1,720,978 research outputs found
IoT Recommender System: A Recommender System based on Sensors from the Internet of Things for points of interest
Full text linkThe Internet of things is a great source of knowledge. Its strength is based on the use of sensors that can provide information in real time on multitude of variables in any part of the world connected to an internet connection. At the same time, social networks increasingly encourage users to create comments on points of interest they visit or could visit, giving valuable feedback for other users or even for recommender systems that could use that information to anticipate user tastes in the future. In this work, we present an overview of a novel recommendation system to generate recommendations for users based on both the information gathered from sensors and the opinions explicitly indicated by users about places or recommendations previously made by the system
Automated human cortical bone Haversian canal histomorphometric comparison system
No full textThis research presents comparison of histological parameters of cortical bones for Malaysian population. For this research 44 samples were collected from Department of Pathology, Faculty of Medicine Universiti Kebangsaan Malaysia. Sample preparation being one of the important aspects of this research, is described in detail. Six Haversian canal-based parameters were selected for the comparison. Sex and race comparisons were performed on the collected Malaysian samples. Race comparison was performed between the two Malaysian races (Chinese-Indian). The results obtained from sex comparison demonstrates difference in mean Haversian canal area (hcm). The hcm parameter in female samples were found to be significantly larger than male samples (P < .05). This research also presents an automated system which can be used as a platform to perform Haversian canal parameter comparison. The system is designed to automatically check normality of the parameters measurement data and select the relevant comparison test. It is divided into two main section. The first section provides race comparison platform while the second section is dedicated for sex comparison
IJMLNCE Editorial Note Volume No 04, Issue No 02
No full textIt gives me pleasure to present the editorial preface of the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 02 (2020). This issue comprises five manuscripts contributed by authors.
The first paper of this issue titles, "Edge detection of Friction Stir Welded Joints by using Fourier Transformation," is contributed by Akshansh Mishra et. Al. In the manuscript, the authors have implemented two machine learning-based image processing techniques. They stated that visual inspection had played a vital role in the beginning era of science. Nowadays, image processing is finding application for defects analysis of the manufactured parts in many industrial processes. We have implemented two machine learning-based image processing techniques in recent work, i.e., Fourier Transformation operator and Laplacian operator for the surface defects detection in Friction Stir Welded joints. In conclusion, The quality of the weld surface in the Friction Stir Welding process depends on the input parameters such as Tool Rotational Speed (rpm), Tool Traverse Speed (mm/min), and an Axial Force (kN).
The second paper, titled "Utilize Machine Learning Methods to Detect Plaintext Passwords," is authored by Nada Alnoaimi et. Al. In this article, information security explores, where the author states that every company is a target today, no matter its type. Hackers and cybercriminals are after data which they can monetize in many ways. Being proactive and have a defensive and protective plan in place, such as evaluating and assessing IT security, is an excellent recipe for avoiding data breaches and, consequently, business disasters. Why not utilizing a machine learning platform could be trained to search text in a computer resource, detect a string of plaintext characters, and analyze the string of characters to predict or detect a plaintext password on a computer resource asset. The machine will be able to catch a plaintext password in a character string by analyzing plaintext character strings for typical password complexity, such as, for example, including at least one uppercase letter, lowercase letter, number, unique character, and text length (for example, minimum of eight characters). It will also predict a level of certainty that a character string includes a password and output a confidence score based on the expected level of certainty. Finally, it will categorize the confidence score in any number of prediction certainty levels, including, for example, three groups – high, medium, or low.
S Nagaprasad et al. contributed the third article for this issue titled "Heart Disease Prediction Propagation approach." Data mining methods are used to test complex data, and regression processing based on input data sets is used to estimate results. A variety of prediction analysis methods have been implemented in recent years. The clustering method k-means and SVM ( support vector machine) are a statistical, computational technique for clustering and defining primary data to detect cardiac disorders. In this study, the Back Propagation Method is used in tandem with the k-means clustering algorithm to cluster knowledge for improved prediction research performance. The implemented algorithm\u27s output is found in the cardiac disorder data sample collected from the UCI depositor. Within this sample, there are 66 attributes.Nonetheless, a subgroup of 14 qualities is needed for every study. The Cleveland platform is utilized in particular for machine-learning investigators. The research designed correlates with the current techniques, precision, error identification, and deployment time (using the numerical mean).
The Fourth article, title "Discovering Trending Topics from the Tweets By Odia News Media During Covid-19," was contributed by Swarupananda Bissoyi et. Al. This paper explores the Covid-19 pandemic\u27s onset, and the lockdown imposed because it has significantly fueled news consumption. News portals, including the ones in Odia language, are actively feeding news related to Covid-19 to their consumers via their websites and Twitter handles. The news items didn\u27t restrict to Covid-19 alone; they also touched various domains of life like education, healthcare, administration, politics, movies, etc. Discovery of the news trends provides a bird\u27s eye view of the issues and topics popular in the online community. This could be of interest to advertisers, marketers, researchers, sociologists, and policymakers. This paper applies Topic Modeling to discover the trends from the tweets made by the Odia news media from 20th March 2020 to 31st August 2020, the period which saw the emergence of both lockdowns and unlocks in India. We found that during this period, the Odia news media didn\u27t restrict themselves to report news surrounding Covid-19; rather they reported other happenings as well.
In the fifth and last article of this issue, titled "Designing Hand-Held Vibration Measuring Device for Industrial Machines," is contributed by Thi Dieu Linh Nguyen et al., In this manuscript, the authors discuss that Evaluating the quality of industrial machines, the vibration meter is used to measure the actual vibration of the machine. The two most important parameters describing machine vibration, amplitude, and frequency, are the basis for determining the cause of vibrations. Spectral analysis of the vibration signal will give information about the vibration level and choose which part of the machine the vibration signal is caused. This paper presents the manufacturing of vibration measuring devices with simple structure, compact size, and high accuracy at a reasonable price. The spectral analysis method of vibrating signals and real-time spectrum display of the measured vibration signals
I am sure that these five papers included in the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 02 (2020) will be useful to the research community. At this end, I am thankful to the Editorial board member for their timely support in the review. I am looking forward to receiving your unpublished research work for Volume 05 No 01 (2021)
IJMLNCE Editorial Note Volume No 04, Issue No 01
No full textIt gives me pleasure to present the editorial preface of the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 01 (2020). This issue comprises five manuscripts contributed by authors.
The first paper contributed by Ramachandran A et al. titles, "Efficient Distributed Web Crawler Using Hefty and Enhanced Bandwidth Algorithms for Drug Website Search," In this paper, a novel Hefty Algorithm and enhanced bandwidth algorithm are combined for a better-distributed crawling system. The hefty algorithm was implemented to provide efficient and robust surfing results while applying on the drug web search. Refabricate a proficient search structure is very important due to the current scale of the web. Search engines mine information from the web, and a web crawler program that surfs the web in an efficient manner. A distributed crawler belongs to a variant of a web crawler, uses a dispersed computation method. In this paper, we design and implement the concept of Efficient Distributed Web Crawler using enhanced bandwidth and hefty algorithms. Mostly Web Crawler does not have any distributed cluster performance system and any implemented algorithm. We also implemented an Enhanced Bandwidth algorithm to improve the efficiency of the proposed crawler.
The second paper, titled "Revealing Brain Tumor Using Cross-Validated NGBoost Classifier," is authored by Shawni Dutta and Samir Bandyopadhyay. In this article, the author writes that the brain is the most complicated and delicate anatomical human body structure. Statistics prove that, among various brain ailments, brain tumors are most fatal, and in many cases, they become carcinogenic. Brain tumors are characterized by abnormal and uncontrolled growth of brain cells. It takes up space within the cranial cavity and varies in shape, size, position, and characteristics viz. It can be benign or malignant, which makes the detection of brain tumors very critical and challenging. A neurologist or neurosurgeons vital information needs to have is the precise size and location of cancer in the brain and whether it is causing any swelling or compression of the brain that may need urgent attention. This paper exploits ensemble strategy-based Machine Learning (ML) algorithms for revealing brain tumors. NGBoost algorithm and 5-fold stratified cross-validation scheme are proposed as classifier models that automatically detect patients with brain tumors. The proposed method is implemented with necessary fine-tuning parameters compared against ensemble-based baseline classifiers such as AdaBoost, Gradient Boost, Random Forest, and Extra Trees Classifier. An experimental study implies that the proposed method outperforms baseline models with significantly improved efficiency. The interfering features that impact brain tumor classification are ranked, and this ranking is retrieved from the best classifier model.
Abdulmohsen Alotaibi contributed the third article for this issue titled "Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images". In this interesting work, he is discussing the COVID-19 pandemic, which is a global health crisis that has already infected more than 3.5 million people and caused more than 250 thousand deaths around the globe. He focused on developing a more efficient way to detect and treat this illness. This paper utilizes transfer learning techniques to detect normal, COVID-19, and viral pneumonia cases from Chest X-Ray images. Four pre-trained models on ImageNet were chosen as the base model, which are ResNet50, VGG19, DenseNet121, and InceptionV3. The performance metrics of each fine-tuned model are overall similar. With an average recall, precision, f1-score, and accuracy of 97.42%, 97.42%, 97.23%, 98.3% respectively.
The Fourth article, title " The Economic Impact of Social Media Fraud and it\u27s Remedies," was contributed by shakik mahmud et al. This paper presents the economic impact of social media fraud in Bangladesh and its IT-based prevention model. Online privacy and security problems become a big concern online day by day—many types of issues growing up here, for example, phishing, hacking, sabotage, etc. Social media is a popular and powerful tool to express personal life and also business purposes in Bangladesh. Social communicating websites such as Facebook, Twitter, WhatsApp, and LinkedIn are popular social sites. Facebook is the most popular one. Through these media, people communicate with their friends and family and share thoughts, photos, videos, and lots of data. Many types of business and commerce have developed on social media. Presently, people depend on it, so it\u27s marketing value increases day by day well. Some Tech fraud groups have been formed and wake up to hack money in some tricky way in this big virtual society. At present, social media is one of the critical areas for fraudsters. This paper will show based on our study how much money is being spent through it and others by the IT-based prevention model of this problem.
The fifth article of this issue, titled " The Performance Enhancement Systems of Human Iris Pattern and Recognition Method through Digital Authentication Application," is contributed by Krishnaveni N et al. Authors discusses that human iris and recognition patterns have been recognized as the best biometric marking ever found. The iris and the textured iris patterns\u27 uniqueness tend to remain natural, unchangeable, and recognizable through existence. Mathematical analyses of the unique stable patterns formed within the iris include Iris detection methods, and a comparative analysis is carried out utilizing an established database. In this document, a clean electoral system is created to build a fraud-free ID list of electors. To find the Iris and Eyes, the algorithm of canny edge detection is used, Dougman\u27s normalization procedure is used, object filters are added, and the corresponding process is finally conducted for the Euclidian set. Biometric authentication confirms our identification by being a simple and increasingly secure method. They implemented a weighted, majority voting process for all biometric authentication systems utilizing a bitwise contrast between inscription and biometric models to resolve this problem and enable Iris identification in less than ideal images. We also observed that the approach outdoes the current majority and efficient bit sorting strategies through a set of tests with the database CASIA iris. Our process is an easy and efficient way to boost the accuracy of established iris detection systems.
I am sure that these five papers included in the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 01 (2020) will be useful to the readers and researchers. At this end, I am thankful to the Editorial board member for their timely support in the review. I am looking forward to receiving your unpublished research work for Volume 04 No 04 (2020)
Volume No 04 Issue No 03 (2020)
No full textFace Recognition Approach using Stereo Matching Algorithm
Enhancing the Accuracy of Indoor Positioning Using System Delay Time Compensation
E-Recruitment In HR Consultants via E-Technology
A Model on Fuzzy Logic Implementation in the Development of Traffic Management in Smart Cities: Artificial Intelligence Approach
IoT and AI-based plant monitoring syste
Volume No 04 Issue No 04 (2020)
No full text[1] Hung, Bui Thanh (2020). Assessment of Recruitment Records using Machine Learning Approach. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 143-151.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.001
[2] Bui,Thien Xuan, Bui,Chuyen Van, Nguyen,Lao, Nguyen,Pha Xuan, Huy, Ha Nguyen Cuong (2020). The Ripening of Pineapple Fruits. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 152-161.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.002
[3] Trong,Nguyen Thanh, Kien,Luong Gia, Tran,Thi T. T., Duong,Hieu N., Hoa,Tran Van, Nam,Thoai (2020). Improving the Performance of One-shot Face Recognition by using Data Augmentation. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 162-170.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.003
[4] Hung,Bui Thanh (2020). Vietnamese Voice Classification based on Deep Learning Approach. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 171-180.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.004
[5] Thai, Dang Nguyen Ha, Quang, Dat Nguyen (2020). Compare model multi-input RNN, LSTM and GRU for prediction of irrigation canal\u27s water level in Red river delta, North Vietnam. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 181-188.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.005  
IJMLNCE Editorial Note Volume No 03, Issue No 03
No full textThe International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242 is now indexed in popular databases such as BASE (Bielefeld Academic Search Engine), CNKI Scholar, CrossRef, CiteFactor, Dimensions, DRJI, Google Scholar, Index Copernicus, JournalTOCs, J-Gate, Microsoft Academic, PKP-Index, Portico, ROAD, Scilit, Semantic Scholar, Socolar or WorldCat-OCLC. We are now proud to present the ninth volume of the journal, Volume No-03 Issue No-03, with some high-quality papers written by international authors and covering different aspects related to machine learning and collaborative engineering.
Phan Trong-Thanh and Doan Van Thang published a work entitles “Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization”. In this paper, they have presented the constrained NMF approach for problem the facial expression recognition. The proposed MNMF_SGR performs well in facial expression recognition task and its effectiveness has been proven in their model. To summarize, with many constraints allows them to build models effectively and specifically on high dimensional, sparse and noisy datasets. For their future work, more sophisticated and efficient way to tune kernel functions will be explored. They also plan to apply the proposed method to problems in other fields, such as bioinformatics and computer vision. Studying the convergence rate for MNMF_SGR and increasing the efficiency, they should be all in consideration.
Praneet Amul Akash Cherukuri published his article “Recommender System for Educational & Corporate Sector In Prediction of Domain Recommendations & Analysis using Machine Learning”. In this manuscript he suggest that his model has a huge impact on the educational institutions and the corporate sector of today\u27s highly competitive world. The model proposes a simple and cross-sector solution to both the corporate and educational sectors that could result in the huge increase of employability solving the problem of wrong decision making of job aspirants as well as mistakes made by the organization whereby suffering losses from those decisions. Hence it could benefit every academician in evaluating his/her students as well as their academic performance in a more sophisticated and a single independent platform that has analysis related to current world trends and scenarios. The model has a vast scope of improvement as well as can provide great accuracy with positive results in the future.
Akshansh Mishra published his article “Understanding Machine Learning for Friction Stir Welding Technology”. In this manuscript, he suggest that there is a loss of time and materials if the optimization of the Friction Stir Welding parameters is done through experimental studies which further leads to increase in the cost of the experiment. Machine Learning approach like Artificial Neural Network and image processing overcome these issues. So, it can be concluded that the mechanical and microstructure properties can be predicted and also the defects formation can also be observed by the implementation of various Machine Learning tools in the Friction Stir Welding process.
Anoop et al. published a work entitled “Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques”. This survey outlined some of the very recent approaches in knowledge graph-based recommendation systems. As knowledge graph is one of the effective representation mechanisms for knowledge that has been unearthed from unstructured text, it got wider acceptance among research communities. A knowledge graph represents entities and relationships as nodes and edges respectively and a large number of meaning-aware applications and algorithms can operate on this graph. One such application is recommendation systems that suggest a user with items based on their previous interactions with the system. Knowledge graph based recommendation systems became very popular recently primarily due to its ability to supply side information for augmenting data and thus enhancing the quality of recommendations. This paper discusses some of the very prominent approaches reported very recently in the recommendation literature. Some interesting research dimensions are also discussed towards the end of this paper. This survey will be useful for the researchers and practitioners who wish to work on entity knowledge graphs based recommendation systems.
Finally Amrit Kaur Saggu and Shivani Agarwal published a work entitled “Performance Evaluation of LAR protocol using real dataset on Highway and City Scenario” In this work they have evaluated the performance of Location Aided Routing protocol (LAR) for Vehicular Ad-hoc Networks (VANETs) in terms of throughput, packet delivery ratio and routing overhead. They have considered two scenarios namely highway and city scenario. For highway they have taken Delhi highway data from OSM map and for city scenario taken real traces of Bologna Ringway dataset. For each of these scenarios the performance is evaluated by considering variation in terms of number of vehicles and simulation time. They observe that with the increase in simulation time the throughput increases for both highway and city scenario. The packet delivery ratio and overhead tend to decrease with increase in simulation time
IJMLNCE Editorial Note Volume No 03, Issue No 02
No full textThe International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242is now indexed in popular databasessuch asBASE (Bielefeld Academic Search Engine), CNKI Scholar, CrossRef, CiteFactor, Dimensions, DRJI, Google Scholar, Index Copernicus, JournalTOCs, J-Gate, Microsoft Academic, PKP-Index, Portico, ROAD, Scilit, Semantic Scholar, Socolar or WorldCat-OCLC.We are now proud to present the eighth volume of the journal, Volume No-03 Issue No-02, with some high-qualitypapers written by international authors and covering different aspects related to machine learning and collaborative engineering.
Puri et al. published a work entitled “Cloudbin: Internet of Things based waste monitoring system”.In this paper, authors present an IoT-based waste management system called Cloudbin to monitor and control waste garbage in urban areas. To that end, authors use different elements like an ultrasonic sensor, a GPS module or a methane detection mechanism. The problem of waste management is one of the key elements in which governments must take an active part.
Rimal published a work entitled “Machine Learning Prediction of Wikipedia Time Series Data using: R Programming”. In this work, author explains how prediction of automatic learning of Wikipedia time series work using the R environment. To that end, author focused on real data from Cristina Ronaldo, a famous football player, presenting, according to the author, the simplest way to predict times series data and its strengths for data analysis.
Sen et al. published a work entitled “Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques”. This study describes how energy efficiency in cloud computing is one of the most important features to be considered to measure the efficiency of such services, balancing power and quality of the service. Thus, authors discuss how virtual machine migration techniques can help to achieve energy efficiency.
Choudhary published a work entitled “Information Processing in GLIF Neuron Model with Noisy Conductance”. Authors investigate the generalized leaky integrate-and-fire neuron model with stochastic synaptic conductance and investigate the effect of varying concentration of electro-chemicals at the synapse in a single neuron model. To that, they developed a simulation-based study with the temporal encoding technique to analyze the encoding mechanism.
Finally, Kothandan and Sujatha published a work entitled “Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection”. In this paper, authors present and discuss a deep neural network to detect phishing uniform resource locators. They use a feature vector with a stacked denoise auto encoder. In addition, the noisy data is trained to reconstruct a clean input feature vector. Experiments are based on the Ham, Phishing Corpus and Phishload datasets to prove its effectiveness
Opinion Mining:Using Machine Learning Techniques
No full textThe machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies
IJMLNCE Editorial Note Volume No 03, Issue No 01
No full text
The International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242 continues its growth. The journal is becoming more and better indexed in platforms such as BASE (Bielefeld Academic Search Engine), CNKI Scholar, CrossRef, CiteFactor, Dimensions, DRJI, Google Scholar, Index Copernicus, JournalTOCs, J-Gate, Microsoft Academic, PKP-Index, Portico, ROAD, Scilit, Semantic Scholar, Socolar, WorldCat-OCLC. After more than two years of intense work, we are proud to present the seventh volume of the journal, Volume No-03 Issue No-01, which introduces five high quality works written by recognized authors that deal with different aspects within the scope of the journal.
Nigar published a work entitled “A Study on Internet of Things in Women and Children Healthcare”. Author focuses on the importance of the new internet of things related- technologies, which can be applied to healthcare. In fact, its integration with electronic health and telemedicine is gaining attention. The paper describes some methods, practices and prototypes based on the internet of things in the field of healthcare focusing on women and children.
Gunagweare and Kiani published a work entitled “Ultimate Indoor Navigation: A Low Cost Indoor Positioning and Intelligent Path Finding”. Authors deal with the drawbacks of the global positioning system (GPS), which is not useful in indoor environments or places where some buildings can interfere with the satellite signal. In this paper, authors present a simple, low-cost, context-aware and user-friendly indoor navigation system based on a common smart phone.
Jaidev et al. published a work entitled “Artificial Intelligence to Prevent Road Accidents”. Authors focus on the traffic congestion that in turn can lead to more car accidents. The idea of the authors is to study and review the literature related to approaches for detecting unsafe driving patterns to predict accidents with the help of artificial intelligence. Two apparently similar but different examples could be drivers under the influence of drugs or drivers under the influence of alcohol.
Rimal published a work entitled “Deterministic Machine Learning Cluster Analysis of Research Data: using R Programming”. The paper discusses various types of cluster analysis of different data sets with large number of dimensions (iris, utilities, mclust and dbscan). The main goal is to explain the simplest way for clustering analysis whose data structure is wide scattered. The work of the author is based on the R programming language and several specific packages.
Gunjal and Shaik published a work entitled “A Robust Decomposition Based Algorithm for Removal of Pattern Noise from Images”. Authors work on a melting pool of complex vectors to present a technique that requires less computer resources and less time for any image removal of pattern noise compared to other previously stated strategies. The work is based on the idea a picture includes components that can be described separately.
 
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