Reviewerly/exHarmony huggingface dataset:
High Accuracy Classification of Populations with Breast Cancer: SVM Approach

Breast cancer is one of the most common cancers diagnosed in the United States.Breast cancer can occur in both men and women.The number of deaths associated with this disease is steadily declining, largely due to factors such as earlier detection and a new personalized approach to treatment.In this article, we offer a highly accurate and reliable classification approach based on feature engineering and an improved support vector machine (SVM) classifier.We examine a dataset with 30 features and use in-depth data analytics and visualization to pinpoint the top nine features that have a significant impact on classification accuracy.The SVM classification outperformed other classifiers, including kernel extensions, with a high accuracy of 99.12%.The study stresses the value of machine learning in medical diagnosis, notably in the early detection of breast cancer, and indicates the possibility for further research in this area utilizing deep learning architectures.Early detection of breast cancer is critical, and our findings contribute to the growing body of knowledge in this area, opening new avenues for improving cancer diagnosis and patient care.
Analyzing Political Sentiment of Indic Languages with Transformers

This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts, while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.
AN ANALYSIS OF PUBLIC INVESTMENT ON HIGHER EDUCATION IN INDIA

Higher education is the integral part of social infrastructure which determines the destiny of any nation. Public policy on education is one of the most significant areas of contemporary development strategy and in a developing country like India, investment on education assumes most important place in such a policy. The higher education sector in India has witnessed a paradigm shift in recent years, in order to shape the future social and economic progress it is necessary to expand, restructure and raising the quality of education in the country. Development of human resources is one of the main purposes of Higher Education, which will play a critical role in preparing the workforce to be constructively utilized. Therefore, India needs to show more concern over higher education in terms of quantity and quality. In this background, the paper presents a birds eye view of the public expenditure by the centre and the state on higher education along with sector wise allocation in the country. An attempt has been made to analyze the growth and trends in public investment with the help of statistical tools like correlation and regression analysis. KEY WORDS: Higher Education, Public Investment, Sector-wise Allocation, Gross Domestic Product.
Effectiveness of E-Module Literacy Movement Learning Devices to Increasing Mathematical Concept Understanding Ability

This study aims to determine the effectiveness of the assisted e-module literacy movement learning device software Flipbook Maker html5 type in improving students understanding of mathematical concepts. The background of this research is the low interest in reading, the ability to read and interpret reading and learning in the classroom is more teacher center so that literacy and the ability to understand concepts are rarely trained. This research is a research and development (R&amp;D) method using the ADDIE model (Analyze, Design, Development, Implementation, and Evaluation). This research was conducted at Karya Nasional Sindangwangi Vocational School with research subjects namely class XII students of Motorcycle Engineering Skills Competency. The effectiveness test phase was carried out by students of class XII TSM 2 with a total of 34 students. Data collection techniques using test instruments in the form of questions pretest and posttest then analyzed using descriptive statistics. The results showed that the average value average yield pretest namely 53,82 and the average value of the results posttest ie 82,35. The results of the effectiveness test reached moderate criteria, with a percentage of 62,0483% so that the e-module that had been developed could be said to be "effective enough" to improve students ability to understand mathematical concepts.
Multifactor QoE scores in IoT topologies

Quality of Experience (QoE) is key to Internet of Things (IoT) applications. Indeed, depending on the nature of the application, different application message loss and latency requirements lead to different QoE metrics. In this context, and considering as inspiration the Mean Option Scores (MOS) typically associated with Real Time Communication (RTC), this paper introduces QoE scores that can be used to assess the performance of IoT applications. Taking into account physical, link, network, transport, and application layer features like the transmission rate, network layer loss and latency, the topology and the session layer protocol under consideration, linear regression mechanisms are used to estimate these QoE scores. Some of these features are regular system parameters while others are extracted from standard mechanisms and protocols used for traffic analysis as Key Performance Indicators (KPIs). This model is then trained and evaluated against a large number of Wireless Personal Area Network (WPAN) and Low Power Wide Area Network (LPWAN) architectures.
Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence

We describe a roadmap for a fully digital artificial intelligence (AI)-augmented nonclinical pathology laboratory across three continents. Underpinning the design are Good Laboratory Practice (GLP)-validated laboratory information management systems (LIMS), whole slide-scanners (WSS), image management systems (IMS), and a digital microscope intended for use by the nonclinical pathologist. Digital diagnostics are supported by tools that include AI-based virtual staining and deep learning-based decision support. Implemented during the COVID-19 pandemic, the initial digitized workflow largely mitigated disruption of pivotal nonclinical studies required to support pharmaceutical clinical testing. We believe that this digital transformation of our nonclinical pathology laboratories will promote efficiency and innovation in the future and enhance the quality and speed of drug development decision making.
Visualization and classification of Radar Emitter Pulse Sequences based on 2D feature map

With the development of modern electronic countermeasure technology, radar reconnaissance equipment receives pulse sequences of increasing density and complexity. Achieving concise as well as robust feature representation of Radar Emitter Pulse Sequences (REPS), and improving the classification and recognition performance of REPS signals have always been the focus at present. Current research didn’t consider the distribution information and co-information buried in REPS data. To solve this problem, we propose to visualize and classify REPS signals based on 2D feature map. First, we extract the Sequence Distribution Property (SDP) of REPS for spatial coding to obtain an encoded feature vector that is deformed into a 2D matrix. Three types of informative 2D matrices generated from REPS are combined into a color 2D feature map. Different from previous feature extraction methods, the proposed method can not only express the single sequence information concisely and accurately but also express the correlation of multiple parameter sequences synchronously in the interaction of different color channels. Second, based on the 2D feature map, we design a ‘L&FCN’ framework for classification, in which we fed the 2D feature map into a network composed of lower convolution neural layers and fully connected layers to judge the category and output recognition result. Finally, we put forward a novel strategy of integrating global and local segments’ information of REPS to train the network to so as to make it converge with smaller classification errors. Simulation results verify the effectiveness and superiority of our methods.
PENGARUH MODEL PEMBELAJARAN INKUIRI BERBANTUAN MEDIA KAPINDO (KANTONG PINTAR DORAEMON) TERHADAP HASIL BELAJAR MATEMATIKA

The purpose in this study is to know the average difference in students' mathematics learning outcomes after applying the inquiry learning model with KAPINDO media. This type of research is a pre-experimental design with a one-group pretest-posttest design. This research was conducted in class V SD 6 Klumpit. The sample in this study were 24 students. There are two variables, the independent variable, namely the inquiry learning model with KAPINDO media, and the dependent variable used, namely student learning outcomes. This type of research is quantitative research. The data analysis technique used was the paired sample t-test on the pretest and posttest results with a significance value (2-tailed) of 0.000 &lt;0.05. The results showed that the application of the inquiry learning model with KAPINDO media had an influence on students' mathematics learning outcomes, with an average student mathematics learning result before being given treatment of 58.58 and after using the inquiry learning model with KAPINDO media of 82.42. The difference in the average results has a difference of 23.84 which shows a significant effect on students' mathematics learning outcomes.
Exploring the Effectiveness of Cybersecurity Training Programs: Factors, Best Practices, and Future Directions

Cybersecurity education is essential in equipping individuals with the proper knowledge and skill sets to protect devices, computers, networks and data from cybersecurity risk. This research examines past research in cybersecurity education and training, evaluates the existing literature on Cybersecurity risk to identify research gaps and suggests the future direction of the research phenomenon. The paper evaluates the mechanics that influence the effectiveness of cybersecurity training programs. The paper also presents best practices and success factors for developing cybersecurity training programs. The research presents the benefits of cybersecurity training and awareness program underpinned around addressing human vulnerability to promote a better and positive cybersecurity awareness culture within organisations. It also highlights the need for a company to adhere to legal and regulatory requirements, which is essential in mitigating cybersecurity risk. The findings of this work propose a model for examining the relationship between cybersecurity training and user behaviour; this is made up of the input, process and output components, which are presented with a model to show the connection of the three elements. Key words: Cybersecurity awareness, Training effectiveness, Human factor, Organisational support, Evaluation methodologies, Security culture, User behaviour Proceedings Citation Format Sadiq Nasir (2023): Exploring the Effectiveness of Cybersecurity Training Programs: Factors, Best Practices, and Future Directions. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 151-160. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P18
Cybercrime On Social Media In Nigeria: Trends, Scams, Vulnerabilities and Prevention

Cybercrime on social media in Nigeria allow for an in-depth analysis of the role of social media in facilitating cybercrime activities in Nigeria, focusing on the prevalent scams, tactics used by cybercriminals, and the vulnerabilities that individuals and organizations face. It would also provide an opportunity to delve into the specific case studies, such as advance fee fraud, romance scams, and phishing, as well as exploring additional examples and their implications. By examining the impact of social media on cybercrime in Nigeria, this research contributes to a better understanding of the evolving nature of cyber threats in the digital landscape. It would also shed light on the specific challenges faced by Nigerian society, particularly with the high number of social media users in the country. Furthermore, the research proposes recommendations and strategies to enhance cybersecurity awareness, digital ethics, and protective measures for individuals, businesses, and policymakers to mitigate the risks associated with cybercrime. Keywords: Cybercrime, Social Media, Nigeria, Trends, Scams, Vulnerabilities, Prevention Proceedings Citation Format Oni Damilola, Arshad Emmanuel &amp; Pham Bich Ngoc (2023): Cybercrime On Social Media In Nigeria: Trends, Scams, Vulnerabilities and Prevention. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 143-150. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P17
Efforts to Increase Elementary Students' Interest in Learning through the Implementation of Role Playing Strategies in Learning Indonesian at SD/MI

Boredom in the learning process is not something that is rarely encountered in class. It is hoped that with the right learning strategy it can attract students' attention and increase students' enthusiasm and interest in learning, therefore the right strategy is needed so that students are not bored and bored when learning is in progress. This paper aims that learning strategies, especially learning strategies for Role Playing can minimize student boredom and create an exciting classroom atmosphere. The type of this research is qualitative using data collection techniques in the form of observation and documentation. From this study it can be found that: (1) students participate actively and focus on playing a role, (2) students seem happy when playing a character/role in the fictional story.
Computational Approaches to Phonology: Advances in Speech Recognition and Synthesis

This article explores the field of computational approaches to phonology, with a particular focus on recent increases in popularity and speech synthesis. Phonology, as a department of linguistics, deals with the observation of sounds and their work in language. Recent improvements in this area have resulted in major improvements in accuracy and efficiency, towards the integration of deep recognition strategies together with recurrent neural networks (RNNs) and convolutional neural networks (CNNs). the models have tested the ability to capture complex phonetic patterns and decorate the overall performance of computerized speech reputation structures. These advances have seen packages in multiple domains, such as virtual assistants, text-to-speech structures, and language acquisition tools. This article looks at the main computational procedures used in phonology, along with characteristic-based representations, rule-based modes, and statistical methods. It explores demanding situations related to phonological evaluation, which include phoneme segmentation, pronunciation variants, and language-specific problems. In addition, this paper discusses mixing phonological knowledge with device learning strategies, which enables the development of robust and correct speech recognition and synthesis systems. As usual, this research represents remarkable advances from computational methods to phonology, particularly in the geographical area of speech recognition and synthesis. the combination of technology and linguistic insights has increased the accuracy, naturalness, and efficiency in processing spoken language. These enhancements open up new opportunities to enhance human-pc interaction, language recognition, and many other packages that rely on speech-based technologies.
Reconstruction Low- Resolution Image Face Using Restricted Boltzmann Machine

Low-resolution (LR) face images are one of the most challenging problems in face recognition (FR) systems. Due to the difficulty of finding the specific features of faces, the accuracy of face recognition is low. To solve this problem, some researchers are using an image reconstruction approach to improve the resolution of their images. In this research, we are trying to use the restricted Boltzmann machine (RBM) to solve the problem. Furthermore, a labelled face in the wild (lfw) database has been used to validate the proposed method. The results of the experiment show that the PSNR and SSIM of the image result are 34.05 dB and 96.8%, respectively.
Identification Types Of Student Learning Modalities In Physics Subjects With Expert Systems Using Bayes Theorem Method

Learning modality is a person's way of absorbing and processing information effectively and efficiently. This study aims to determine the results of the identification types of student learning modalities in physics subjects with an expert system using the Bayes theorem method, and the accuracy of the Bayes theorem method in identifying types of student learning modalities in physics subjects. This study uses the Bayes theorem method because it can produce a parameter estimate by combining information from the sample and other information that has been previously available to determine the results of the learning modality. This study uses 21 characteristics of learning modalities, 3 types of learning modalities, and 30 test cases obtained from an expert physics teacher at SMA Sumsel Jaya Palembang. Based on the tests that have been carried out, the results show that the system has an accuracy of 90% in identifying types of student learning modalities in physics subjects. It can be concluded that the Bayes theorem method can be used to identify types of student learning modalities in physics subjects.
Application of the Group Investigation Learning Strategy in Social Studies Subjects at the Elementary Education Level

This study aims to describe the application of group investigation learning strategies to social studies subjects at MI/SD. This study uses a qualitative method, namely research by collecting data from an object by analyzing data that is seen, observed, so that new knowledge and explanations are obtained that are more complex and detailed than something that happened. The results of this study are that the Group Investigation learning strategy can be used in social studies learning. The advantage of the group investigation type cooperative learning strategy is that students can have a good sense of responsibility and students can also individually or in groups discuss to solve problems. The weakness of the group investigation cooperative learning strategy is that this strategy requires many stages, causing little material to be conveyed, this strategy requires a long learning time, and it takes time for students to adjust to this learning strategy, causing the class atmosphere to become tense and tense disturbing the concentration of other students.
Prototype Sistem Pembagian Beras Bansos Berbasis IoT Menggunakan E-KTP

This study aims to design an IoT-based social assistance rice distribution system using e-KTP which can provide convenience in distributing rice to beneficiaries, practical and automatic social assistance only by bringing the e-KTP that has been registered without having to queue any longer. The results of the study show that the social assistance rice distribution system makes it easy for related village officials to distribute social assistance rice efficiently and effectively. The result of testing this system is that on each e-KTP there is a unique code that is different for each card which will be processed by the microcontroller and the data will be compared with the data registered in the database, with that if the user is registered the LCD will display a successful registered notification, but only Only e-KTPs that have been registered can collect this social assistance rice. Furthermore, there is a pin access on this tool as a second security which allows the recipient to be sick and can be represented by accessing it through the pin that has been inputted into the database.
Rancangan Sistem Informasi Penyewaan Kamar Kos Pada Rumah Singgah Jerry

This research aims to facilitate employees in data processing at Boarding Room Rentals at Jerry Bogor Halfway House, resulting in precise and accurate data. The system is made with the Research &amp;Development (R&amp;D) method which a research method based on facts, bservations, library research and data analysis. The system is built using the desktop-based Java programming language and MySQL based data storage. The results of this research is the existence of a new system that can facilitate and help employees’ performance especially in the front office in carrying out their tasks optimally, no mistakes and can facilitate employees to process the data bookings, rentals, and payments stored in the database.
Learning Methods in English Class

English is a language for writing and speaking. To comprehend the meaning of information, thoughts, and feelings and to use language to advance science, technology, and culture is to comprehend and express communication. If you want to keep up with how the world is changing, you need to know and understand English. This study looked at how English class learning methods and facilities affected students' motivation to learn more about art and culture. The results show that better facilities improve students' motivation. Students will be able to learn more effectively and more easily if the appropriate method is chosen. In addition, the students' motivation to learn can be influenced and bolstered by the availability of a quality learning facility and the effective implementation of the learning method.
the THE IMPACT OF COVID-19 IN EDUCATION FIELD AND TEACHERS AS AGENTS OF CHANGE

Covid-19 cause many changed to the education field and the teachers. In normal situation and condition, teaching and learning process is done face to face at the class, but it is very different now. The teachers and the students have to do online learning using learning applications. These changed causes the stakeholders face much impact especially in the process of transferring the knowledge. This research aimed to describe the impact of Covid-19 in education field and teachers as agents of change. This research used literature study to find out related theories to the research problems. The research sources are journal, book, documentation, internet, and library and so on. The results show that Face to face learning has to move to daring learning at home using technology internet. The teachers have to learn more about how to use daring media as a tool in the teaching and learning process like how to apply Google classroom, Facebook, Twitter, Zoom, and YouTube. The teacher also needs to learn how to make an online attendance list, online materials, online quiz, online presentation or discussion, and so on. Not only the teacher but also the students will do the same thing as the teacher learns how to do daring learning at home.&#x0D; Key words: Covid-19; Education field; teachers