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international conference on Information Systems and Computer Networks (ISCON)

作者： Rishabh Chauhan Sunil Kumar Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9781665447874

Big data analytics, Artificial Intelligence and cloud computing all together has emerged with an ultimate goal of automating and changing human life by providing their services. these incredibly strong technologies have huge potential by working together, making human life simpler and advanced. To increase the popularity of any of these services, Quality of Service metrics are needed to be defined clear. One of those quality metrics is packet loss or packet delivery, which is the main research idea of this paper. With advancement in Intelligent Network there exists a scope to predict packet loss, by analyzing the recorded network traffic and processing said data under certain machine learning algorithms to create a model to either predict packet loss or tell which variable is responsible for packet loss. this paper includes the study of packet loss behavior of networks. the analytics techniques applied successfully by analyzing big network traffic data, processing of data, using AI and machine learning classifier “XGBoost” and hence designed a model to predict Packet loss which is a QoS metric with an accuracy of 90 percent. the model is personalized to work on WireShark data.

关键词： Cloud computing Analytical models Packet loss Quality of service Telecommunication traffic machine learning Big data

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Is calibration a fairness requirement?: An argument from the point of view of moral philosophy and decision theory 22

Is calibration a fairness requirement?: An argument from the...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Loi, Michele Heitz, Christoph Department of Mathematics Politecnico di Milano Italy Institute of Data Analysis and Process Zurich University of Applied Sciences Switzerland

ISBN: (纸本)9781450393522

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we focus on moral arguments in support of either measure. the conflict between group calibration vs. false positive and false negative rate equality is one of the core issues in the debate about group fairness definitions among practitioners. For any thorough moral analysis, the meaning of the term "fairness"has to be made explicit and defined properly. For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness. More specifically, we equate it with "prima facie wrongful discrimination"in the sense this is used in Prof. Lippert-Rasmussen's treatment of this definition. In this paper, we argue that a violation of group calibration may be unfair in some cases, but not unfair in others. Our argument analyzes in great detail two specific hypothetical examples of usage of predictions in decision making. the most important practical implication is that between-group calibration is defensible as a bias standard in some cases but not others;we show this by referring to examples in which the violation of between-group calibration is discriminatory, and others in which it is not. this is in line with claims already advanced in the literature, that algorithmic fairness should be defined in a way that is sensitive to context. the most important practical implication is that arguments based on examples in which fairness requires between-group calibration, or equality in the false-positive/false-negative rates, do no generalize. For it may be that group calibration is a fairness requirement in one case, but not in another. © 2022 ACM.

关键词： Calibration

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Emotion Identification and Detection from Multichannel EEG Signals Using Artificial Neural Network

Emotion Identification and Detection from Multichannel EEG S...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Cuddapah Anitha E.P John. K. Kathiresan N.J Krishnakumar. V. Karthik J. Sasidevi Department of Computer Science and Engineering Sree Vidyaniketan Engineering College Mohan Babu University Sri Sainath Nagar A Rangampeta Tirupati College of Management SRM Institute of Science and Technology Chennai TamilNadu India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore TamilNadu India Department of Electronics and Communication Engineering Gopalan College of Engineering and Management Seetharamapalaya Basava Nagar Bangalore Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore TamilNadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Samayapuram Trichy TamilNadu

Modified steady inclination affirmation considering multi-station EEG signals is shaping into a basic PC upheld technique for feeling issue finding in sensory system science and psychiatry as a problematic model affirmation task. In light of expansive space data, standard machine learning approaches call for arranging and eliminating various features from a lone or various channels. Along these lines, these strategies could acquaint a test with individuals who need point dominance. then again, huge learning frameworks have been truly applied in various persistent created endeavors to see consolidates and sort different kinds of information. In this overview, standard signals are considered, and a direct yet strong pre-managing methodology is proposed to expand confirmation accuracy. In the interim, by truly learning compositional spatial-standard depiction of unpalatable EEG streams, a cross mix brain network that joins "Convolution Brain Organization (CNN)" and "Long Brain Organization (RNN)" has been used to portray human tendency states. the CNN module changes the chain-like EEG gathering into a 2D packaging improvement to mine the between channel relationship among genuinely lining EEG ***, harshness, and wrath are several the various sentiments that people experience reliably. Electroencephalography (EEG) data should in this way have a convincing inclination recognizing confirmation structure to unequivocally reflect feeling. in the present. In spite of the way that new assessment on this issue can offer great execution estimations, they are at this point lacking for the execution of a full inclination affirmation system.

关键词： Standards organizations Organizations Artificial neural networks machine learning Packaging Electroencephalography Reliability

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Using Resampling Techniques with Heterogeneous Stacking Ensemble for Mobile App Stores Reviews analytics 5th

Using Resampling Techniques with Heterogeneous Stacking Ense...

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5th international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Gomaa, Ahmed El-Shorbagy, Sara El-Gammal, Wael Magdy, Mohamed Abdelmoez, Walid Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Alexandria Egypt

ISBN: (纸本)9783030311292;9783030311285

Over the past few years, a boom in the popularity of mobile devices and mobile apps has appeared. More than 205 billion apps were downloaded in 2018. Developers directly distribute mobile apps to end users via a centralized platform like the " App Store" for iOS or the "Play Store" for Android. the Mobile app developers get continuous feedback from users' reviews added to these stores. Tools like CLAP or AR-MINER were used to crawl reviews from the stores and try to classify them into lots of classifications like (Bug, required feature, usability issue, performance issue,) to facilitate the categorization of issues or features addition to the developer. Some machine learning techniques is used to get the most accurate data classification to help the developer to classify the reported reviews on the stores. this paper presents a machine learning model that uses the Resampling techniques for handling imbalanced classes in addition to ensemble learning and stacking. the model outperforms those tools and enhances the results applied on Mobile App Stores Reviews analytics. In addition to that the paper provides experiments applied on different kinds of datasets and showed improvements in accuracy from 85% (previous model) to 90% (our model) and ROC from 96% to 98% especially on the Reviews dataset.

关键词： Mobile app reviews machine learning Ensemble SMOTE Software engineering Classification Stacking Heterogeneous

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A Study on Seizure Detection Performance in an Automated Process by Extracting Entropy Features

A Study on Seizure Detection Performance in an Automated Pro...

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international conference on Signal Processing and Information Security (ICSPIS)

作者： Sricheta Parui Debasis Samanta Nishant Chakravorty Wathiq Mansoor Uttam Ghosh Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India University of Dubai Dubai United Arab Emirates Meharry School of Applied Computational Sciences Nashville TN USA

ISBN: (纸本)9781665492669

Millions of people of all ages have been diagnosed with epilepsy all over the world. Electroencephalography (EEG), a quantitative component, is vital in identifying and analyzing epileptic seizures. Manual EEG detection takes a long time and has serious consequences. To prevent this circumstance, the world needs alternative detection technologies. For more than a decade, several strategies and procedures have been used to assist medical doctors in detecting technological improvement. Numerous automated seizure identification frameworks that use machine learning approaches have recently been presented to replace traditional methodologies. However, because multichannel EEG data is unpredictable, selecting optimal channels as well as characteristics and classifying them remain unsolved issues. During automatic signaling, the gadget also emits a noisy signal, making detection and prediction challenging. In this paper, we have tested various entropy features and selected the best features to classify seizure patients. First, we preprocessed the data using IMF of the raw signal, and then entropy features were extracted to classify the epileptic patients. the proposed channel selection method, SVM-GA, works well with our framework and successfully removes redundant channels from the data.

关键词： Support vector machines Medical services Signal processing Feature extraction Entropy Electroencephalography data mining

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A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective 21

A Deep Feedforward Neural Network and Shallow Architectures ...

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Proceedings of the 5th international conference on Advances in Artificial Intelligence

作者： Desmond Bala Bisandu Mohammed Salih Homaid irene Moulitsas Salvatore Filippone Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre(DARTeC) Cranfield University UK Department of Computational Engineering Sciences Cranfield University UK and Machine learning and data analytics laboratory digital aviation research and technology centre(DARTeC) Cranfield University UK Department of Civil and Computer Engineering Universita di Roma Italy and Department of Computational Engineering Sciences Cranfield Univeristy United Kingdom

ISBN: (纸本)9781450390699

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector machine models on a binary classification problem. the research revealed that the models achieved different accuracies of flight delay classifications. the Support Vector machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. the Deep Feedforward Neural Network outperformed Support Vector machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher ac

关键词： deep learning Support Vector machine flight delays Classification deep neural network

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Hypothyroidism Prediction and Detection Using machine learning

Hypothyroidism Prediction and Detection Using Machine Learni...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Hebatullah Mohammad Almahshi Esraa Abdallah Almasri Hiam Alquran Wan Azani Mustafa Ahmed Alkhayyat Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Advanced Computing Centre of Excellence Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

the world is facing an increase in the incidence of thyroid diseases, including in Jordan as well, especially hypothyroidism. this condition can be diagnosed through blood samples to know the percentage of hormones (TSH, T3, and T4) and take the appropriate decision after that. the major goal of this study is to make diagnosis easier and reduce the risk of misdiagnosis, which happens all too often. Methods: the use of machine learning in the prediction of disease is a crucial step in the process of data analysis. this paper presents an analysis and classification model that takes into account the various factors involved in the prediction of disease. We applied a number of machine learning algorithms like support vector machine (SVM), Nave Bayes, decision trees, and ensemble to get the best prediction. the decision tree algorithm showed the highest accuracy and lowest cost with a percentage of 97.6 percent and 90, respectively. the results appeared in the form of three classes (compensated hypothyroidism, primary hypothyroidism, and negative). Conclusion: the best results were obtained at the milestone point in computer-aided diagnosis of hypothyroidism. Future work is considered to increase the number of cases that can be detected and diagnosed using machine learning.

关键词： Support vector machines Solid modeling machine learning algorithms databases machine learning Medical services Predictive models

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Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model 11

Investigating Automated Hyper-Parameter Optimization for a G...

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11th international conference on Electronics, Communications and Networks, CECNet 2021

作者： Sani, Usman Sammani Lai, Daphne Teck Ching Malik, Owais Ahmed Digital Science Faculty of Science Universiti Brunei Darussalam Brunei Institute of Applied Data Analytics Universiti Brunei Darussalam Brunei Department of Electrical Engineering Bayero University Kano Nigeria

ISBN: (纸本)9781643682402

this work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered. To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques. Differentiation among the various environments was achieved by the assignment of the clutter height values based on international Telecommunication Union Recommendation (ITU-R) P.452-16. We also included the elevation of the transmitting antenna position as a feature so as to capture its effect on path loss. the best machine learning model observed is K Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2), average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE) values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100 different samplings of train set and test set. Results show that machine learning can also be used to develop path loss models that are valid for a certain range of distances, frequencies, antenna heights, and environment types. HyperBand produced hyper-parameter configurations with the highest accuracy in most of the algorithms. © 2022 the authors and IOS Press.

关键词： Decision trees

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Automated Cell Recognition using Single-cell RNA Sequencing with machine learning 21

Automated Cell Recognition using Single-cell RNA Sequencing ...

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2021 5th international conference on Computational Biology and Bioinformatics

作者： Chengqi Xu Yuetian Chen Yiyang Cao Southern University of Science and Technology China Rensselaer Polytechnic Institute USA The University of British Columbia Canada

ISBN: (纸本)9781450387446

this paper investigates the superiority and limitations of different dimensionality reduction schemes and classification methods in specific single-cell RNA sequencing (scRNA-seq) data sets. With systematic analysis as well as variables-controlled experiments, a pipeline was constructed from rpkm data to final cell type recognition and multiple dimension reduction methods are applied (including PCA, AutoEncoder, ISOMAP, and the combination algorithm of PCA+t-SNE) and multiple classifiers (Random Forest and Support Vector machine, etc.) to obtain the accuracy difference of multiple solutions. By comparing the variation of different models and parameters on the final classification accuracy, this paper summarizes and outlook the information loss and classification effects of different processing schemes on the data set and seeks to find the best combination from them. Using the combination of PCA+SVM, this work obtained 53.13% global maximum accuracy and based on this result to further explore the possibility of improving accuracy and model transfer learning in a wider range of applications.

关键词： Classification Dimension Reduction sc-RNA Seq

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Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding

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BMC BIOINFORMATICS 2022年第SUPPL 3期23卷 158-158页

作者： Liu, Yang Lim, Hansaim Xie, Lei CUNY Hunter Coll Dept Comp Sci 695 Pk Ave New York NY 10065 USA CUNY Grad Ctr 356 5th Ave New York NY 10016 USA

Background Drug discovery is time-consuming and costly. machine learning, especially deep learning, shows great potential in quantitative structure-activity relationship (QSAR) modeling to accelerate drug discovery process and reduce its cost. A big challenge in developing robust and generalizable deep learning models for QSAR is the lack of a large amount of data with high-quality and balanced labels. To address this challenge, we developed a self-training method, Partially LAbeled Noisy Student (PLANS), and a novel self-supervised graph embedding, Graph-Isomorphism-Network Fingerprint (GINFP), for chemical compounds representations with substructure information using unlabeled data. the representations can be used for predicting chemical properties such as binding affinity, toxicity, and others. PLANS-GINFP allows us to exploit millions of unlabeled chemical compounds as well as labeled and partially labeled pharmacological data to improve the generalizability of neural network models. Results We evaluated the performance of PLANS-GINFP for predicting Cytochrome P450 (CYP450) binding activity in a CYP450 dataset and chemical toxicity in the Tox21 dataset. the extensive benchmark studies demonstrated that PLANS-GINFP could significantly improve the performance in both cases by a large margin. Both PLANS-based self-training and GINFP-based self-supervised learning contribute to the performance improvement. Conclusion To better exploit chemical structures as an input for machine learning algorithms, we proposed a self-supervised graph neural network-based embedding method that can encode substructure information. Furthermore, we developed a model agnostic self-training method, PLANS, that can be applied to any deep learning architectures to improve prediction accuracies. PLANS provided a way to better utilize partially labeled and unlabeled data. Comprehensive benchmark studies demonstrated their potentials in predicting drug metabolism and toxicity profiles using sp

关键词： Drug-target interaction Drug discovery Drug toxicity Drug metabolism Deep neural network Graph neural network Semi-supervised learning Self-supervised learning Chemical embedding Artificial intelligence

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