and samples, substantiate the model's superiority over existing methods. It exhibited a remarkable 10.4% improvement in precision, an 8.5% increase in accuracy, a 4.9% enhancement in recall, an 8.3% rise in AUC, a 4.5% boost in specificity, and a notable 2.5% reduction in detection delay. These compelling results underscore the model's potential in revolutionizing malicious activity detection, providing organizations with a more effective and resilient defense mechanism against a spectrum of cyber threats. The research culminates in a significant stride forward in cyber security, offering a robust, adaptive, and comprehensive solution that addresses the pressing need for advanced malicious activity detection, thereby bolstering the overall cyber security posture of organizations in the digital age sets.
Malware Detection and Analysis Tools
Authors: Rajesh Yadav Et al.
Published: January 2024

The huge amounts of data and information that need to be analyzed for possible malicious intent are one ofthe big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads.In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by usingmachine learning techniques to identify and classify unknown malware into their established families. Thissurvey paper gives an overview of the malware detection and analysis techniques and tools.
Hybrid Spam Filtering using Monarch Butterfly Optimization Algorithm with Self-Adaptive Population
Authors: Deepika Mallampati Et al.
Published: January 2024

Spam causes bottlenecks and congestion, reducing the speed, processing power, available memory, and bandwidth. Existing spam email classification methods need to be more accurate because of the large dimensionality of hybrid spam datasets. This makes the need for a feature dimensionality reduction technique that uses only associated features of the problem instead of all features in the dataset. This paper presents a feature selection based on the monarch butterfly optimization (MBO) algorithm that emphasizes less complexity and few features. This method is efficient and produces a more accurate classification. To improve further standard MBO algorithm performance, we introduce the population size in both subpopulations 1 and 2 will experience dynamic variations as the algorithm proceeds along its linear way. As the idea of a self-adaptive and greedy strategy is modified, the self-adaptive population monarch butterfly optimization (SPMBO) method is introduced, and only newly generated SPMBO individuals are eligible for the next generations if they are better individuals earlier before. Later, this paper proposes an email classification system based on k-nearest neighbors (k-NN) based on two distance metrics, explicitly Euclidean, and Manhattan, that also uses the SPMBO technique. This method seeks to determine whether a hybrid email is a spam. The efficiency of the proposed SPMBO algorithm was compared with standard MBO based on three datasets Dredze, Image spam hunter, and Spambase. Thus, the use of SPMBO results has shown superior as related to other authors' works in relevant fields.
Optimized Forecasting Air Pollution Model Based On Multi-Objective Staked Feature Selection Approach Using Deep Featured Neural Classifier
Authors: Chitra Paulpandi Et al.
Published: January 2024

In recent days air pollution has been an essential issue affecting the environment nature leads to various natural causes. Especially the Covid-19 pandemic period has a variation environment changes due to vehicle controls and industrial facts at regular intervals. So air pollution has different scaling factors before and after the pandemic, period produces non-scaled data features. Many methodologies provide the differential solution to analyze the air quality measurements under various conditions to make warnings to avoid air pollution. By the impact of exiting forecasting, ML approaches do not provide the accuracy in precision levels because feature dependencies are non-relevant in high dimension nature. To create the best Air quality index, we need to improve the feature analysis and classification objectives to produce higher prediction performance. This paper proposes a new forecasting model based on the Multi-objective Staked Feature Selection Approach (MoSFS) using the Deep Featured Neural Classifier (DFNC) model to predict air pollution. Initially, the Successive Feature Defect Scaling Rate (SFDSR) was carried out Auto Regressive Integrated Moving Average (ARIMA) rate for finding variation dependencies. The multi-objective relational successive feature index was scaled using the Spider Herding Algorithm (SHA) to select the features based on these variations in feature limits. Then the chosen features get activated to logical activation function with Long Short Term Memory (LSTM) and trained with a Fuzzified Convolution Neural Network (F-CNN) to predict the class by variance. This resultant factor proves the performance of RMSE values attaining the best level to forecast the features and in precision rate produce higher performance in classification accuracy compared to the other system.
From Pixels to Diagnoses: Deep Learning in Diabetes Detection
Authors: Sai Prakash .S Et al.
Published: January 2024

Diabetes mellitus is a chronic metabolic disease with a rising prevalence worldwide, affecting millions of individuals. Early detection and accurate classification of diabetes are crucial to reduce mortality rates and enhance the quality of life for affected individuals. Traditional diagnostic techniques for diabetes, such as blood testing and glucose tolerance tests, are costly, time-consuming, and often require substantial resources.This study proposes a deep learning model that utilizes the Pima Indian Diabetes dataset, consisting of health information from 768 individuals with attributes including blood pressure, glucose levels, BMI, etc. The aim is to overcome the limitations of traditional detection methods and develop a model capable of early detection and precise classification of diabetes.The classification of the data into diabetes and non-diabetic groups is done using a convolutional neural network (CNN) model. The experimental outcomes show the effectiveness of the proposed deep learning model, obtaining an accuracy of 94.2%, precision of 90.18%, recall of 98.9%, F1-score of 94.3%, Cohen's kappa of 88.5%, and ROC AUC of 94.4%. These findings indicate that a deep learning approach can be utilized to develop a model capable of accurately identifying diabetes in its early stages.Early identification of diabetes through the suggested deep learning framework holds promise for reducing the risk of complications associated with the disease. By leveraging the power of deep learning techniques, healthcare professionals can enhance their ability to detect and manage diabetes more efficiently, leading to improved patient outcomes and an overall reduction in the burden of this chronic condition.
Visual Storytelling: A Generative Adversarial Networks (GANs) and Graph Embedding Framework
Authors: K. Dinesh Kumar Et al.
Published: January 2024

Visual storytelling is a powerful educational tool, using image sequences to convey complex ideas and establish emotional connections with the audience. A study at the Chinese University of Hong Kong found that 92.7% of students prefer visual storytelling through animation over text alone [21]. Our approach integrates dual coding and propositional theory to generate visual representations of text, such as graphs and images, thereby enhancing students' memory retention and visualization skills. We use Generative Adversarial Networks (GANs) with graph data to generate images while preserving semantic consistency across objects, encompassing their attributes and relationships. By incorporating graph embedding, which includes node and relation embedding, we further enhance the semantic consistency of the generated high-quality images, improving the effectiveness of visual storytelling in education.
AI-Based Drone System for Medical Support in Congested Areas
Authors: Chandrashekhar kumar Et al.
Published: January 2024

Serving the needs of human beings is much more important to us. So, considering how to service the product or provide medicinal support is also very important to reach the destination. While supported by the local field-side forces, it is going through delays and getting affected by the delays in medical treatment. Why this failure in medical support? To avoid this delay, I have come up with a trend in technology in real life to send things like oxygen to the nearby hospital and make it emergency support on an urgent basis. At the same time, we can look for college and industry, food, and grocery items that we can utilize for the same methods. We can make it using AI technology used in the drone system and so by using different scales of sensors and cameras for recognition for bill service for auto-detection and bill payment as well. Nowadays, most people try to use their outside food purchases quickly, so we can get it via air drone easily without delay. This process is unintentionally free and GPS-based, with an advantage system to track the location. And confirmation of customer detection can help to unlock the lock-unlock the drone's locked body, get material, and lock the door once service is received. If there is any unwanted attack, it will be updated with the tracking system. There is an anti-drone system used here. This activity can be avoided.
HealthBlock: A Blockchain-IoT Fusion for Secure Healthcare Data Exchange
Authors: D. Rajalakshmi Et al.
Published: January 2024

Managing healthcare data while ensuring its security and privacy is critical to providing quality care to patients. However, traditional approaches to healthcare data sharing have limitations, including the risk of data breaches and the lack of privacy-preserving mechanisms. This research paper proposes a novel hybrid blockchain-IoT approach for privacy-preserving healthcare data sharing that addresses these challenges. Our system incorporates a private blockchain for protected and tamper-proof data sharing, with privacy-preserving techniques such as differential privacy and homomorphic encryption to protect patient data. IoT devices are utilized to collect and transmit real-time data, equipped with privacy-preserving mechanisms such as data anonymization and secure transmission protocols. Our approach achieved an accuracy rate of 98% for access control and a 99.6% success rate for data privacy protection. Furthermore, our proposed system demonstrated improved data storage and retrieval performance, with a data storage overhead reduction of up to 86% and a data retrieval time reduction of up to 81%. These results indicate the potential of our approach to enhance the security, privacy, and efficiency of healthcare data management, contributing to improved patient care outcomes.
Enhanced Internet Access in MANETs: A Robust and Efficient Gateway Detection Approach with Amplified Backing
Authors: Santosh Kumar, Dinesh Kumar Singh Atma Prakash Singh,
Published: January 2024

When a certain gateway is connected to a significant number of mobile devices, the quality of service begins to decline. As a result, we propose that the number of mobile devices up to which a specific gateway performs well within expectations be identified, and that this number be referred to as the gateway's capacity. The capacity of each node should then be verified in the optimum gateway discovery protocol before connecting to the gateway for the best results. Trust-based systems might help protect decentralised, self-organized networks like dedicated cellular carriers and dedicated vehicle networks against insider attacks. To guard against external threats, A/V encryption and access control might be utilised. Cryptographic systems, for example, are prevention-focused.
An Efficacious Deep Fusion based Tomato Leaf and Fruit Classifier using Convolutional Gated Recurrent Unit and Bonobo Optimizer
Authors: M. Sharmila Et al.
Published: January 2024

Agriculture is suffering a drastic blow because of sudden changes in environmental conditions and novel pathogenic attacks. These attacks have brought in heavy disruptionto the integrity of the food supply chain. In order to meet the growing global food demand,an automated system is needed for early identification of diseases and to enable smart farming with the advent of upcoming technologies. This paper proposes such an expert system that augments farming returns both economically and productively. Tomato leaf images are obtained from Kaggle repository and fruit images are self- captured, both making a total of 3676 images. Data is augmented using techniques like rotation, zooming, width shift, height shift, horizontal flip etc.Under preprocessing block, Wiener filter and Contrast Limited Adaptive Histogram Equalization model are used for noise smoothing and contrast enhancement.The U2Net architecture of convolutional neural networks is used for image segmentation after whichfeatures are extracted from a fusion layer based on InceptionV3 and EfficientNetB2 models. The final stage involves aConvolutional Gated Recurrent Unit classifier along with Bonobo optimizationfor obtaining optimal results.Evaluation metrics like accuracy, precision, recall, F1 score,Matthew’s correlation coefficient are calculated to find the effectiveness of the proposed system. The proposed algorithm is compared with currently prevailing algorithms like InceptionV3, MobileNet, VGG16, CNNandKernel Extreme Learning Machine and is found to produce promising results with an astounding accuracy of 96.90%.
Application of Grey Wolf Optimization based Multi Agent System for All Electric Naval Ship
Authors: Vijaya Kumar Begari Et al.
Published: January 2024

The concept of an all-electric naval ship, while delivering unequalled advantages in terms of efficiency and operational flexibility, has raised new concerns about stability and power flow regulation. The introduction of a full power electronics power system has resulted in additional system dynamics challenges, especially when dealing with the new medium-voltage direct current distribution. Researchers have considered two main approaches to ensuring the power system's secure operation: reducing the dynamics of the large load to operate in a range of dynamics compatible with traditional generation systems, or making the generator set smarter via its power electronics interface. Power availability is crucial in the shipping industry, which depends on regular power output and the reliability of power and propulsion systems. This study proposes a multi-level technique for hybrid power generation control. To do this, a mathematical model for each power system component is provided first, followed by a model of the entire on-board power system. The results demonstrated that the proposed approach successfully improves the stability of an interconnected power system when compared to GA-based MAS.
Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications
Authors: Anupama Ganesh Phakatkar Anupama Ganesh Phakatkar
Published: January 2024

An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy.
Enhanced Authentication Scheme for Mobility Model in Medical Wireless Sensor Networks
Authors: B. Naresh Kumar Et al.
Published: January 2024

The advent of wireless sensor networks has brought significant advancements in healthcare, enabling remote interactions between medical professionals and patients. However, ensuring the security of communication in Medical Wireless Sensor Networks (WSNs) poses different challenges. To address this, this paper introduces a novel authentication framework designed for doctors and patients. The proposed mechanism incorporates essential features such as mutual authentication, anonymity, and data integrity, safeguarding the Medical Wireless Sensor Networks (MWSN). Symmetric encryption techniques are employed to maintain the overall security of the system.
Reducing Features Within an Extensive Network of Medical Subject Headings Metadata to Enhance Deep Predictions
Authors: Zolzaya Dashdorj Et al.
Published: January 2024

Feature reduction in a large amount of Medical Subject Headings (MeSH) metadata for deep prediction involves selecting a subset of relevant features to improve the efficiency and effectiveness of deep learning models. MeSH is a controlled vocabulary thesaurus used for indexing articles in the life sciences and biomedical fields. We analyze a disease-symptom network exploiting MeSH metadata and configure a deep model for disease prediction based on symptoms. Dimension reduction techniques have yielded positive results in optimizing a large amount of Medical Subject Headings (MeSH) metadata for deep prediction with good accuracy. Therefore, our result highlights that decrease in the severity or degree of symptoms associated with a disease correlates with an improvement in the accuracy of disease prediction. This finding may have important implications for disease prediction models in healthcare: interpretation of results, clinical significance, practical implications.
Lung Cancer Detection Refined: A Study on SVM Hyperparameter Tuning Using BayesOptimization
Authors: Ashok Kumar Gottipalla Et al.
Published: January 2024

This research focuses on the application and enhancement of machine learning algorithms for the detection and differentiation of various types of cancers, with a primary emphasis on lung cancer. Central to this study is the integration of the Bayes Optimization algorithm for hyperparameter optimization and the XGBoost algorithm for predictive modelling. A significant aspect of this work involves the strategic reduction of hyper-features, aimed at refining the XGBoost model's performance. This process not only ensures a more efficient model but also contributes to a higher accuracy in cancer-type prediction. Additionally, a comparative analysis is conducted with other ensemble models to evaluate the relative performance improvements. The findings of this study are pivotal, as they demonstrate the optimized model's enhanced capability in accurately detecting different cancer types, particularly lung cancer, and show marked advancements over other contemporary models. The research highlights the potential of combining advanced machine learning techniques for significant improvements in oncological diagnostics and treatment planning.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
Authors: Yarlagadda Sri Charan
Published: January 2024

The plastic industry and consumer demand have both exploded since the 1950s. Plastic waste in the ocean has also skyrocketed, growing by a factor of 10 since 1980. Many animal species can't survive this kind of pollution. This is probably bad for people. Impacts plankton, which in turn modifies the carbon cycle. Effects global warming by adding to it. This list, by the way, is not comprehensive. When scarce resources are used inefficiently and without good planning, a great deal of waste is generated, which has a negative impact on the natural world. The notion of a circular economy (CE) has shown encouraging signs of being adopted at industrial and governmental levels as an alternative for the conventional but wasteful linear manufacturing lines. Through careful planning and subsequent reuse, recycling, and remanufacturing, CE strives to maximise the value of raw materials over a product's entire life cycle. Two cutting-edge technologies that can considerably aid in the widespread acceptance and application of CE in actual practises are artificial intelligence (AI) and machine learning (ML). This research delves into how AI applications are being included into CE.
Statistical Features of Seismicity Associated with Large Earthquakes on the Chinese Continent between 2008 and 2019 Based on Newly Detected Catalogs
Authors: Li Lu , Wang Baoshan , Peng Zhigang , Hou Jinxin , Wang Fang
Published: January 2024

The Chinese continent is seismically active and is an important region for analyzing statistical behaviors of large intraplate earthquake sequences. A systematic and detailed record of large earthquake sequences provides the basis for such analysis in a tectonically complex region. In this study, using a graphic processing units-accelerated matched filter technique, we detected up to five times more events than standard earthquake catalogs to generate newly detected catalogs for 32 Ms≥6.0 earthquakes on the Chinese continent from October 2008 to December 2019. The statistical analysis of the newly detected high-resolution catalogs showed that strike-slip mainshocks have higher potential to have anomalous foreshock sequences but are followed by fewer aftershocks than those with other faulting styles. We also provided an example for the 2014 Yingjiang, Yunnan, earthquake sequence, in which the b-value shows a reduction for foreshock sequences and is followed by a recovery in the aftershock period. Our detected catalogs can be used to further study statistical behaviors of earthquake sequences, which is essential for understanding earthquake interaction and improving seismic hazard assessment on the Chinese continent and around the world.
The Application of German Criminal Law in the Occupied Polish Territories during World War II and Article 43 of the 1907 Hague Convention
Authors: Graczyk Konrad
Published: January 2024

The article is devoted to the issue of the relationship between the application of German criminal law in the occupied Polish territories (the so-called incorporated territories and in the General Government) and the provisions of international law, in particular the Hague Convention. The legal basis for the application of German criminal law, examples of the strictest, draconian regulations and examples of judgments in which they were applied are presented. Then, the regulation of Article 43 of the Hague Convention and comments in the German legal science regarding this regulation are discussed. Efforts were made to confront the achievements of the doctrine with the legal status introduced by the occupant on Polish lands during World War II. The conclusions drawn, indicating multiple violations of the provisions of the Convention by the German authorities, were additionally supported by post-war judgments of Polish courts.
Two Polish Romanists’ Voices on the Subject of Law in Times of War
Authors: Czech-Jezierska Bożena
Published: January 2024

Roman law scholars seldom leave the subject of their field of interest for contemporary law research studies, especially not for criminal law. Exceptional were views on law in times of World War II expressed by two famous Polish Romanists. The first of them was Rafał Taubenschlag – a famous Polish Romanist and papyrologist who lived in New York over the period 1940–1947. He published there in 1945 a paper Plea of Superior Order. Taubenschlag in his paper argued that the members of the German army could by no means plead obedience to superior orders as justification for their participation in the massacre of unoffending civilians, in the exercise of inhuman cruelties such as torturing and slaughtering of women and children, on the grounds that they regarded those orders as legal and that their superiors did not intend to commit a crime by these acts. They were not bound to obey such orders – he emphasized, and if they did, they did so at their own risk and must be held responsible, as such outrageous acts could not be considered as falling under the heading of military duty. Taubenschlag’s argumentation was used in the Nuremberg trials by Robert H. Jackson – the Chief United States Prosecutor at the International War Crimes Tribunal in Nuremberg. The other Polish Romanist was Kazimierz Kolańczyk from the University of Poznań, who published, due to his activity in the Institute for Western Affairs in Poznań, a paper German Legislation as a Crime Weapon. In this paper, based on numerous examples, he emphasized that German legislation imposed on Polish territories during World War II was not a manifestation of law but lawlessness and injustice.
Criminal Law in Ukraine in the Context of War (2022–2023): Responding to Challenges
Authors: Szupjana Marjana , Burdin Wołodymyr
Published: January 2024

The maintenance of martial law, as well as the realities of life during war, require the legislator’s response it in various areas of life, in particular in matters of regulation of criminal and legal relations. In this respect, a significant number of changes and additions were made to the Criminal Code of Ukraine in response to the challenges that arose in connection with the war. In particular, new types of crimes were introduced (e.g. collaboration – Article 111¹), the statutory features of several of the existing ones were modified (e.g. Article 361), and sanctions for certain crimes were increased if they were committed during martial law (e.g. Article 111 item 2).
Service in a Foreign Army and Related Factual Basis of Criminal Offences in the Interwar Czechoslovak Criminal Law
Authors: Knoll Vilém , Kotroušová Denisa , Schwarzová Aneta , Byszowiec Marián
Published: January 2024

The topic of the article is service in a foreign army and the related factual basis of criminal offences of military recruitment into and service in an enemy army (military treason). These offences were regulated in Czechoslovak interwar law by the adopted Austrian-Hungarian law. The article presents the issue from the perspective of the law applicable in the territory of the Czech Republic today. Thus, it deals with the legal regulation adopted from Cisleithania. The offences in question were regulated by both military and civil criminal law. The original legislation was gradually supplemented, amended and partially unified by Czechoslovak legislation, in particular the Act on the Protection of the Republic of 1923 and the Conscription Act of 1927. The final part of the article outlines the issue of related post-war amnesties and the criminal prosecution of persons in connection with their service in the Spanish Civil War or with the organisation of their departure for such service.
PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS
Authors: ALTUNKULAH Elif , ENSARİ Yunus
Published: January 2024

Proteins play crucial roles, including biocatalysis, transportation, and receptor activity, in living organisms. Moreover, their functional efficacy is influenced by their structural properties. Determining the three-dimensional structure of a protein is crucial to comprehending its catalytic mechanism, identifying potentially beneficial mutations for industrial applications, and enhancing its properties, including stability, activity, and substrate affinity. Although X-ray crystallography, nuclear magnetic resonance (NMR), and electron microscopy are employed to ascertain protein structures, many researchers have turned to bioinformatics modeling tools because of the high cost and time demands of these techniques. For structure prediction, there are three basic methods: ab initio (de novo), homology-based, and threading-based modeling techniques.

In this study, 11 modeling tools belong to different approaches were compared through modeling of various proteins; Geobacillus kaustophilus ksilan alpha-1,2-glucuronidase, Actinosynnema pretiosum bifunctional cytochrome P450/NADPH-P450 reductase, human high affinity cationic amino acid transporter 1 (SLC7A), human proton-coupled zinc antiporter (SLC30A) and Bacillus subtilis RNA polymerase sigma factor (sigY). Generated models were validated through QMEAN, QMEANDisCo, ProSA, ERRAT and PROCHECK tools. All of the studied proteins could be successfully modeled using homology modeling techniques, while some of the proteins could not be effectively modeled using threading or ab initio-based methods. YASARA generated reliable models for proteins that contain heteroatoms, such as P450 monooxygenases, because other tools exclude heteroatoms in their produced structures. Among approaches for modeling without templates, AlphaFold is a potent tool. On the other side, well-known template-based tools like YASARA, Robetta, and SWISS-MODEL have arisen. These results will help scientists choose the best protein modeling strategy and tool to guarantee high-quality structures.
Literation of the Acculturation of Islamic Culture in Yogyakarta, Indonesia
Authors: Maksum Ali , Azizah Nur , Afif Muhammad , Sahide Ahmad
Published: January 2024

This community service intends to promote literacy relevant to the acculturation of Islamic culture in Yogyakarta, Indonesia, particularly in the Serambi Da'wah institution under the Muhammadiyah Bangunjiwo Barat umbrella. Understanding the acculturation of Islamic and local culture is crucial because it will influence the social and religious dynamics of a community with a high degree of pluralism. In addition, this will have an effect on the Islam preaching cycle, particularly in the West Bangunjiwo region and generally in Yogyakarta, which is heavily influenced by Javanese culture and the Yogyakarta palace culture. Da'wah institutions must therefore comprehend how the dynamics of Islamic and local culture develop in society. This community service is expected to contribute to community development, particularly within the Muhammadiyah organization, in order for its messages to be accepted by the community, particularly in Bangunjiwo Barat, Yogyakarta, Indonesia
Implementation of the "SANTRI" Software at the MBS Wanayasa Islamic Boarding School
Authors: Abdurahim Ahim , Fatmawati Dewi , Saud Ilham Maulana , Budiarso Aditya Prasetyo , Agustin Apia Dewi
Published: January 2024

The purpose of community service activities is the empowerment and independence of the MBS Wanayasa Islamic boarding school in using the "SANTRI" software. This activity is the result of collaboration between Bank Indonesia, the Indonesian Institute of Accountants and LPM UMY. "SANTRI" software has been provided by the Indonesian Institute of Accountants in collaboration with Gama Techno. Implementation of the "SANTRI" Software using the RAD (Rapid Application Development) method with 5 stages of activity, namely 1) Studying the business model of the MBS Wanayasa Islamic boarding school, 2) Designing a chart of accounts, 3) Testing the reliability and suitability of the "SANTRI" application using simulation data 4) . Assisting the implementation of the "SANTRI" software to users, and 5) monitoring and improvement activities. The results of the activity show that the MBS Wanayasa Islamic boarding school has several activities, namely learning activities, student dormitories, several business units such as laundry, kitchens, animal husbandry, agriculture and Islamic boarding school cooperatives, and revenue is divided into two sources, namely acceptance of infrastructure development activities and acceptance of routine activities. Based on these activities, a chart of accounts is prepared in accordance with existing activities. The chart of accounts is used to test the reliability of the "SANTRI" software. After the "SANTRI" software is adjusted to the needs of the activities and reporting of the MBS Wanayasa Islamic boarding school, the next stage is to assist the implementation of the "SANTRI" software to the treasurer of the foundation and the treasurer of the MBS Wanayasa Islamic boarding school. To ensure the continued use of the "SANTRI" software, the community service team carries out continuous monitoring and improvement
“Blazing the Road to Success Through Entrepreneurship”, a Motivational Training for Indonesian Migrant Workers in Hong Kong
Authors: Rahayu Meika Kurnia Puji , Suryani Dewi Amanatun , Rahmawati Fitri Maulidah , Umaroh Sri Nasiati
Published: January 2024

The role of Indonesian migrant workers as contributors to the country's foreign exchange cannot be denied anymore. However, with such a large amount of money, not all workers can have an established life when they are no longer working as migrant workers Entrepreneurship can be an appealing option for returned migrant workers to utilize the skills and experiences they gained abroad while contributing to their local community. However, there is a need for entrepreneurship training for Indonesian Migrant Workers to prepare for their retirement. On April 30, 2023, the training was held. The training was attended by approximately 60 migrant workers in Hong Kong from various communities, not only members of the Hong Kong PCIA but also the Church community, Underground Music, and others. To ensure the continuity of community service activities at the end of the training event, the community service team provided grants in the form of funds for program sustainability. The grants are expected to help them to be able to maintain routine activities even though the UMY and UNISA community service teams do not directly assist them
Improving The Skills of Corpse Management for The Community of Karangrejek, Karangtengah, Imogiri, Bantul.
Authors: Setiawan Asep , Damarjati Cahya , Wibisono Danang , Salsabila Zulfa
Published: January 2024

Karangrejek is one of the hamlets in Karangtengah village, Imogiri, Bantul. Apart from tourism, entrepreneurship and agriculture, one of the potentials that needs attention is the religious aspect. Among the problems faced by Muslims in Karangrejek hamlet is related to the understanding of the fiqh of corpses and also the practice of organizing corpses. In the hamlet, there are still many corpse care practices that can be said to just follow the ancestral traditions and pay less attention to the guidance taught in Islam. In addition, it is also a concern that so far there has been no team of residents who really understand and are trained in how the fiqh of corpses and their management. When one of the villagers dies, “the mbah kaum (mudin)” is the one who takes care of it, while the other Muslims only help as much as they can. In the management, especially in the process of bathing and shrouding, it is also “the mudin” who does it. There is no division of tasks, such as if the deceased is a man, the one who takes care of him should also be a man and vice versa, as the Shari'a requires. Another problem is the existence of several rituals that are actually inherited from ancestors, which are still maintained and practiced even though when viewed from the aspect of sharia, there are several things that need to be straightened out. Based on these problems, the author and the service team will conduct training on the fiqh of the corpse and how to organize the corpse according to Islamic law, in order to increase the understanding and skills of Karangrejek residents in the management of the corpse. The implementation of this service by conducting training, direct practice and discussion in the form of FGD (Forum Group Discussion) with mosque takmir in the Karangrejek area and the surrounding community who are usually involved in the management of the body. The output targets of this service activity include scientific articles, training books, and videos of fiqh studies of the corpse and tutorials on the practice of organizing the corpse according to Shari'ah.
Preventive Measures on Sexual Freedom in The Maesan Wetan Area In Islam and Health Sciences
Authors: Rohmansyah Rohmansyah , Mustar Muhamad Yusvin
Published: January 2024

Abstract. The problem experienced by society in general is sexual freedom which is practiced by most teenagers and parents on average. Free sex or sexual freedom is carried out by force and there is consensual consent for men and women. They are free to do so even though there is already a statutory law that regulates the prohibition of sexual harassment. This service is carried out in the Maesan Wetan area and aims to anticipate sexual freedom in society. The method of this dedication is a qualitative method and lectures with three stages, namely: the first stage, presentation of free sex material in hadith and medical science. The second stage, the question and answer method. The third stage is evaluation. The results of this service are: First, the public's knowledge of the dangers of free sex increases. Second, the strategy is to stay away from it and overcome it by providing educational aspects for their children to stay away from free sex. Third, the community has the ability to know the bad consequences arising from free sex, namely HIV-AIDS which causes the perpetrators to develop the disease. There is no medicine for this disease that can cure it and only relies on drugs that can reduce and reduce the number of HIV viruses, namely Antiretrovirals (ARVs) so that they do not enter the AIDS stage. In addition, ARVs function to prevent opportunistic infections that can attack the human immune system.
Waste Sorting and Organic Waste Processing Training at SD Muhammadiyah Banyuraden
Authors: Aristyasari Yunita Furi , Faizah Restu , Pratama Wahyu Arif , Karim Mashuda Abdul
Published: January 2024

Recently, garbage has become an emergency problem in Yogyakarta. The Piyungan integrated waste processing site was blocked so which there was a buildup of waste in several places. This makes the government and community immediately moved to deal with the waste problem. The waste processing and management movement began massively in the community. Similarly, this movement needs to be carried out in educational institutions because schools are part of the agents of change in society. The activity method is carried out by pretest and post-test assessment, education, and training by Mrs. Neni Widuri from Gowasari Training Center. The participants who participated in training and education were 12 people contained of teacher and staff. The activity was held at Muhammadiyah Banyuraden school. The purpose of this community service activity is to provide education and training for teachers and employees of Muhammadiyah Banyuraden elementary school (SD Muhammadiyah Banyuraden) regarding organic waste processing by making ecoenzymes. The assessment results showed that socialization and training activities had a significant impact on increasing participants' knowledge about organic waste management and its processing into ecoenzymes. The average increase in knowledge of 12 teachers and employees of Muhammadiyah Banyuraden elementary school was 49%. Community service activities are expected to bring changes to the school environment of SD Muhammadiyah Banyuraden and the environment around the school
Mother-Baby Friendly Community Health Services Training Course For Maternity Staff to Prevent Stunting in Children
Authors: Huriah Titih , Yuniarti Falasifah Ani , Binti Abdul Hamid Siti Hazariah
Published: January 2024

. Stunting is one of the significant nutritional issues around the world. Stunting is a common phenomenon among Indonesian and Malaysian children. In 2019, statistics from the National Health Morbidity Survey (NHMS) Malaysia reported that 21.8% of Malaysian children under four years were stunted. Some studies revealed that exclusive breastfeeding was one of the highest risk factors for stunting. All health workers caring for women and children during the post-natal period and beyond have a crucial role in establishing and sustaining breastfeeding. Many health workers cannot fulfill this role effectively because they have yet to be trained. The materials in the training course are designed to make it possible for trainers, even those with limited experience in teaching the subject, to conduct up-to-date. Community service activities are carried out at Tengku Ampuan Afzan Hospital in Kuantan, Malaysia. The procedure for community service activities is divided into several stages; the first was coordination with the Head of the Tengku Ampuan Afzan Hospital in Kuantan, Malaysia. The next stage is implementing a mother-baby-friendly services training course for maternity staff to prevent stunting. The last stage is the evaluation of the competencies of nurses. The results of the community service show that 25 nurses have the competencies to become exclusive breastfeeding counselors and are certified
Self-Empowerment of Pregnant Women to Maintain Wellness During Pregnancy
Authors: Astuti Yuni , Nurmaguphita Deasti , Wijayanti Elviana , Apriliani Nanda
Published: January 2024

Women naturally experience pregnancy as part of the growth process. To prepare for pregnancy and the development of the fetus, pregnant women go through physical and psychological changes. Pregnant women often complain of discomfort during their pregnancies, with symptoms including back pain, abdominal pain, a developing physique, and prenatal anxiety. According to the findings of interviews with several expectant mothers in Kebondalem Lor Village, they were concerned about the state of their pregnancies and back pain. and have never engaged in prenatal exercise and are still unsure what to do. The community service team offers pregnant women self-empowerment as a solution to this issue. On May 6 and May 24, 2023, two meeting sessions with offline sessions were used to carry out community service activities. Prenatal yoga, prenatal nutrition, and anxiety management for pregnant women were the topics covered at the second meeting, while the first activity focused on changes in physiological and psychological adaptation to pregnancy, independent monitoring of fetal well-being, physical activity for pregnant women, and prenatal yoga. According to the results, pregnant women's average knowledge increased both before and after the activity. Participants in the activities expressed great satisfaction with the way things were done, how information was contributed, and how perceived complaints were decreased
Improving Skills and Knowledge of Healthy Drink Intake in PKK Cadres Sempu Village Wonokerto Turi Sleman
Authors: Astuti Yoni , Hanifati Farhana , Suharto Agus
Published: January 2024

The temperature in Sempu, Village of Wonokerto Turi Sleman is cool on weekdays, while at dusk and dawn the temperature is cold because it is located on a medium-high plain. This cold condition requires warm and healthy intake, especially for elderly people. This is because Sleman Regency has the highest average of life expectancy (UHH) in Indonesia. The pre-elderly population (45-59 years) is 53,146 people and the elderly population (> 60 years) is 55,967 people out of a total population of 1,090,567 people. The large number of elderly population requires serious attention in the health sector so that it does not become a burden. Thus, a promotive preventive program is needed. The morbidity rate for the elderly is the proportion of health problems that interfere with daily activities. The morbidity rate of the elderly in rural areas tends to be higher than in urban areas. It is important for the elderly to maintain their health by consuming nutritious food and additional mineral intake which has high health benefits, considering that the elderly has experienced a decrease in the performance of their organs. There are many natural ingredients that have healthful properties for the body, especially for the elderly in rural areas, and they are easy to grow in Turi with cool weather conditions. Among those natural ingredients is ginger (Zingiber officinale). Phenol compounds are antioxidants, which are thought to protect cells from damage. Ginger has properties for treating impotence, coughs, aching rheumatic pain, headaches, rheumatism, back pain, and colds. Those health problems often affect the elderly. Not many people, especially PKK Dusun (Hamlet of) Sempu, know the benefits of ginger widely and how to process it as a healthy drink. So far, ginger is only used as cooking spice. The purpose of this service is to increase knowledge and skills in making ginger-based healthy drinks for the community and PKK in Sempu, Turi. The method used was the lecture and practice method. Conclusion: This activity was fully completed, and the community was enthusiastic and grateful. The result of this activity was an increase in knowledge and understanding of the benefits of ginger by 15.6%.