scholarly journals Biological Correlates Of Machine Learning

2021 ◽  
Author(s):  
Walter Gordon Kruberg
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Neural Nets ◽  
Training Algorithms ◽  
Memory Research ◽  
The Public ◽  
Intelligent Machines ◽  
Intelligent Behavior ◽  
Network Operations ◽  
Error Terms

The greatest successes of artificial intelligence are intelligent machines founded on models of how neurons interact with each other. In creating these models, machine-learning modelers divide intelligent behavior into separate learning and operating experiences: training and inference. We, the public, see machine-learning engines while they are operating, in inference mode, as they interpret our requests and images. To make them operational, their neural nets are trained using algorithms that are widely tested, optimized, and which even compete against each other regularly. The most successful training algorithms use back-propagation to train their neural networks.What has been learned about intelligence from these models is largely absent in biological models: that creating the memories underlying intelligent behavior occurs independently of network operations and requires network-level functions. This paper recasts memory research in the context of those two requirements and outlines novel biological correlates for training and inference modes, vector spaces and error terms. Specific biological machinery is identified as holding the key to understanding memory creation: the operation of tripartite synapses and how astrocytes act as normalization operators to manage synaptic plasticity.

scholarly journals Trigonometric Inference Providing Learning in Deep Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6704
Author(s):  
Jingyong Cai ◽  
Masashi Takemoto ◽  
Yuming Qiu ◽  
Hironori Nakajo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Trigonometric Approximation ◽  
Model Parameters ◽  
Training Algorithms ◽  
Activation Functions ◽  
Classical Training ◽  
Sum Formula

Despite being heavily used in the training of deep neural networks (DNNs), multipliers are resource-intensive and insufficient in many different scenarios. Previous discoveries have revealed the superiority when activation functions, such as the sigmoid, are calculated by shift-and-add operations, although they fail to remove multiplications in training altogether. In this paper, we propose an innovative approach that can convert all multiplications in the forward and backward inferences of DNNs into shift-and-add operations. Because the model parameters and backpropagated errors of a large DNN model are typically clustered around zero, these values can be approximated by their sine values. Multiplications between the weights and error signals are transferred to multiplications of their sine values, which are replaceable with simpler operations with the help of the product to sum formula. In addition, a rectified sine activation function is utilized for further converting layer inputs into sine values. In this way, the original multiplication-intensive operations can be computed through simple add-and-shift operations. This trigonometric approximation method provides an efficient training and inference alternative for devices with insufficient hardware multipliers. Experimental results demonstrate that this method is able to obtain a performance close to that of classical training algorithms. The approach we propose sheds new light on future hardware customization research for machine learning.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things

2021 ◽  
Vol 11 (1) ◽  
pp. 283-287
Author(s):  
Saranya N ◽  
◽  
Kavi Priya S ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
High Volume ◽  
Deep Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Batch Size ◽  
Feed Forward

In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.

scholarly journals John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

2021 ◽  
Vol 28 (3) ◽  
pp. 442-446
Author(s):  
Valentin Kuleto ◽  
Milena Ilić
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Problem Solving ◽  
Computer Science ◽  
Image Recognition ◽  
Language Processing ◽  
Educational Innovation ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Future Learning

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

Evaluation of Machine-Learning Tools for Predicting Sand Production

2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu
Keyword(s):  
Machine Learning ◽  
Niger Delta ◽  
Oil And Gas ◽  
Back Propagation ◽  
Oil And Gas Industry ◽  
Learning Tools ◽  
Sand Production ◽  
Data Set ◽  
Test Set ◽  
Gas Industry

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.

Design of Intelligent Network to Predicate Phenol Removal from Waste Water by Emulsion Liquid Membrane

2021 ◽  
Vol 1021 ◽  
pp. 115-128
Author(s):  
Suheila Abd Alreda Akkar ◽  
Sawsan Abd Muslim Mohammed
Keyword(s):  
Neural Network ◽  
Liquid Membrane ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Volume Ratio ◽  
Phenol Removal ◽  
Acid Extraction ◽  
Training Algorithms ◽  
Membrane Phase ◽  
Emulsion Liquid Membrane

This research introduced Intelligent Network's proposed design for predicting efficiency in the removal of phenol from wastewater by liquid membrane emulsion. In the inner phase of W / O emulsions, phenol extraction from an aqueous solution was investigated using emulsion liquid membrane prepared with kerosene as a membrane phase, Span 80 as a surfactant, and NaOH as a stripping agent. Experiments were conducted to investigate the effect of three emulsion composition variables, namely: surfactant concentration, membrane phase to-internal (VM / VI) volume ratio, and removal phase concentration in the internal phase, and two process parameters, feed phase agitation speed at organic acid extraction rates, and emulsion-to-feed volume ratio (VE / VF). More than 98% of phenol can be extracted in less than 5 minutes. This article describes compares the performance of different learning algorithms such as GD, RB, GDM, GDX, CG, and LM to predict the efficiency of phenol removal from wastewater through the liquid emulsion membrane. The proposed neural network consisted of (7, 11, 1) neurons in the input , hidden and output layers respectively feed forward ANN with various types of back propagation training algorithms were developed to model the emulsion liquid membrane removal of phenols. The values predicted for the neural network model are found in close agreement with the results of the batch experiment using MATLAB program with a correlation coefficient ( R2) of 0.999 and Mean Squared Error (MSE) of 0.004.

A Zero-Trust Methodology for Security of Complex Systems With Machine Learning Components

2021 ◽  
Author(s):  
Britta Hale ◽  
Douglas L. Van Bossuyt ◽  
Nikolaos Papakonstantinou ◽  
Bryan O’Halloran
Keyword(s):  
Machine Learning ◽  
Security Threats ◽  
Training Algorithms ◽  
Technological Advances ◽  
Autonomous Machines ◽  
Complex Models ◽  
Aerial Vehicle ◽  
Very High ◽  
Testing Coverage

Abstract Fuelled by recent technological advances, Machine Learning (ML) is being introduced to safety and security-critical applications like defence systems, financial systems, and autonomous machines. ML components can be used either for processing input data and/or for decision making. The response time and success rate demands are very high and this means that the deployed training algorithms often produce complex models that are not readable and verifiable by humans (like multi layer neural networks). Due to the complexity of these models, achieving complete testing coverage is in most cases not realistically possible. This raises security threats related to the ML components presenting unpredictable behavior due to malicious manipulation (backdoor attacks). This paper proposes a methodology based on established security principles like Zero-Trust and defence-in-depth to help prevent and mitigate the consequences of security threats including ones emerging from ML-based components. The methodology is demonstrated on a case study of an Unmanned Aerial Vehicle (UAV) with a sophisticated Intelligence, Surveillance, and Reconnaissance (ISR) module.

DETERMINING LEARNING SUCCESS of kNN ALGORITHM on ZOO DATASET

2021 ◽  
Vol 8 (18) ◽  
pp. 78-82
Author(s):  
Ahmet ÇELİK
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Method ◽  
Data Set ◽  
Intelligent Machines ◽  
Weight Parameter ◽  
Living Things ◽  
Learning Success ◽  
Similarities And Differences ◽  
Two Parameters

People learn by examining, observing and researching their environment. They actually gains experience from what they have learned. By using the experience they have gained, they can adapt to the new situation they encounter and make decisions. People always make decisions by comparing their previous knowledge while describing objects and classifying them. Similarities and differences to previously learned objects are very effective in decision making. It has been shown in the studies that the experiential learning method can also be used on machines. Intelligent machines and devices that use machine learning methods in their structure are widely used in many areas. Machine learning can be performed using different algorithms. These algorithms use the attributes of the objects in the data set when making decisions. Similarities and differences in the attributes of objects are obtained by comparing them with previous experiences. As a result of the comparison, a decision is made and predictions are made about the classes of the objects. In this study, kNN machine learning algorithm, which is a supervised learning method, was used on the Zoo dataset. In this data set, there are attributes of common living things. By using these attributes, the classes of living things in the data set are determined. The “k” neighbor value and weight parameter selected in the kNN algorithm affect the learning success. In this study, the effect of two parameters used in the kNN algorithm on learning success is shown. According to the results obtained, the "k=1" neighbor value and the "Distance Weight" parameter were selected and the highest success result was obtained.

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