International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE)
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Heart Disease Prediction Propagation approach
No full textData mining methods are used to test complicated data and regression processing on the basis of input data sets is used for the estimation of 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 main data for the detection of cardiac disorders in this study. The Back Propagation Method is used in tandem with k-means clustering algorithm to cluster knowledge for improved prediction research performance. The output of the implemented algorithm 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)
Biomedical Engineering in Healthcare
No full textEngineering plays an unmistakable job in serving and progression of social insurance. With the overall rise in population of over eight billion globally all around the world which still continues to grow. With such growing population rates, there arises a universal demand for long living along with healthy and active lifestyle.
To meet these requirements of human race there is a strict requirement of such a discipline which makes the interaction of engineering with the human body possible. This is provided by one of the quickest developing fields of designing known as Biomedical Engineering. Biomedical Engineering is an interdisciplinary methodology. It is a broad field which involves an immense range of controls.
Biomedical specialists (likewise called bioengineers) utilize their sound information on maths and science to tackle wellbeing related issues. Materials, devices and procedures are created by biomedical specialists that aides in avoidance or treatment of ailments or restore patients. The field of biomedical designers incorporates specialization for biomaterials; bioinstrumentation; biomechanics; medicinal imaging; restoration; and cell, tissue, and hereditary building as indicated by the Biomedical Engineering Society.
In this paper we will discuss various introductory terms related to biomedical engineering and health care industry which are actually amalgated together. We will further discuss the pros and cons of biomedical engineering on health care industry. Devices and instruments which are used in biomedical engineering are also discussed in this paper.
This paper mainly focuses on some of the latest medical devices, instruments and technologies like biosensors, biomedical signal processing, biomedical imaging and image processing, bioinformatics and computational biology, health informatics, biomechanics, bio robotics, diagnostic, cardiopulmonary systems engineering, and therapeutic systems, neural engineering, rehabilitation engineering, variable and implantable technologies, micro and nano technologies, tissue engineering and regenerative medicine, biomedical engineering in education industry and society.
A case study has also been included to support the understanding of the above technologies viz. a case study on image-guided interventions. The discussion has been concluded with the observation that biomedical engineering can be deeply integrated with healthcare and various devices and instruments can be designed in order to cure various diseases. These devices are ergonomically designed.
The Future extent of biomedical designing in medicinal services is being discussed in this paper. Medical diagnostic tools nearly triple every year as with biomedical Engineering, every new advancement taking place to cure various diseases. Biomedical engineers are developing advanced tools for various health diseases. Consequently, there is an immense extent of Biomedical Engineering in Healthcare
The mechanism for Predictive Load Control in the Implementation Framework through Genetic Intelligence
No full textCloud Storage is a pay-per-use range of resources. The consumer wants to ensure that all requirements met in a limited time for optimal performance in cloud applications that are every day. Load balancing is also crucial, and one of the essential cloud computing issues. It is also called the NP-full load balancing problem since load balancing is harder with increasing demand. This paper provides a genetic algorithm (GA) framework for cloud load. Depending on population initialization duration, the urgent need for the proposal considered. The idea behind the emphasis is to think about the present world. Real-World Scenario structures have other targets that our algorithms can combine. Cloud Analyst models the suggested method. A load-balancing algorithm based on the forecasts of the end -to - end Cicada method given in this paper. The simulator for cloud services or Cloud Sim can be used as a simulator to achieve a low computing requirement algorithm and a better workload balance. A simulation of cloud services is feasible. The result indicates the possibility of offering a quantitative workload balancing approach that can help manage workloads through the usage of computer resources. The next generation of cloud computing would make the network scalable and use available resources effectively. Load balancing, a significant problem in the cloud storage, and distributed workload over
Several nodes to ensure that no single resource is overloaded. This can be seen as a question of efficiency, and its solution must adapt to the environment and styles of work to the right balance of load. This article introduces a new approach to genetic algorithm (GA) power loads. When trying to reduce the complexity of a particular task, the algorithm handles the cloud computing fee. A software analyst model evaluated the proposed method of load balancing. Results from simulations for a standard sample program show that the suggested algorithms outperform current methods like FCFS, Round Robing (RR), and local search algorithms Stochastic Hill Climbing (SHC)
Revealing Brain Tumor Using Cross-Validated NGBoost Classifier: NG Boost Classifier
No full textBrain is the most complicated and delicate anatomical structure in human body. Statistics proves that, among various brain ailments, brain tumor is most fatal and in many cases they become carcinogenic. Brain tumor is characterized by abnormal and uncontrolled growth of brain cells, and takes up space within the cranial cavity and varies in shape, size, position and characteristics viz., can be benign or malignant, which makes the detection of brain tumor very critical and challenging. The vital information a neurologist or neurosurgeon needs to have is the precise size and location of tumor 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 reveling brain tumors. NGBoost algorithm along with 5-fold stratified cross-validation scheme is proposed as classifier model that automatically detects patients with brain tumors. The proposed method is implemented with necessary fine-tuning of parameters which is compared against ensemble based baseline classifiers such as AdaBoost, Gradient Boost, Random Forest and Extra Trees Classifier. Experimental study implies that proposed method outperforms baseline models with significantly improved efficiency. The interfering features those have impact on brain tumor classification are ranked and this ranking is retrieved from the best classifier model
A Study on Internet of Things in Women and Children Healthcare
No full textIndividual entities are being connected every day with the advancement of Internet of Things (IoT). IoT contains various application domains and healthcare is one of them indeed. It is receiving a lot of attention recently because of its seamless integration with electronic health (eHealth) and telemedicine. IoT has the capability of collecting patient data incessantly which surely helps in preventive care. Doctors can diagnose their patients early to avoid complications and they can suggest further modifications if needed. As the whole process is automated, risk of errors is reduced. Administrative paperwork and data entry tasks will be automated due to tracking and connectivity. As a result, healthcare providers can engage themselves more in patient care. In traditional healthcare services, an individual used to have access to minimal insights into his own health. Hence, they were less conscious about themselves and depended wholly on the healthcare facilities for unfortunate events. But they can track their vitals, activities and fitness with the aid of connected devices now. Furthermore, they can suggest their preferred user interfaces. This paper describes several methods, practices and prototypes regarding IoT in the field of healthcare for women and children
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
Smart Building: A Low Cost Indoor Positioning and Intelligent Path Finding
No full textDespite the rapid improvement in mobile devices, overall gradual growth in the ubiquitous computing field, the wide applicability, more usefulness of location based services in general and indoor navigation. The Global Positioning System (GPS) has undergone tremendous improvement since the 1900s and it, indeed is considered one of the most successful navigation systems known to date. However, it is still inefficient for sufficiently accurate positioning in both indoor environments and environments with many tall buildings such as skyscrapers since such buildings block or interfere with its signal transmissions. In particular, building a sufficiently accurate, efficient and relatively cheap indoor navigation system in a GPS-free environment is still a challenging task with a lot of tradeoffs and constraints to put into consideration. In this paper, a simple yet robust, low-cost, context-aware user-interactive, user-friendly hybrid of fingerprinting and dead reckoning indoor navigation system suitable for both the visually and the physically disabled as well that takes advantage of the results yielded by sensor fusion is proposed. The presented system is also designed to allow for efficient evacuation of users in cases of emergences. The prototype is made majorly of the following parts; user tracking, optimal, context-aware and dynamic route calculation and planning and dynamic route representation with an upper bound of 2m and an average of 0.8-1.3m accuracy. All that is required from the user is a smart phone without installation of extra hardware
Recommender System For Educational Analysis In Prediction of Appropriate Career & Domain Recommendations using Machine Learning Techniques
No full textThese days Career and Domain options have always been a very big ambiguous decision-making process for many prospective aspirants. Many aspirants make substantial domain changes very late in their career which may result in drastic effects on their career as well as their financial status. Many reports suggested that companies have suffered huge losses because of making wrong choices regarding the domain and employee interest. Hence providing a common platform early in the education sector for both the aspirants as well as companies that would provide appropriate domain suggestions for aspirants as well as right employee choices for companies would be highly beneficial that could help in generating better results when compared to the traditional ways of career choices employment. In this research, we are proposing a recommender system based model that would bridge the gap and help in formulating future need
Performance Evaluation of LAR under Highway and City Scenarios: Performance Evaluation of LAR
No full textVehicular ad-hoc networks have gained immense popularity as a research domain as more and more vehicles interact with each other to communicate information. This paper is aimed at evaluating the performance of Location Aided routing protocol (LAR) for Vehicular Ad-hoc networks (VANETs) using NS2 and SUMO. This protocol is evaluated under highway and city scenarios obtained from Open Street Map (OSM) and Bologna Ringway dataset respectively. The performance metrics considered for these scenarios are throughput, packet delivery ratio (PDR), routing overhead. The above mentioned parameters are calculated by varying the simulation time and number of vehicles. The results obtained are graphically plotted and analyzed
IJMLNCE Editorial Note Volume No 02, Issue No 04
No full textPreface
The International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242is in a clear stage of expansion.The journal now appears in popular indexes such as BASE (Bielefeld Academic Search Engine), CrossRef, CiteFactor, DRJI, Google Scholar, Index Copernicus, J-Gate, Portico, PKP-Index, ROAD, Scilit and Socolar. After two years of hard work we are proud to present the sixth volume of the journal, Volume No-02 Issue No-04, with other five high quality works written by international authors and covering different aspects related to machine learning and collaborative engineering.
Chauhan et al. [1] published a work entitled “IoT Based Intelligent Vehicle Parking Solution System”.In this paper, authors present a vehicle parking solution based on the Internet of Things through four different layers to compose the parking system: sensor, hardware, cloud and application. The main idea is that users are updated in real time on the available spaces near the destination allowing them to choose the one more suitable for their needs.
Mahmud et al. [2] published a work entitled “Domestic Mechanization System with IoT and Robotics”. In this work, authors discuss home automation based on the Internet of Things focusing on three different projects: a smart window, a smart almirah and a smart bookshelf. They pay special attention to the smart window, which can be controlled in accordance with the weather conditions, the house temperature and the proper balance of gas in the air.
Sharma et al. [3] published a work entitled “DNA Based Storage: Introduction, Characteristics, Applications and Challenges”. This study describes how the domain of knowledge of storage systems based on how the DNA works, since it is a viable alternative for conventional methods. They review the past, the current state of the art, with the advantages and drawbacks, and they also explore different challenges that would be interesting to overcome in the future.
Dash and Mohanty [4] published a work entitled “A Machine Learning Approach for Speech Detection in Modern Wireless Communication Environment”. Authors propose a technique that improves the intelligibility of speech quality in noise environments. To that end, authors propose the use of different elements like an OFDM modulation based communication system, a neural network model of RBFN and different parameters such as energy and fundamental frequency.
Gupta et al. [5] published a work entitled “Study of Concurrency Control Techniques in Distributed DBMS”. In this paper, authors present and discuss various lock-based concurrency control techniques for distributed data base management systems. They also show a comparative study of various two phase locking based concurrency control techniques. The focus is on proposing a proper concurrency control technique to maintain the integrity of database systems