Machine Learning Algorithms
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2022 ◽  
Vol 201 ◽  
pp. 110878
Author(s):  
Mohan S.R. Elapolu ◽  
Md. Imrul Reza Shishir ◽  
Alireza Tabarraei

2021 ◽  
Vol 248 ◽  
pp. 114793
Author(s):  
Mehrdad Raeesi ◽  
Sina Changizian ◽  
Pouria Ahmadi ◽  
Alireza Khoshnevisan

2022 ◽  
Vol 70 (3) ◽  
pp. 4523-4543
Author(s):  
Ashutosh Kumar Dubey ◽  
Umesh Gupta ◽  
Sonal Jain

2022 ◽  
Vol 12 (1) ◽  
pp. 1-14
Author(s):  
Parmeet Kaur ◽  
Sanya Deshmukh ◽  
Pranjal Apoorva ◽  
Simar Batra

Humongous volumes of data are being generated every minute by individual users as well as organizations. This data can be turned into a valuable asset only if it is analyzed, interpreted and used for improving processes or for benefiting users. One such source that is contributing huge data every year is a large number of web-based crowd-funding projects. These projects and related campaigns help ventures to raise money by acquiring small amounts of funding from different small organizations and people. The funds raised for crowdfunded projects and hence, their success depends on multiple elements of the project. The current work predicts the success of a new venture by analysis and visualization of the existing data and determining the parameters on which success of a project depends. The prediction of a project’s outcome is performed by application of machine learning algorithms on crowd-funding data stored in the NoSQL database, MongoDB. The results of this work can prove beneficial for the investors to have an estimate about the success of a project before investing in it.


Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1934
Author(s):  
Kyoung Jong Park

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups.


2021 ◽  
Author(s):  
Arvind Thorat

<div>In the above research paper we describe the how machine learning algorithm can be applied to cyber security purpose, like how to detect malware, botnet. How can we recognize strong password for our system. And detail implementation of Artificial Intelligence and machine learning algorithms is mentioned.</div>


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour

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.


2021 ◽  
Author(s):  
Daichi Konno ◽  
Yuji Ikegaya ◽  
Takuya Sasaki

Senescence affects various aspects of sleep, and it remains unclear how sleep-related neuronal network activity is altered by senescence. Here, we recorded local field potential signals from multiple brain regions covering the forebrain in young (10-week-old) and aged (2-year-old) mice. Interregional LFP correlations across these brain regions showed smaller differences between awake and sleep states in aged mice. Multivariate analyses with machine learning algorithms with uniform manifold approximation and projection (UMAP) and robust continuous clustering (RCC) demonstrated that these LFP correlational patterns in aged mice less represented awake/sleep states than those in young mice. By housing aged mice in an enriched environment, the LFP patterns were restored to those observed in young mice. Our results demonstrate senescence-induced changes in neuronal activity at the network level and provide insight into the prevention of pathological symptoms associated with sleep disturbance in senescence.


2021 ◽  
Author(s):  
Arvind Thorat

<div>In the above research paper we describe the how machine learning algorithm can be applied to cyber security purpose, like how to detect malware, botnet. How can we recognize strong password for our system. And detail implementation of Artificial Intelligence and machine learning algorithms is mentioned.</div>


Author(s):  
Steven M. Appel ◽  
Cary Coglianese

At the same time that artificial intelligence and machine learning systems are deployed with increasing frequency and success in the private sector, governments around the world are increasingly looking to harness the power of these digital tools to improve a variety of governmental functions, including sorting mail, identifying hazardous chemicals, uncovering securities and tax fraud, and improving traffic flow in congested cities. With time, algorithms will play a much larger role in assisting—or even replacing—humans involved in governmental tasks. This article assesses the range of legal, ethical, and policy concerns implicated by governmental use of algorithmic tools. Although machine-learning algorithms and other automated tools present important challenges for government related to accountability, procedural justice, transparency, privacy, and equality, the issues presented are not qualitatively distinct from the government’s use of other complex analytic tools. Ultimately, existing legal principles should prove to be no intrinsic or insurmountable obstacle to the responsible deployment of artificial intelligence. Yet to help ensure that artificial intelligence is used responsibly, public administrators, elected officials, and concerned citizens must remain vigilant in their use of such digital tools and see that machine-learning systems are ultimately deployed by governments in a manner consistent with both sound ethical judgment and sufficient empathy for those affected by these systems.


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