bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effectiveness of the Pipek Model (Concept Map-Based Interactive Learning) in Learning Modern Algebra
Authors: Suradi  , Djam’an Nurwati 
Published: May 2021

Abstract
The major objective of this study was to investigate the effectiveness of the PIPEK model in learning modern algebra. This study was to identify the effects of the use of PIPEK model on students’ engagement and students’ learning in modern algebra course at Mathematics Department in Semester 2, 2020. During this study, the students completed achievement test and questionnaire. The study revealed that, in terms of surface strategy, students tended to memorize formulas and the method for solving problems. Moreover, students still relied heavily on the lecturer’s instructions; Particularly, related to the aspects of the attitude, student participant generally found modern algebra as a boring and difficult course. Futhermore, students felt stressed and anxious; The findings related to the behavioral aspect indicated that although student’s attention were high, they were not diligent in accomplishing independent work task. The finding also demonstrated that, the level of mastery achievement of student learning outcomes has not achieved classical completeness. However, the PIPEK model has given students the opportunity to engage more in student center learning process.
The Development of Higher Order-Thinking Skills (HOTS) Instrument Assessment in Physics Study
Authors: Arafah K , Amin B D , Sari S S , Hakim A 
Published: May 2021

Abstract
This study aims to determine the characteristics of the test instruments in the mechanics of SMA to measure higher order thinking skills. This study also aims to see the validity, reliability, level of difficulty, and differentiation of items using the classical theory approach. This study adapted the development model from Mardapi. The items developed were tested on 114 students at one of High School in Makassar, South Sulawesi. The results show that the items that have been developed have content validity with a coefficient of 0.860. Of the 35 test items developed, there were 7 invalid items, with a test reliability of 0.593. The proportion of item difficulty level has the distribution of 0% hard items,74.3% medium items, and 25.7% easy items. The distinguishing power of test items has shown good criteria.
The Need of Science Learning to Empower High Order Thinking Skills in 21st Century
Authors: Bahri A , Jamaluddin A B , Muharni A , Fikri M J N , Arifuddin M 
Published: May 2021

Abstract
One of the characteristics of 21st century learning is the need to empower higher order thinking skills, especially in science learning. This research is a survey research that aims to determine how the learning process in the field and the desire of students to learn science in empowering higher order thinking skills. Respondents in this study were 102 students of the Faculty of Mathematics and Natural Sciences, The State University of Makassar for the academic year 2019/2020. The instrument used in the instrument sheet was a questionnaire to measure student needs and a deep interview sheet. The results showed that students considered the learning process still negative in its application process. In addition, students also need an innovative learning process that can activate students in the learning process, train critical and creative thinking skills facilitate students to always learn collaboratively, and be able to apply their knowledge in everyday life. Therefore, a solution is needed, namely the development of appropriate learning models in science learning in empowering higher order thinking skills during the learning process.
Analysis Anthropometry Comfort of Table and Chairs in the Elementary School Class
Authors: Alahudin Muchlis , Solekha  , Simorangkir Yosi Valentina 
Published: May 2021

Abstract
This study aims to calculate the level of anthropometric compatibility of elementary school students with the design of learning furniture that they use in school. This research uses a descriptive method. The data analyzed included the dimensions of the table and chairs as well as the Anthropometry data of grade 1, grade 4 and grade 6 elementary school students. Research sites include Inpres Tanah Miring I Elementary School, Inpres Tanah Miring IV Elementary School, and Inpres Tanah Miring VII Elementary School. The results showed that the dimensions of the desk had a height of a work table is 77 cm, a width is 50 cm, and a length is 120 cm. The dimensions of the chair has a height of 43 cm, width of the back is 39 cm, seat width is 40 and seat length is 120 cm. The difference between the standard size and the size of the survey results in the spotlight is the difference in height over the work table for elementary school grade 1 is 13.5 cm, grade 4 is 7.5 cm and for grade 6 is 1.5 cm and table height for grade 1 is 21 cm, grade 4 is 16.5 cm, and for grade 6 is 10 cm.
Blended Learning for Undergraduate Students: Validity, Practicality, and Effectivity
Authors: Sabtiawan W B , Sari D A P , Purnomo A R , Widodo W 
Published: May 2021

Abstract
This study aimed to implement blended learning in Integrated Science Learning Course for undergraduate students by using three parameters, namely, validity, practicality, and effectivity. The research design was post-test group design. This research was conducted for eight months in Science Department, Universitas Negeri Surabaya. The data of research were collected in three ways, that were, validation, observation, and test. The results obtained in this study indicated that the blended learning was: (1) valid based on the expert judgments laid on didactic requirements, construction, content, and technical aspect; (2) practicable in which all teaching and learning instructions were implemented well; and (3) effective in which all students and groups learning could exceed the minimum limit score for cognitive learning outcomes and skills.
Real Time Fire detection and Localization in Video sequences using Deep Learning framework for Smart Building
Authors: Sridhar P , Sathiya R R 
Published: May 2021

Abstract
This work presents autonomous electrical fire-detection and localization using computer vision based techniques. The proposed work uses YOLO v2 to extract the electrical fire features more effectively than other conventional and machine learning approaches. This working model is tested on commercial and residential building as well as indoor and outdoor environments. This framework has achieved high detection accuracy and low false alarm rate. Besides, the proposed frame work can be used for early real-time electrical fire detection in surveillance videos and we present experimental results for electrical fire localization in CCTV footage using the deep learning architecture proposed in this work.
Internet of Things enabled Parking System In Smart Cities
Authors: Prakash G Suriya , Ragavan G Vijaya , Chandru S Vikram , Sarathy C , Suresh K P 
Published: May 2021

Abstract
Internet of Things (IOT) performs a important function in connecting the encompassing environmental matters to the community and made clean to get entry to the ones un-net matters from any far off location. It’s inevitable for the humans to replace with the developing technology. And typically human beings are dealing with issues on parking automobiles in parking slots in a city. In this have a look at we layout a Smart Parking System (SPS) which permits the person to discover the closest parking vicinity and offers availability of parking slots in that respective parking place. And it specially awareness on lowering the time in locating the parking masses and additionally it avoids the needless journeying thru stuffed parking plenty in a parking place. Thus it reduces the gas intake which in flip reduces carbon footprints in an atmosphere.
Intelligent Classification Method for Web page Manipulation Detection
Authors: Roopa V , Santhosh babu K , Prabhakaran B 
Published: May 2021

Abstract
In this paper, we endorse fake detection, a questionable Twitter URL identity system. Our system looks at URL links isolated from certain tweets. Given that aggressors have limited assets and usually reuse them, the URLs of their distracting chains often share similar URLs. We construct techniques to detect and evaluate their doubt by using the frequently exchanged URLs. We acquire numerous public Twitter tweets and build an observable classification model for them. Assessment results indicate that the classifiers identifies questionable URLs correctly and efficiently. Furthermore, we pose fake DETECTION information to circulate within the Twitter as a connected to ongoing mechanism to order dubious URLs.
Real Time Face Mask Identification Using Deep Learning
Authors: Sujaritha M , Kabilan S , Manikandan M , Kisore S Nanda 
Published: May 2021

Abstract
Corona virus disease (COVID-19), one of the most life-threatening diseases which is highly harmful to the people in all over the world. The government of all countries in the world are in the situation to make effective steps to prevent the spread of COVID-19 in the society. If this situation continuous means the extinction of human beings may possible. So, the foremost action towards the virus is to stop spreading from the infected to the non-infected persons. To prevent spreading of the virus, one has to maintain social distancing from other people and personal hygiene. The main mode of transmission of the COVID-19 is by the droplets of saliva and the discharge from the nose. The governments laid a strict rule to wear a mask in the public places to prevent the spreading of the virus. Various technological techniques are well applied in pandemic studies. In this case we applied a deep learning algorithm to determine whether or not an individual is wearing a face mask. This research resulted in identification of face mask in the public places which results in the lowering of spread of the virus. This model is developed using the Convolutional Neural Network (CNN). It extracts the facial landmarks of the person and finds the face mask region. It is trained by datasets and predicts the result.
Privacy Preserving with Surfed Social Media Content
Authors: KaviyaSri B , Parkavi S , ReeyaKumari M , Kiruthika S 
Published: May 2021

Abstract
The world is going to become more digital day by day. A lot of data has been collected from Web Site users on how they think about society and what they believe in. we are targeting generalized part(content based retrieval) in Youtube surf material, in the proposed stage the data collection is more important and making of uncertain data to certain term, like find the sentimental relation between the contents and other equivalent structure of data to be identified, kind of group formulation and removing the out layers of uncertain data with privacy preserving, in such aspect we can able to achieve the correct content over area of people interest which can be used to improvise the surfing contents for any sectors. In order to obtain priority, information must be classified according to the approximate value of three variables. First, the frequency in which a subject appears in the mainstream media over time is a significant factor that can be referred to as the topic’s media focus (MF). Second, the topic’s temporal prominence in social media means that it has a high level of consumer interest (UA). Finally, the social media interaction between users who mention the topic reflect the intensity of the group debating it, and could be considered as user interaction (UI) with the subject. SociRank increases the consistency and variety of topics that are automatically listed, according to our tests.
An Energy Efficient Path Selection Using Swarm Intelligence in IoT SN
Authors: Arulanantham D , Palanisamy C , Pradeepkumar G , Kavitha S 
Published: May 2021

Abstract
IoT is regarded as one of future technology’s most important fields and is receiving significant interest from a wide variety of applications. Nowadays, with the emergence of smart objects, the quantity of things that are associated with the web is expanding dramatically. In IoT, there are many critical problems such as scalability, load balancing, tolerance for faults, energy utilization, etc. A few steering conventions have been created to meet the Internet of Things details for various models, for example, energy utilization, load adjusting, multicasting, network life expectancy, unwavering quality, and versatility. Another directing calculation dependent on the insect settlement calculation is proposed to locate the ideal course of information transmission in the IOT. In Swarm Intelligence, Ant-Colony Optimization algorithm is a procedure used to address computational issues that can be utilized to locate an ideal way and subsequently devour less energy. Results of simulation show that the new ant-colony algorithm can successfully saves node energy and broaden the life of the system.
Predicting Supervised Machine Learning Performances for Sentiment Analysis Using Contextual Based Approaches
Authors: Lakshmi Venkata S , Janan K , Joshua Joseph P S , Sharoz Mohammed 
Published: May 2021

Abstract
The fundamental thought of our methodology is to inspire client inclinations communicated in text-based audits, an issue known as opinion investigation, and guide such inclinations onto some evaluating scales that can be perceived by existing CF calculations. One significant errand in our rating deduction system is the assurance of wistful directions PSWAM and qualities of assessment words. It is because surmising a rating from an audit is chiefly done by separating assessment words in the survey, and afterward accumulating the PSWAM of such words to decide the predominant or normal notion inferred by the client. We played out some primer examination on film audits to research how PSWAM and qualities of assessment words can be resolved and proposed a relative-a recurrence-based technique for performing such assignments. The proposed technique tends to a significant impediment of existing strategies by permitting comparative words to have distinctive PSWAM. We additionally created and assessed a model of the proposed structure. Fundamental outcomes approved the viability of different assignments in the proposed system and suggested that the process doesn’t rely on a large preparation corpus for working. An accelerated algorithm based on the Naïve Bayes approach is used to solve the PSWAM and a parallel algorithm based on FISTA is incorporated to further improve the efficiency. The result is a graph representing opinion target and opinion word candidates before and after extraction further helping users simplify the task of analysis.
Modified Zeta Converter Based On ANFIS Controller Using MPPT PV System
Authors: Jonnalagadda Venkatesh , Sugavanam K R , Mohanasundaram K , Kuppuswamy C L , Bhukya Ramu , Kadali Kalyan Sagar 
Published: May 2021

Abstract
In this paper, the photo volatic (PV) renewable energy is fed to load demand using Modified ZETA converter with ANFIS logic controller. Nowadays, renewables have become a blooming market. These are the main sources of energy that exist in the natural environment. It is a clean alternative to fossil fuels. Therefore the application of power electronics to the wind power generation region has become critical. Converters and inverters are the primary method used to produce such renewable resources, quickly and effectively implemented. The ANFIS controller for the SEPIC converter that interfaced between inverter and source, keeps constant voltage during variable load situation and depicted in a small steady error and a small overflow. The design and analysis of PV based modified ZETA converter is explained and the results are verified using MATLAB/Simulink.
Voice Activated Face Recognition based Smart Support System
Authors: Yuvaraj N , Sanjeev M , Jenifa G , Preethaa K R Sri 
Published: May 2021

Abstract
Voice assistants usually respond to voice commands and provide the user with specific details about his question. Currently, voice assistants can process product orders, answer questions, perform tasks such as playing music, or initiate a quick phone call with a friend. For voice assistants, the long-term goal is to serve as a smart bridge between humans and the immense information and capabilities delivered by the Internet. Taking away the need to use some gadget or screen to communicate in various locations with the internet, technology or other humans. This paper deals with such a voice assistance for schedule maintenance for individual which starts with the face recognition. Once a person enters the classroom / office, their face is captured and the face identification is done. If the identified person is the authorised one, then the system responds with a greeting message. When the person starts to ask about the schedule to the system, it responds with the schedule of the day of that identified person. In case of emergency schedules like meetings, the intimation is sent to the user as a message to alert him/her.
An Effective Dementia Diagnosis System using Machine Learning Techniques
Authors: Priyanka G , Thulasi Priya R , Vasunthra S 
Published: May 2021

Abstract
Dementia being a major cause of creating dependency among aged people also has an inevitable impact on people suffering from it and the families around them. Since the symptoms are gradual and may overlap, diagnosing dementia and identifying its type is risky. The main purpose is to develop a machine learning-based method in diagnosing dementia using the dataset obtained from OASIS. Algorithms such as Support Vector Machine, AdaBoost, K-Nearest Neighbors, Random forest, Linear Discriminant Analysis, XgBoost algorithms are used to find accuracy, recall, and confusion matrix. Implementation of the following algorithms provides accuracy in the range of 83 to 90 percent. SVM provides 87%, KNN gives out accuracy of 84%, LDA gives an accuracy of 83%, Random forest gives an accuracy of 88%, AdaBoost gives 81% and XgBoost gives 90%. XGBoost shows more accuracy than other algorithms.
An Effective Identification of Food Adulteration Using IoT
Authors: Deepiga M , Gopika shree S , Jagadesh V , Jai Kumar R , Venkatesh T 
Published: May 2021

Abstract
To protect fish and seafood from microbial spoilage, Some fishermen and fish vendors immorally use chemicals. An effort was made to detect formalin through our system in wet seafood markets. We get into recent formalin-based studies, then we efficiently develop this project. The emission of gas mostly from fish harmed by formalin can be detected by our device. The values identified by the gas sensors can be changed in the smartphone app using Blynk. The mobile computer is connected to the PC’s hotspot where the arduino IDE has been mounted. Via the arduino IDE, the blynk program gets the values from the sensor and tests with restrictions. We may therefore achieve the optimum solution and be mindful of the fish affected by formalin.
Fuzzy Techniques to Verify A Person’s Identification Using the X-Ray Images
Authors: Ponprakash N , Radhakrishnan K , Manesh M , Suganya T 
Published: May 2021

Abstract
Biometric x-rays are a means of testing the physical properties of an individual to check their identity. There are anatomical attributes, such as eyes or traits of behavior, fingerprints and a special path to authentication. In this article, the use of a deep neural network with fuzzy clustering is proposed as a new approach for forensic radiography based human authentication. A complex, convolutionary neural architecture is used to derive features of hand x-rays and for the detection of fluid clusters. Our experiment demonstrates that hand x-rays provide fingerprint information to identify individuals. That laboratory analysis shows that the proposed solution is much better than previously existing authentication schemes with hand X-rays.
Advanced Hyperchoatic Image Encryption Technic with DNA Sequence
Authors: Karpagam M , Shyam Kumar M , Sowmiya M , Monikavishnuvarthini A 
Published: May 2021

Abstract
As the development of computer usage increases the need for security also increases as it had changed from entertainment based to high confidential files maintaining medium. So, there was a great improvement in the field of cryptography for data transfer and these are basically text and so they are more simple to process when compared to higher level of files like image and vide. This lead to the need for encryption methods for fast and secure techniques to avoid quality and quantity of high speed encrypted data files.. There are lots of highly secure cryptographic methods but they need to be compatible with image for processing. So the project goes along with a technic called hyper chaotic encryption technique with DNA sequence for hybrid image cryptography.
Analysis and Prediction of Epilepsy Using Heart Rate by Application of Ensemble Learning and Linear Regression
Authors: Ganesh Kumar D. , Raaghavi S. , Pavithra S. , Gokul S. , Anto Pravin C. 
Published: May 2021

Abstract
In the existing methods the epilepsy was analysed using the PPG, EEG, ECG. The output obtained from the two different signals were analysed and found that the signal obtained while using ECG wearable sensor was more accurate and reliable when compared to the result obtained while using PPG. The main drawback while using EEG signal is the placement of wet scalp electrodes. Hence heart rate is used to analyse epilepsy in the proposed methodin order to obtain accurate and reliable result. In this method analysing the heart rate signal of the patient and epilepsy death rate prediction are two main goals of the project. The biomedical sensors and wearable sensors currently available in the market was studied. Sensors like EEG, ECG, PPG characteristics, and their working were studied completely. In this paper, we have not used any biomedical sensors or wearable sensors. For the heart rate signal, the dataset from the “fitbase” website was collected. Heart rate data collection per minute average heartbeat of the person. heart rate signal data length was 12,000 But only 90 samples were used for ensemble averaging. Not only the heart rate of the signal the epilepsy was also focussed. The epilepsy death rate data from the Statista. Epilepsy data contains year and death for future prediction; a linear regression algorithm was used.
An Efficient Real Time Face Expression Identification System Using SVM
Authors: Balamurugan A , Suganya B 
Published: May 2021

Abstract
Real-time emotion detection and recognition system (REDRS) has been rapidly grown in the current era, especially in human-computer interaction and artificial intelligence field. The mutual interaction of human-computer like service-based sectors including online classrooms, e-business, banking services, robotic automation, and others to utilize the growing technology for the base analysis i.e. tracing the human face recognition for the state of the receiver to adapt the strategy that useful for growing sectors. However, the robust emotional identification of the facial emotions from the images and videos residues a big challenging task because of the accuracy of the emotional features. These features can sometimes represented in several forms, like point-based geometric, static, dynamic, or region-based appearance. Changes of facial features like feature position and shape movements are usually affected by the changes of facial elements and muscles during the expression of emotion. Trying to predict an individual’s spirit during a spoken form, usually requires decoding his/her face. Repeatedly, body language and particularly facial expressions, speak quiet words about the state of mind of a person. The proposed paper focuses on experimenting with a person’s countenance and classifying the mood of the person.