bluuebunny/crossref_metadata_2025_split huggingface dataset:
Twitter Sentiment Analysis of Juvenile Behaviour Deviations using LSA (Latent Semantic Analysis)
Authors: Muthasima Rochma , Sumpeno Surya , Suprapto Yoyon Kusnendar 
Published: June 2019

Abstract
Sentiment analysis is an effort to mine data or can be called text mining. One of the uses from sentiment analysis is to analyze public opinion on a particular topic or product. Research related to sentiment analysis has been carried out with various methods and different accuracy results. The approach method in analyzing sentiment is divided into 2, namely with statistical approach and semantic approach. Research with a statistical approach has been developed previously. While the semantic approach is still quite rare. Based on previous study, The LSA (Latent Semantic Analysis) approach seems effective in analyzing data and further more for semantic understanding of sentiment orientation. In this study, we build two model, the first is statistic approach using TF-IDF for feature extraction then classified into SVM (Support Vector Machine) algorithm. Then compare it with semantic approach using TF-IDF and SVD (Singular Value Decomposition) for feature extraction then put into LSA (Latent Semantic Analysis) model. The LSA (Latent Semantic Analysis) model will convert the original feature space to a new lower-dimension space by calculate cosine similarity between sentence vectors with vector topics to produce a score for each sentence by measuring how closely the semantic meaning between the sentence and all the words on the topic. Then divided into three collection of words that sentiment oriented including its vector similarity value. The results of the study prove that the research with statistical approach using SVM (Support Vector Machine) algorithm gives results of precission average about 0.56 and recall about 0.54. While with semantic approach using LSA (Latent Semantic Analysis) gives a slight increase in result of precission about 0.57 and recall about 0.56.
Recommendation System for Complementary Breastfeeding using Ontology Modelling and Naïve Bayes
Authors: Sihwi S W , Fadhilah A N , Puspasari M P , Winarno  
Published: June 2019

Abstract
Complementary breastfeeding is an additional food given to the baby started from six to 24 months. The giving of complementary foods is given gradually according to the age of the children and adapted to the condition of the children, such as allergies suffered or malnutrition suffered. This research aims to develop an ontology based decision support system that will help mothers in giving breastfeeding to their babies with keep regarding to their food preferences. This research successfully develops a content-based recommendation system by performing the Naïve Bayes Method and modified the existing ontology modelling and also develop a mobile application with the Android platform so that it can be more accessible to many people. Thirty-five users evaluated the system, and the result of the usability testing shows that the user satisfaction level using SUS (System Scale Usability) method is 79.57, which is in Grade A-. This grade indicates that the system can be well understood by the user and can help mothers in choosing breastfeeding recipes or menus.
Flipping the Message Bits to Increase Imperceptibility in the Least Significant Bit Image Steganography
Authors: Astuti Erna Zuni , Moses Setiadi De Rosal Ignatius , Rachmawanto Eko Hari , Sari Christy Atika , Sarker Md Kamruzzaman 
Published: June 2019

Abstract
Imperceptibility is one of the issues that continue to be studied in the science of steganography in images. Imperceptibility means that the message embedded in the cover image cannot be perceived by the human senses, especially the sense of sight. Flipping the message bits is the method proposed in this study, which is inspired by the inverted LSB method, but this method is simpler because it only flips the message bit when the change in the cover image pixel exceeds 50%. Although the proposed method is simpler than the inverted LSB method, based on the test it is found that the imperceptibility quality can increase significantly when compared to the traditional LSB method. This is evidenced by the PSNR value increasing around 9dB, from 51dB to 60dB and the MSE value dropping from around 0.44 to 0.05.
Mobile Recommendation System for Culinary Tourism Destination using KNN (K-nearest neighbor)
Authors: Riswanto E , Robi’in B , Suparyanto  
Published: June 2019

Abstract
Currently culinary tourism is becoming very popular among the Indonesian people. People make visits to interesting culinary places. They want to visit a culinary place that is strategic, comfortable, and cheap as their culinary destination. The culinary destination in Yogyakarta that continues to grow every year makes the tourists who will visit be confused to determine the location of culinary tourism that they will choose. Travelers need references and recommendations to help them determine culinary destinations that meet their expectations. Mobile devices and application guides are able to gather information about an environment and suggest certain places such as tourism locations, based on context factors such as location, weather conditions, and the time needed to get there. The purpose of this study is to develop a recommendation system for culinary tourism destinations in Yogyakarta based on mobile using the KNN (K-nearest neighbor) algorithm. The method used in this study consists of five main steps: literature study, identification, data collection, implementation, and evaluation. The recommendation system is based on previous user ratings of food taste, environmental atmosphere, price, service and distance between users and location. The weight of each criterion is tailored to the needs of the user. The results showed that the mobile application recommendation system was able to provide recommendations to users to determine culinary tourism destinations in Yogyakarta in accordance with the parameters of user desires.
Early Detection of Hand, Foot, and Mouth Disease based on Palmprint using Certainty Factor as Expert System Method based on Android
Authors: Novitasari Dwi , Irawan Budhi , Prasasti Anggunmeka Luhur 
Published: June 2019

Abstract
Hand, foot, and mouth disease is an infectious disease caused by coxsackievirus. This virus lives in the digestive tract and spreads from person to person. Patients will get a rash on their hands, feet and mouth. An application that can do early detection for HFMD is needed, so patients can get the right references. The application is made for android based phone with expert system implementation. Method used for expert system is certainty factor using data that has been validated and is fully supported by doctors as experts. The output of this application is information on whether or not a patient is suffering from HFMD as well as recommendations to visit medical personnel for further examination. This paper is a result of testing the expert system to detect the HFMD. The accuracy, precision, and recall from this method is as high as 100%.
Literation Hearing Impairment (I-Chat Bot): Natural Language Processing (NLP) and Naïve Bayes Method
Authors: Anggraeni Merry , Syafrullah Mohammad , Damanik Hillman Akhyar 
Published: June 2019

Abstract
A part from advances in Artificial Intelligence and Natural Language Processing, designed CHATBOT will try to understand user requests accurately, so that they do not give the wrong answer or no response. The difficulty of getting specific information about hearing impairments that can be asked properly is asking someone who understands that it still cannot be found on search engines on the internet which is a favorite tool for most people in obtaining information. By using Machine Learning and with the help of Artificial Intelligence, a question and answer (conversation) interaction mechanism is created to gain literacy knowledge that supports the educational process. Therefore, the CHATBOT application was developed in this study as a media for retrieving information about hearing loss. Using the NLP method and the Naïve Bayes Algorithm for classifications used to get input classes to I-Chat Bot, as well as to test hypotheses using the Technology Acceptance Model developed (extended). The result is an I-Chat Bot with artificial intelligence that understands user input and provides an appropriate response and produces a preferred and easy system model to be used in the search for information about required hearing impairments. This result paper also gets the value of the test accuracy with Precision 98.6%, Recall 88.75% and Accuracy 88.75%.
Ontology Modelling on Legal Document: Case Study : Legal Document of Indonesian Republic
Authors: Maftuhah T S , Purwarianti A , Asnar Y D W 
Published: June 2019

Abstract
Regulation representation and reasoning process of the regulation representation is an important thing for finding inconsistency in Indonesian Republic Regulation Document. Indonesia is a legal nation. The mean of Legal Nation is that nation using law to regulate the life of society, state, and the government. Indonesia has been using law to maintain it’s stability since it’s founded. An inconsistence in law has been found in Indonesian regulation document. In this paper,we proposed a method to represent a Indonesian Republic regulation in an ontology form for reasoning process. Ontology language that used in this paper is OWL. The purpose of this representation is to be able to do a reasoning process to find inconsistent regulations and do the process of finding concepts in regulations. After transforming Indonesian Republic regulation document into OWL ontology form and reasoning process of Indonesian Regulation ontology inconsistency has been found.
Efficient time reducing and energy saving routing algorithm for wireless sensor network
Authors: Venkataramana S , Sekhar B V D S , Deshai N , Chakravarthy V V S S S , Krishna Rao S 
Published: June 2019

Abstract
The research article presents a novel energy saving routing algorithm for wireless sensor networks (WSNs) to meet the demand of present research trend. In contrast to conventional methods, a new concept of dominant node has been introduced in this paper. Unlike the conventional algorithms, the proposed method can be applied to both homogeneous and heterogeneous networks. The proposed method focuses on the proper energy distribution among the sensor nodes so that it can offer an energy efficient solution while maintaining improved network lifetime and throughput. The proposed idea has been validated through simulation results. Moreover, the supremacy of the proposed method has been established over existing ones.
Computational prediction and validation studies on a diverse dataset of cox-2 inhibitors
Authors: Yesu Babu Adimulam , Nedunuri Deepak , Madhava Rao Ch 
Published: June 2019

Abstract
In linear regression analysis, when data was derived from various reference sources, the experimental quality of such data has to be assessed. Significant variables based on the statistical data of analysis were chosen. Based on the parameters like correlation coefficient (r), F-value, cross-validation r2 etc quality of the generated equation was judged. An additional condition for high predictive ability of regression model is based on external set cross-validation r2, (R2
cv,ext) and the regression of observed activities against predicted activities and vice versa for validation set. Multivariate regression analysis using python program resulted in few influential parameters displayed significant positive and negative contribution towards biological activity of COX-2 inhibitors. A new regression model was attempted by dividing the complete set (n=64) as a 58 molecule training set and a 6 molecule validation set based on selection criteria after rejecting outliers from the data set.
Performance study of cloud computing for scientific applications
Authors: Pranav V , Satish Kumar P , Krishna M 
Published: June 2019

Abstract
I propose the primary visualization of the cost estimate to evaluate the costs of the cloud database in simple and codified cases from the point of view of an occupant in a intermediate term period. Consider the variability of cloud costs and the chances that the database workload will vary in the evaluation time frame. The proposed model is instantiated in relation to some offers of cloud service providers and related authentic costs. Obviously, adaptable encryption affects the costs recognized with the capacity size and system usage of a database advantage.
A frame work for mining huge data by non-expert users with the assistance of knowledge base
Authors: Vijayalakshmi K N V S K , Malathi J , Krishnaveni G 
Published: June 2019

Abstract
Data mining is the examination of (regularly huge) observational enlightening files to find unsuspected associations and to plot the data in novel ways that are both legitimate and valuable to the information proprietor. At the end of the day information mining is a procedure of finding beforehand obscure, beneficial and utilizes designs covered up in information, with no earlier theory. Robotized Data Mining and demonstrating programming gives supervisors a device to perform examinations that generally would should be dealt with by an exceptionally prepared specialist. Robotized information mining systems isn’t to give more exact outcomes yet endeavors to enable non expert clients to accomplish sensible outcomes with least exertion. Data mining is a troublesome and arduous action that requires a lot of mastery for acquiring quality outcomes. We require new techniques for wise information investigation to separate important data with less exertion. For that in this paper we propose an edge work with the assistance of learning base. That will be tried on e-realizing which creates better outcomes.
Data productive collaborative filtering using deep learning based recommender model
Authors: Purna Prakash K , Krishna M , Satya Vijaya M 
Published: June 2019

Abstract
The term Synergistic Filtering is utilized as a spine in all Commercial Recommendation Systems today. Conventional synergistic separating (CF) strategy does not take in thought successions of client’s appraising, which reflects changes of client’s inclination over some stretch of time. The suggestion undertaking is affected by the profound learning pattern which demonstrates its critical effectiveness. The profound learning based recommender models give a superior detainment of client inclinations, thing highlights and clients things connections history. The proposed structure incorporates three segments: a network factorization demonstrates for the watched rating remaking, a bi-grouping model for the client thing subgroup examination. We recognize uninteresting things that have not been assessed yet rather are presumably going to get low evaluations from customers, and particularly attribute them as low regards. One imperative undertaking in our rating induction structure is the assurance of nostalgic introductions (SO) and qualities of sentiment words. It is on the grounds that deducing a rating from a survey is fundamentally done by removing conclusion words in the audit, and afterward amassing the SO of such words to decide the predominant or normal assumption suggested by the client. The proposed structure and recommend that the system does not depend on a substantial preparing corpus to work. Advance improvement of our rating derivation structure is progressing. Trial results demonstrate that the proposed system indicate changes over the conventional community oriented sifting strategy.
Ensemble path finding in wireless sensor networks
Authors: Prasad N , Kanna babu A M K , Krishna rao S 
Published: June 2019

Abstract
A wireless sensor network (WSN) plays the major role in providing the network for the various scenarios. It consists of various nodes and base stations that are used to communicate the nodes and transfer the data from source to destination and in between the nodes. The important parameters of the WSN are accuracy, computation time and shortest path between the nodes. Various existing approaches are available to get the results. But the existing approaches are not performed well. In this paper, the ensemble path finding is implemented to solve issues identified in the existing system. The proposed system shows the performance based on accuracy, computation time and shortest dynamic path.
Implementation of data mining techniques in web of things
Authors: Vihari G. , Prasad N. , Satyanarayana K. 
Published: June 2019

Abstract
Web of Things has been becoming quickly because of late progressions in interchanges and sensor advances. Interfacing an each question together through web looks extremely trouble some, however inside a casing of time Internet of Things will definitely change our life. The tremendous information caught by the web of Things are considered of high business and also social qualities and removing concealed data from crude information, different information mining calculation can be connected to web of things information. In this paper, We study efficient audit of different information mining models and also its application in web of Thing field alongside its benefits and negative marks.
An ensemble integrated mailing system for detecting spam mails
Authors: Pavan G , Lakshmaji K , Krishna Rao S 
Published: June 2019

Abstract
In Internet, today most widely issues occur with security. Security plays the vital role in Internet. Many software and hardware systems are affecting with the security issues. This may cause major problem for the companies to maintain the network securely. Attackers mainly concentrate on attacking the servers and systems with any type of malware and virus to hack the system or mail account. Today the most popular attacking by the attackers are spam mails. This brings lot of security issues if it is implemented in gmail, yahoo and other mail service providers. Spam zombies are the spam activities done by the spam developers to attack the system. In this paper, an ensemble spam mail detection system named ESMDS by observing outgoing and incoming messages of a network. ESMDS is developed based on a powerful analysis tool called Sequential Probability Ratio Test (SPRT). Results show the performance of the ESMDS.
Identification of MITM Attack by Utilizing Artificial Intelligence Mechanism in Cloud Environments
Authors: Ramiah Chowdary P , Challa Yallamanda , Jitendra M S N V 
Published: June 2019

Abstract
Cloud dealing with has changed into an essential bit of the reliably life. It basically made everyone life less troublesome with astounding highlights. To help broadening many of clients and more over keen gadgets, it is depended upon to keep the cloud condition continuously secure and time tested. Scattered handling security has changed into a fundamental testing field at now a day. Here we introduced Man-made consciousness based structure that might be useful for an exposure of man-in-the-middle assault (MITM) in passed on figuring condition. As MITM strike winds up being eminent with the development of time, on the off chance that it is recognized at first, by then the assault might be limited. So we concentrated on assault region fragment to remain the cloud condition utilizing Man-made consciousness technique for thinking. Watchwords—Cloud enrolling condition, Computerized reasoning method of reasoning, MITM strike.
Privacy preserving for global data using ensemble approach
Authors: Mohanrao M , Karthik S 
Published: June 2019

Abstract
Security becomes the most important aspect in current world and also in many applications. In data mining, privacy preserving is one of the global techniques to secure data from various attacks and to providing the data integrity for the data. It is very important that for every data there is need of data sensitivity is required to secure the information and to avoid the data spillage and also the information duplication. In this paper, the Ensemble approach which is integrated with an ensemble privacy preserving for data, maintaining the data sensitivity, providing the security for the data by using encryption technique and preventing the data de-duplication. Results show the performance of the proposed system.
RETRACTED: Big Data Analytics in E-Commerce User Interest Patterns
Authors: Amarnadh V. , Akhila M. 
Published: June 2019

Abstract
The assortment of online buys is always expanding. Offices have recognized the open doors this gives and are progressively more the use of on line channels. With the goal that it will profit capacity clients, organizations frequently endeavor to pick up a superior comprehension by means of web examination. A standout amongst the most useful sources is web log records. Basically, these give an abundance of basic insights around buyer direct on a web webpage, comprehensive of the course or motivate passage to time. Mining these so-known as snap flow measurements inside the greatest finish way has end up being a fundamental assignment in anticipating on line client lead, streamlining sites, and giving customized pointers. Since the scope of customers keeps on expanding, so does the degree of insights log documents created, each as far as size and amount. For beyond any doubt gatherings, the by and by utilized innovation are presently insufficient. This work proposes a total work process that utilizes a grouping calculation in ahadoop surroundings to investigate examples of individual leisure activities. The total work process is demonstrated in an utility circumstance of one in everything about greatest B2B e-exchange (B2B) sites for big business to-business endeavor (B2B). Moreover, a trial appraisal technique is utilized to check the pertinence and proficiency of the arrangement of guidelines utilized alongside the related structure.
A Mobile Ad Hoc Grid is Simulated Using Three Basic Conquering Protocols
Authors: Pandey Nalini , Rajeshwari P. 
Published: June 2019

Abstract
An ad hoc grid mightsubsistamixture containing cellular movable nodes dynamically forming a brief grid while not affecting employment containing any occur grid infrastructure or centralized administration. Variety was containing conquering protocols like aggressive supply conquering (DSR), Adhoc On-Demand Distance Vector conquering (AODV) including Destination-Sequenced Distance- Vector (DSDV) grip enforced.During aforementioned paper, a trial acquire persist created en route being check affecting act containing 2 outstanding on-demand hastydirection-finding obligation being fixed spontaneous networks: DSR including AODV, at affecting side containing affecting standard proactive DSDV protocol.Affectingreproductions were administered through NS2 machine.Affectingperformances containing affecting steering obligation were evaluated via matrices conquering overhead, suspension plus container transmission magnitude relation being various traffic densities including knob mobility’s. Once counterfeit including elaborated search containing affecting consequences, it absolutely found that DSR outperforms higher than unusualdirection-findingset containing rules.
Facial expression classification using Cross Diagonal Neighborhood Pattern
Authors: Obulesu A. , Keerthi R. 
Published: June 2019

Abstract
Facial Expression Recognition has significant applications in the field of Affective Computing. FER has its significant contribution in the fields like human computer interaction, neurology, psychiatry, image processing, computer vision, affective computing, and information security. This work gives unique and robust FER System by extracting unique and robust face features. This work proposes Cross Diagonal Neighborhood Patterns (CDNP) for unique feature extraction. The CDNP features are further processed by Gray Level Co-occurrence Matrix (GLCM). The derived CDNP-GLCM features are sent to Convolutional Neural Network (CNN) to train various expressions.