scholarly journals Optimization Techniques to Solve Travelling Salesman Problem Using Machine Learning Algorithms

2022 ◽  
Vol 10 (1) ◽  
pp. 274-279
Author(s):  
Prince Nathan S
Keyword(s):  
Machine Learning ◽  
Ant Colony Optimization ◽  
Travelling Salesman Problem ◽  
Learning Algorithms ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Ant Colony ◽  
Lasso Regression ◽  
Q Learning ◽  
The Right

Abstract: Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS

scholarly journals Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

2020 ◽  
Vol 6 (12) ◽  
pp. 426-435
Author(s):  
Aditya, Lalit and Mantosh Kumar
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Heart Disease ◽  
Ant Colony Optimization ◽  
Learning Algorithms ◽  
Particle Swarm ◽  
Machine Learning Algorithms ◽  
Ant Colony ◽  
Disease Classification ◽  
Swarm Optimization

The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

scholarly journals Automatic solution for solar cell photo-current prediction using machine learning

2021 ◽  
Vol 297 ◽  
pp. 01029
Author(s):  
Mohammed Azza ◽  
Jabran Daaif ◽  
Adnane Aouidate ◽  
El Hadi Chahid ◽  
Said Belaaouad
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Linear Regression ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Prediction Methods ◽  
Lasso Regression ◽  
Photo Current

In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

scholarly journals The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform

2020 ◽  
Vol 12 (18) ◽  
pp. 7642 ◽  
Author(s):  
Michael J. Ryoba ◽  
Shaojian Qu ◽  
Ying Ji ◽  
Deqiang Qu
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Prediction Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Success Prediction ◽  
Combined Effects ◽  
Baseline Model ◽  
Predicting Success ◽  
The Right

Only a small percentage of crowdfunding projects succeed in securing funds, the fact of which puts the sustainability of crowdfunding platforms at risk. Researchers have examined the influences of phased aspects of communication, drawn from updates and comments, on success of crowdfunding campaigns, but in most cases they have focused on the combined effects of the aspects. This paper investigated campaign success contribution of various combinations of phased communication aspects from updates and comments, the best of which can help creators to successfully manage campaigns by focusing on the important communication aspects. Metaheuristic and machine learning algorithms were used to search and evaluate the best combination of phased communication aspects for predicting success using Kickstarter dataset. The study found that the number of updates in phase one, the polarity of comments in phase two, readability of updates and polarity of comments in phase three, and the polarity of comments in phase five are the most important communication aspects in predicting campaign success. Moreover, the success prediction accuracy with the aspects identified after phasing is more than the baseline model without phasing. Our findings can help crowdfunding actors to focus on the important communication aspects leading to improved likelihood of success.

scholarly journals An Effective System to Detect Fake Research

2019 ◽  
Vol 9 (1) ◽  
pp. 2971-2975
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Web Based ◽  
User Reviews ◽  
Effective System ◽  
Media Systems ◽  
The Right ◽  
Fake Reviews

Detection of spam review is an important operation for present e-commwebsites and apps.We address the issue on fake review detection in user reviews in e-commerce application, which wasimportant for implementing anti-opinion spam.First we analyze the characteristics of fake reviews and we apply the machine learning algorithms on that data. Spam or fake reviews of the itemsreducing the reliability of decision making and competitive analysis.The presence of fake reviews makes the customer cannot make the right decisions of sellers, which can also causes the goodwill of the platform decreased. There is a chance of leaving appraisals via web-based networking media systems whether states or harming by spammers on specific item, firm alongside their answers by recognizing these spammers just as in like manner spams so as to understand the assessments in the interpersonal organizations sites, we exist a stand-out structure called Netspam which uses spam highlights for demonstrating tribute datasets as heterogeneous subtleties systems to guide spam location treatment directly into gathering issue in such systems.

scholarly journals Big Data, Data Science, and Causal Inference: A Primer for Clinicians

2021 ◽  
Vol 8 ◽  
Author(s):  
Yoshihiko Raita ◽  
Carlos A. Camargo ◽  
Liming Liang ◽  
Kohei Hasegawa
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Causal Inference ◽  
Precision Medicine ◽  
Causal Reasoning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Biological Knowledge ◽  
The Right

Clinicians handle a growing amount of clinical, biometric, and biomarker data. In this “big data” era, there is an emerging faith that the answer to all clinical and scientific questions reside in “big data” and that data will transform medicine into precision medicine. However, data by themselves are useless. It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity to re-think this data-centric view. For example, while precision medicine seeks to provide the right prevention and treatment strategy to the right patients at the right time, its realization cannot be achieved by algorithms that operate exclusively in data-driven prediction modes, as do most machine learning algorithms. Better understanding of data science and its tasks is vital to interpret findings and translate new discoveries into clinical practice. In this review, we first discuss the principles and major tasks of data science by organizing it into three defining tasks: (1) association and prediction, (2) intervention, and (3) counterfactual causal inference. Second, we review commonly-used data science tools with examples in the medical literature. Lastly, we outline current challenges and future directions in the fields of medicine, elaborating on how data science can enhance clinical effectiveness and inform medical practice. As machine learning algorithms become ubiquitous tools to handle quantitatively “big data,” their integration with causal reasoning and domain knowledge is instrumental to qualitatively transform medicine, which will, in turn, improve health outcomes of patients.

scholarly journals Framework of Intelligent System for Machine Learning Algorithm Selection in Social Sciences

2022 ◽  
pp. 21-28
Author(s):  
Dijana Oreški ◽  
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Ranking Algorithms ◽  
Meta Learning ◽  
The Right

The ability to generate data has never been as powerful as today when three quintile bytes of data are generated daily. In the field of machine learning, a large number of algorithms have been developed, which can be used for intelligent data analysis and to solve prediction and descriptive problems in different domains. Developed algorithms have different effects on different problems.If one algorithmworks better on one dataset,the same algorithm may work worse on another data set. The reason is that each dataset has different features in terms of local and global characteristics. It is therefore imperative to know intrinsic algorithms behavior on different types of datasets andchoose the right algorithm for the problem solving. To address this problem, this papergives scientific contribution in meta learning field by proposing framework for identifying the specific characteristics of datasets in two domains of social sciences:education and business and develops meta models based on: ranking algorithms, calculating correlation of ranks, developing a multi-criteria model, two-component index and prediction based on machine learning algorithms. Each of the meta models serve as the basis for the development of intelligent system version. Application of such framework should include a comparative analysis of a large number of machine learning algorithms on a large number of datasetsfromsocial sciences.

scholarly journals Exact Techniques for Hardware Modelling of Machine Learning Algorithms with Building of Neuron Network

2022 ◽  
pp. 89-95
Author(s):  
Ms. Amita P. Thakare ◽  
Dr. Sunil Kumar
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Stock Market ◽  
Travelling Salesman Problem ◽  
Learning Algorithms ◽  
Market Price ◽  
Machine Learning Algorithms ◽  
Neuron Network ◽  
Travelling Salesman ◽  
Simple Arithmetic

System getting to know algorithms are complicated to version on hardware. that is due to the truth that those algorithms require quite a few complicated design systems, which are not effort lessly synthesizable. Therefore, through the years, multiple researchers have developed diverse kingdom-of-the artwork techniques, every of them has sure distinct advantages over the others. In this newsletter, we compare the specific strategies for hardware modelling of the various device gaining knowledge of machine learning algorithms, and their hardware-stage overall performance. this newsletter could be useful for any researcher or gadget dressmaker that needs to first evaluate the superior techniques for ML layout, and then inspired with the aid of this, they are able to similarly enlarge it and optimize the device’s performance. Our assessment is based on the 3 number one parameters of hardware layout; that is; place, power and postpone. Any layout approach that can find a stability among those three parameters may be termed as greatest. This work additionally recommends sure enhancements for some of the techniques, which can be taken up for similarly studies. Machine Learning is a concept to find out from examples and skill, while not being expressly programmed. Rather than writing code, you feed knowledge to the generic formula, and it builds logic supported the info given. for instance, one reasonably formula could be a classification formula. It will place knowledge into totally different teams. The classification formula accustomed notice written alphabets may even be accustomed classifies emails into spam and not-spam. Machine learning has resolve many errors ranging from simple arithmetic problems like TSP (Travelling Salesman Problem) to complex issues like predicting the variations in stock market price, Machine learning algorithms like genetic algorithm, particles swarm optimization, deep nets and Q-learning are currently being developed on software platforms due to the ease of implementation. But the full utilization of core algorithms can only be possible. If they are designed & integrated inside the silicon chip. Companies like Apple, Google and Snapdragon etc. are continuously updating their ICs to incorporate these algorithms. But there is no standard architecture defined to implement these algorithms at chip level, due to these inefficiencies of every alternative multiply when these devices connected together. In this research work, we plan to develop a standard architecture for implementation of machine learning algorithms on integrated circuits so that these circuits. connected together work seamlessly with each other & improve the overall system performance. Finally, we planned to implement at least two algorithms on the proposed architecture & verify its optimization capability for practical systems. Our assessment is based on the 3 number one parameters of hardware layout; i.e.; place, power and postpone. Any layout approach that can find a stability among those three parameters may be termed as greatest. This work additionally recommends sure enhancements for some of the techniques, which can be taken up for similarly studies. Machine learning has solved many problems ranging from simple arithmetic problems like TSP (Travelling Salesman Problem) to complex issues like predicting the variations in stock market price, Machine learning algorithms like genetic algorithm.

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