bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems
Authors: Das Abhijit , Pramod Pramod 
Published: January 2023

In the Internet of Things arena, smart gadgets are employed to offer quick and dependable access to services. IoT technology has the ability to recognize extensive information, provide information reliably, and process that information intelligently. Data networks, controllers, and sensors are increasingly used in industrial systems nowadays. Attacks have increased as a result of the growth in connected systems and the technologies they employ. These attacks may interrupt international business and result in significant financial losses. Utilizing a variety of methods, including deep learning (DL) and machine learning (ML), cyber assaults have been discovered. In this research, we provide an ensemble staking approach to efficiently and quickly detect cyber-attacks in the IoT. The NSL, credit card, and UNSW information bases were the three separate datasets used for the experiments. The suggested novel combinations of ensemble classifiers are done better than the other individual classifiers from the base model. Additionally, based on the test outcomes, it could be concluded that all tree and bagging-based combinations performed admirably and that, especially when their corresponding hyperparameters are set properly, differences in performance across methods are not significant statistically. Additionally, compared to other comparable PE (Portable Executable) malware detectors that were published recently, the suggested tree-based ensemble approaches outperformed them.
Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model
Authors: Ramana N. Venkata , Nair S. Anu H. , Sanal Kumar K. P. 
Published: January 2023

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.
Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering
Authors: Dwivedi Sampurnanand , Singhal Vipul 
Published: January 2023

An early diagnosis of brain disorders is very important for timely treatment of such diseases.Several imaging modalities are used to capture the anomalities by obtaining either the physiological or morphological information. The scans obtained using imaging modalities such as magnetic resonance imaging (MRI) are investigated by the radiologists in order to diagnose the diseases. However such investigations are time consuming and might involve errors. In this paper, a fuzzy c-means clustering method is used for brain MRI image segmentation.The GLCM features are obtained from the segmented images and are subsequently mapped in to a PCA space. A support vector machine (SVM) classifier is used to classify brain MRI images taken from BRATS-13 images. The method is evaluated by employing various performance measures such as Jaccard index, Dice index, mean square error (MSE), peak signal to noise ratio (PSNR). The results show that the method outperforms the existing methods.
Modernized Wildlife Surveillance and Behaviour Detection using a Novel Machine Learning Algorithm
Authors: Gogula Sreenivasulu , Khanna M. Rajesh , Neelappa Neelappa , Sundaram Ajith , Kumar E. Rajesh , Kumar R. Sravanth 
Published: January 2023

In a natural ecosystem, understanding the difficulties of the wildlife surveillance is helpful to protect and manage animals also gain knowledge around animals count, behaviour and location. Moreover, camera trap images allow the picture of wildlife as unobtrusively, inexpensively and high volume it can identify animals, and behaviour but it has the issues of high expensive, time consuming, error, and low accuracy. So, in this research work, designed a novel wildlife surveillance framework using DCNN for accurate prediction of animals and enhance the performance of detection accuracy. The executed research work is implemented in the python tool and 2700 sample input frame datasets are tested and trained to the system. Furthermore, analyze whether animals are present or not using background subtraction and features extracted is performed to extract the significant features. Finally, classification is executed to predict the animal using the fitness of seagull. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure and error.
An Adaptive Feature Centric XG Boost Ensemble Classifier Model for Improved Malware Detection and Classification
Authors: Pavithra J. , Samy S. Selvakumara 
Published: January 2023

Machine learning (ML) is often used to solve the problem of malware detection and classification and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To manage the issue, an efficient Adaptive Feature Centric XG Boost Ensemble Learner Classifier “AFC-XG Boost” novel algorithm is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the process of XG Boost classifier in several stages to optimize the performance. At preprocessing stage, the data set given has been noise removed, normalized and tamper removed using Feature Base Optimizer “FBO” algorithm. The FBO would normalize the data points as well as performs noise removal according to the feature values and their base information. Similarly, the performance of standard XG Boost has been optimized by adapting Feature selection using Class Based Principle Component Analysis “CBPCA” algorithm, which performs feature selection according to the fitness of any feature for different classes. Based on the selected features, the method generates regression tree for each feature considered. Based on the generated trees, the method performs classification by computing Tree Level Ensemble Similarity “TLES” and Class Level Ensemble Similarity “CLES”. Using both method computes the value of Class Match Similarity “CMS” based on which the malware has been classified. The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 seconds for 75000 samples
Question-Driven Ensembles of Flexible ETAS Models
Authors: Mizrahi Leila , Nandan Shyam , Savran William , Wiemer Stefan , Ben-Zion Yehuda 
Published: January 2023

AbstractThe development of new earthquake forecasting models is often motivated by one of the following complementary goals: to gain new insights into the governing physics and to produce improved forecasts quantified by objective metrics. Often, one comes at the cost of the other. Here, we propose a question-driven ensemble (QDE) modeling approach to address both goals. We first describe flexible epidemic-type aftershock sequence (ETAS) models in which we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates during model calibration. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. Then we consider 64 QDE models in pseudoprospective forecasting experiments for southern California and Italy. QDE models are constructed by combining model parameters of different ingredient models, in which the rules for how to combine parameters are defined by questions about the future seismicity. The QDE models can be interpreted as models that address different questions with different ingredient models. We find that certain models best address the same issues in both regions, and that QDE models can substantially outperform the standard ETAS and all ingredient models. The best performing QDE model is obtained through the combination of models allowing flexible background seismicity and flexible aftershock productivity, respectively, in which the former parameterizes the spatial distribution of background earthquakes and the partitioning of seismicity into background events and aftershocks, and the latter is used to parameterize the spatiotemporal occurrence of aftershocks.
Integrated, Personalized Care for Older People
Authors: Stange Kurt C. , Gaglioti Anne , Bindas James 
Published: January 2023

AbstractMedical science has made magnificent advances by dividing complex problems into their component parts. The strength of clinical trials, and the resulting evidence‐based clinical guidelines, is that they isolate a particular phenomenon or therapy from its context to assess its effect without the confounding of diverse contextual factors. However, the health and health care of whole people, and particularly older people who often live with multiple chronic conditions, is context‐dependent. Older people are not well served by the current fragmented medical knowledge and organization of health care, which is impersonal, often ineffective, and dangerous. More helpful approaches to health care for older people begin with the whole of the person in their family and community circumstance, and then examine the parts of people (including individual strengths, as well as diseases and disabilities) in context.We interpret three case studies in light of research on what patients and primary care clinicians say matters in health care. What matters are 11 domains of care: accessibility, a comprehensive, whole‐person focus; integrating care across acute and chronic illness, prevention, mental health, and life events; coordinating care in a fragmented system; knowing the patient as a person; developing a relationship through key life events; advocacy; providing care in a family context; providing care in a community context; goal‐oriented care; and disease, illness, and prevention management.The health and health care of older people requires contextualized knowledge and personal knowing, supported by integrated systems that treat health care not as a commodity, but as a relationship.
Long-Term Survival in Patients with Cancers
Authors: Sakr Rokia A , Nasr Abdelrahman A , Zineldin Eman I , Gouda Mohamed A 
Published: January 2023

Objectives: Long-term survival is an important endpoint in management of different malignancies which is rarely assessed due to unfeasibility of follow-up for long duration of time. In this study, we explored real-world data on cancer’s long-term survival using historical records from the Surveillance, Epidemiology, and End Results (SEER) Program. Besides reporting the 5-year relative survival, we analyzed the 10- and 20- year survival rates for different types of cancers. Additionally, survival trends as a function of time, age, and tumor type were reviewed and reported. Methods: We used SEER*Stat (version 8.3.6.1) for data acquisition from the SEER 9 Regs (Nov 2019 Submission) database. Data of patients diagnosed with cancer between 1975 and 2014 were retrieved and included in the analysis. Results: For patients diagnosed with any malignant disease (n = 4,412,024), there was a significant increase in median overall survival over time (p<0.001). The 20-, 10-, and 5-year survival rates were higher in solid tumors compared to hematological malignancies (50.8% vs. 38%, 57% vs. 47.4%, and 62.2% vs. 57.4%, respectively). The highest 20-year relative survival rates were observed in thyroid cancer (95.2%), germ cell and trophoblastic neoplasms (90.3%), melanoma (86.8%), Wilms’ tumor (86.2%), and prostate cancer (83.5%). Conclusions: Long-term follow-up data were suggestive of high 20-year relative survival rates for most tumor types. Relative survival showed an improving trend over time especially in solid tumors.
Keywords: Survival; Neoplasms; SEER Program; Prognosis; United States.
The Effect of Adding Various Levels of Crystal Coconut Sugar on Total Solid and Color in Red Dragon Fruit Yoghurt (Hylocereus polyrhizus L)
Authors: Anwar Syamsul , Zulaikhah Siti Rahmawati , Sidhi Arif Harnowo 
Published: January 2023

The purpose of this study was to determine the effect of adding various levels ofcrystalline coconut sugar to the total solids and color of red dragon fruit yogurt. This study used an experimental method with a completely randomized design (CRD) with 4 treatments and 5 replications. The treatments used in this study were the addition of 0%, 5%, 10% and 15% (w/v) crystal coconut sugar. The variables observed were total dissolved solids and color, which included brightness (L*), greenish-reddish color (a*), and bluish-yellowish color (b*). The results showed that all treatments had an effect on the total dissolved solids and the color of the yogurt produced. The total solids in the addition of 15% crystal coconut sugar had the highest total solids with 18.38°Brix and the lowest was the addition of 0% with 8.02°Brix. The brightness (L*) of Yoghurt color is lowest at the addition of 15% crystal coconut sugar, the addition of crystal coconut sugar shows a greenish-reddish color (a*) will tend to lead to a greenish color, and for a bluish-yellowish color (b*) will tend to lead to a yellowish color.
‘Firearming’ Fairytales: NRA and Gun Culture in American Fan-Fiction
Authors: Nugroho Bhakti Satrio 
Published: January 2023

Gun issue becomes one of the most polemic issues in the United States alongside racism. Regardless, the last major gun control legislation to make it into law was the assault weapons ban in 1994 as part of a larger crime-related bill approved during Bill Clinton presidential period. After the assault weapons ban expired, American society is threatened by the increasing numbers of gun violence issue such as mass shooting and gun homicide. In this case, NRA involvement is vital towards gun culture in the United States. As non-profit organization, NRA has influential lobbying for any policies towards gun policies. Thus, this paper discusses the dissemination of gun culture on NRA family website www.nrafamily.org. In 2016, Amelia Hamilton rewrote two Grimm’s fairytales “Little Red Riding Hood (Has a Gun)” and “Hansel and Gretel (Have Guns). Since gun becomes commodity, these NRA versions of fairytales can be analyzed as part of consumer manipulation by using consumer capitalist theory. Thus, this analysis shows that those fan-fictionalized fairytales consist of two main aspects: gun as protector and gun culture as common culture in the United States. It embraces the rationalization of gun ownership’ in the United States despite its lethal consequences.
Wasei Eigo Analysis on Instagram Social Media
Authors: Masrokhah Yuni , Fajrin Berliana 
Published: January 2023

Wasei eigo is a word or combination of words originating from English and then adopted into Japanese vocabulary with different meanings from native speakers or in other words, wasei eigo is English made by Japanese people. The aims of this study are (1) to describe the process of wasei eigo formation, (2) to classify the types of wasei eigo, and (3) to conclude the use of wasei eigo on Instagram social media. The main theory of Japanese word formation is based on Tsujimura's theory and for wasei eigo classification is taken from Shibasaki, Tamoka and Takatori's theories. The method used in this study is a qualitative descriptive method with Miles and Huberman theory analysis techniques. The data source comes from social media in the form of Instagram. The results of the study from 45 wasei eigo data that were studied, shows that there were four processes of wasei eigo formation: affixation, compounding, clipping and borrowing. In classification there were 15 data from Imizurekata, 11 data from Tanshukukata, 2 data from Junwaseikata and 12 data from Eigohyougenfuzaikata. There are several words that can be categorized as more than one type, namely there are 4 data included in imizurekata and tanshukukata, and 1 data included tanshukukata and eigohyougenfuzaikata.

Keywords: Wasei eigo, Sociolinguistics, Instagram
In Puppet Master’s Hand: Discursive Manipulation Strategies in Socio-semiotic Perspective
Authors: Anggraheni Dini , Anandha Anandha , Rosaria Stefani Dewi 
Published: January 2023

Love has a huge impact on someone's life. To be in love, someone tries to find a romantic relationship. Indeed, romantic love can be developed without having frequent or real-life interactions, such as with a stranger from a dating app. Telecom and internet fraud has become a public hazard as a new type of contactless crime, with crooks targeting large numbers of unsuspecting victims. This paper linguistically investigates the main character of the Tinder Swindler documentary film. Simon, the main character discursive strategies designed to manipulate girls' minds into accepting, even doing anything that he says to get profit to fulfill his luxurious lifestyle. Regardless of the scientific and technological tools utilized by swindlers, language is the primary communication. From a socio-semiotic standpoint, this research shows that criminal suspects use discourse resources to fabricate false information and construct false identities through discursive practice, to gain victims' trust, trick, deceive, and manipulate them into giving out confidential information and funds. This study investigates the dynamic process of selecting and building identities in the social-cultural context by altering associated discourse resources and techniques. This research also offers a socio-semiotic strategy for preventing online and telecom scams.

Keywords: discursive manipulation, film, identity, scam, socio-semiotic, dating application
Analysis of the Metaphors in the Song ‘All Too Well’ by Taylor Swift as a Tool for Storytelling
Authors: Sofian Ela Sofiarti 
Published: January 2023

Language is used by humans to communicate and express their ideas as well as feelings, which can be done through literary works such as a song. A song conveys an idea and/or a feeling through its lyrics. In a song, the lyrics may contain various metaphors that carry contextual meaning in order to convey a feeling and emotion. Thus, people may have different interpretation of a song. This applies to all kinds of songs, such as the longer version of Taylor Swift's song, "All Too Well". Therefore, this research will discuss 1) the meaning of the metaphors in the song to help create a better storytelling and 2) the type of metaphors used in the song. The metaphors found in this song will be analysed using Ullman’s theory on metaphors. The method used in this research is descriptive qualitative method because this research will describe the results of this research instead of depict them through numbers. The data in this research is the metaphors found in the song, and the sampling technique for this data is purposive sampling. This technique is chosen because this technique help supplies the data needed to answer the problems of this research. In relations to the aims of this research, there are 26 metaphors found to be used in this song; 3 anthropomorphic metaphors, 1 animal metaphor, 19 concrete to abstract metaphors, and 3 synesthetic metaphors. The use of these metaphors is intended not only so the story in the song can be better delivered to the listeners to make it more captivating, but also for the listeners to be able to understand the depths of emotions the song-writer wished to convey in the song. Therefore, the metaphors have significant influences in the act of storytelling through the lyrics of the song.
DECISION SUPPORT SYSTEM FOR SELECTING DOCTORS IN APPLICATION X USING ANALYTICAL HIERARCHY PROCESS (AHP)
Authors: Ka Teh Yu , Tisno Atmojo Wahyu 
Published: January 2023

Doctors are one of the human resources contributing to the world of health, especially in the current COVID-19 pandemic situation. With the pandemic and increasing technological developments, this sat have sprung up many digital-based health service applications that can help the community, especially its users in health consultations, looking for health articles, and hospital searches. In this digital-based health service, if you want to consult online or make an appointment, there are many choices of doctors available with various criteria. This method is used to obtain weighting (Eigen Vector) criteria used to facilitate making decisions, which is to choose a doctor for consultation or appointment. With three criteria (experience, consultation price, and user satisfaction level) and alternatives (Dr. A, Dr. B, Dr. C) obtained weighting (Eigen Vector) or the best alternative option is Dr. A with a weighting value of 0.509, which has 27 years of experience criteria, a consultation price of Rp. 30,000.00, and a user satisfaction rate of 96%.
DATA MINING CLUSTERING FOOD EXPENDITURE IN INDONESIA
Authors: Julianto Indri Tri , Kurniadi Dede , Nashrulloh Muhammad Rikza , Mulyani Asri 
Published: January 2023

The availability of food in a country is determined by a conducive climate. Prolonged droughts, floods, and natural disasters, especially for food crop production areas, will have an impact on the availability of natural disaster conditions faced by all countries including Indonesia is the Covid-19 pandemic, where this will affect food security in Indonesia. Data mining is the process of discovering the hidden meaning of a very large data set. The technique used in this study is Data Mining Clustering and the validity index used is Davies-Bouldin. This study aims to determine the Food Security Strategy in Indonesia through the Data Mining Clustering process based on food expenditure data and the Indonesian people's food expenditure per capita. The methodology used is Cross Industry Standard for Data Mining using the K-Means and K-Medoids Algorithm. The best cluster for the K-Means Algorithm is K=7 with a value of 0.341 and for the K-Medoids Algorithm, it is K=7 with a value of 0.362. This research produces the best algorithm, namely K-Means with a value of 0.341, which has a smaller value than K-Medoids with a value of 0.362. The results showed that the regional. cluster with the highest average expenditure on food was cluster 5 covering the DKI Jakarta area, while the cluster with the lowest expenditure was cluster 6 covering Central Java, East Nusa Tenggara, Southeast Sulawesi, Gorontalo, and West Sulawesi. In cluster 6, it is necessary to implement a strategy to increase food security by increasing production capacity and food reserves in each region.