2023 3rd International Conference on Artificial Intelligence (ICAI)最新文献_第4页

Comparison of Deepfakes Detection Techniques 深度造假检测技术的比较

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136659

Sonia Salman, J. Shamsi

引用次数: 0

Solar Panels Efficiency Improvement Through Orientation Monitoring by Non Contact Method using TMR Sensor 利用TMR传感器非接触式定向监测提高太阳能电池板效率

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136650

Saeed Iqbal, Sehrish Shahnawaz, Uzair Khaleeq uz Zaman, Yasmeen Mohsen Mohamed Ali Elsabagh, Y. Ayaz, Muhammad Jawad Khan

{"title":"Solar Panels Efficiency Improvement Through Orientation Monitoring by Non Contact Method using TMR Sensor","authors":"Saeed Iqbal, Sehrish Shahnawaz, Uzair Khaleeq uz Zaman, Yasmeen Mohsen Mohamed Ali Elsabagh, Y. Ayaz, Muhammad Jawad Khan","doi":"10.1109/ICAI58407.2023.10136650","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136650","url":null,"abstract":"The need for renewable resources has become cru-cial as a result of the escalating rate of energy consumption, the finite supply of fossil fuels, and the growing problem of pollution. Solar energy, in particular, has gained significant traction among renewable resources due to the abundant solar potential present around the world. This paper presents a novel technique of position monitoring and solar tracking of a PV panel with the help of non-contact Tunnel Magneto Resistance (TMR) sensor. Sun tracking of the Solar PV system enhances the energy production and in case of Solar thermal applications like parabolic trough and dishes it is essential. The tracking of sun and system orientation uses different types of sensors and actuators. The sensor size, precision, cost, and the gathered information from the sensor is import. In this research Tunnel Magneto Resisters (TMRs) sensor is used to determine the PV system one axis orientation. The training data sets are created through tests at a curved surface with different measured radial distances. The validation results showed the Bx, By Bz axis error values as 0.1, 0.3 and 0.5 percent maximum in the position measurement.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126716284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human Robot Interaction: Identifying Resembling Emotions Using Dynamic Body Gestures of Robot 人机交互:利用机器人动态肢体动作识别相似情感

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136649

Sara Ali, Faisal Mehmood, Fahad Iqbal Khawaja, Y. Ayaz, Muhammad Sajid, M. B. Sial, Muhammad Faiq Malik, Kashif Javed

{"title":"Human Robot Interaction: Identifying Resembling Emotions Using Dynamic Body Gestures of Robot","authors":"Sara Ali, Faisal Mehmood, Fahad Iqbal Khawaja, Y. Ayaz, Muhammad Sajid, M. B. Sial, Muhammad Faiq Malik, Kashif Javed","doi":"10.1109/ICAI58407.2023.10136649","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136649","url":null,"abstract":"Natural social interaction between a human and robot requires perception ability of robot for complex social behaviors and in turn displaying the learnt emotional behavior during interaction. The emotions expressed with body language are one of the key parameters during Human-Robot Interaction (HRI). This research focuses on extending the concept of affective non-verbal human-robot interaction using full body gestures for closely resembling emotion in absence of intricate facial expressions. The movements of head, torso, legs, and arms are used to link body movements and gestures of a bi-pedal humanoid robot with 28 closely resembling emotions. Variation of speed, frequency, and joint angles of the robot are the key features used for expressing a specific emotion. This research uses Russell's circumplex model to define 8 primary emotional categories which are further grouped into closely associated emotions resembling each other. 33 participants were involved in experimentation to authenticate different emotions using body gestures of robot. Each participant performed 28 trials in which emotions were displayed at random to recognize the state of robot. The results showed an average accuracy of 79.69%. The study authenticates that perceived emotions are highly dependent on body movements, postures, and selected features. This design model can therefore be used for even conveying the closely resembling emotions.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127499882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ML and DL Classifications of Route Conditions Using Accelerometers and Gyroscope Sensors 使用加速度计和陀螺仪传感器的路径条件ML和DL分类

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136666

Ibrahim Khan, Zahid Ahmed

{"title":"ML and DL Classifications of Route Conditions Using Accelerometers and Gyroscope Sensors","authors":"Ibrahim Khan, Zahid Ahmed","doi":"10.1109/ICAI58407.2023.10136666","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136666","url":null,"abstract":"The Modern Era has undergone vast transformations in terms of automation in a wide array of industrial applications. The Artificial Intelligence platform has revolutionized our daily lives with the advent of Intelligent systems. The usefulness of AI in Road Mapping and Route Classification is demonstrated in our study where an Intelligent Transport System (ITS) is proposed which enables monitoring and classification of road conditions by implementing Machine Learning (ML) and Deep Learning (DL) algorithms on data recorded by cellular accelerometer, gyroscope, and GPS sensors. Field Data was recorded in two different scenarios on different vehicles. The route mapping was performed by plotting latitude and longitudes on Google Earth. The labelling of different classes of road was done manually with correlation done via video camera recording. Road Terrain was classified into Bumps. Potholes, Rough and Smooth Roads. Six classical Supervised Machine Learning models (K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Gaussian Naive Bayes Model and Logistic Regression Model) were implemented. Furthermore, Ensembler classifier was used on all six classifiers. The selection of an Optimum Classification Model is done via Soft Voting Algorithm. Finally, K-Fold cross validation was performed to determine the accuracy of our trained model.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Convolutional Neural Networks for Deep Spoken Keyword Spotting 深度语音关键字识别的卷积神经网络

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136648

Nayyer Aafaq, Mehran Saleem, Jahanzeb Tariq Khan, I. Abbasi

引用次数: 0

Machine Learning based Fault Classification using Stray Flux and Stator Current in Induction Motor 基于机器学习的异步电机杂散磁通和定子电流故障分类

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136678

Najeeb Ullah, Muhammad Farasat Abbas, Syed Ali Abbas Kazmi, M. Numan, H. Khalid

{"title":"Machine Learning based Fault Classification using Stray Flux and Stator Current in Induction Motor","authors":"Najeeb Ullah, Muhammad Farasat Abbas, Syed Ali Abbas Kazmi, M. Numan, H. Khalid","doi":"10.1109/ICAI58407.2023.10136678","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136678","url":null,"abstract":"Induction motors have wide range of applications in many industrial processes. Fault detection and classification is an important subject for the sake of safe and reliable operation of induction motors. In this work, ANSYS Maxwell-based simulations are performed for four different loading conditions (25%, 50%, 75%, and 100%) of the induction motor to obtain the stator current and stray flux data under normal and faulty conditions (BRB1, BRB2, BRB3, FPP, and SE). A deep neural network (DNN) machine learning (ML) algorithm is then proposed and compared with support vector machine (SVM) and random forest classifiers (RFC) for the detection and classification of various faults in induction motors using stray flux and stator current. The proposed deep neural network algorithm has shown better accuracy for stray flux compared with SVM and RFC on 100% loading conditions, however, it could not perform well on stator current. The results indicate that the overall performance of all machine learning algorithms is less efficient for stator current than that of stray flux.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Ensemble approach for Fake Reviews Detection 虚假评论检测的高效集成方法

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136652

Asif Iqbal, Muhammad Arslan Rauf, Muhammad Zubair, Tanveer Younis

引用次数: 0

Speaker Recognition in Realistic Scenario Using Multimodal Data 基于多模态数据的现实场景说话人识别

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136626

Saqlain Hussain Shah, M. S. Saeed, Shah Nawaz, M. Yousaf

引用次数: 2

CAS - Attention based ISO/IEC 15408–2 Compliant Continuous Audit System for Insider Threat Detection CAS -基于关注的ISO/IEC 15408-2内部威胁检测合规持续审核系统

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136657

Syed Khurram Jah Rizvi, Khawaja Faisal Javed, Muhammad Moazam

{"title":"CAS - Attention based ISO/IEC 15408–2 Compliant Continuous Audit System for Insider Threat Detection","authors":"Syed Khurram Jah Rizvi, Khawaja Faisal Javed, Muhammad Moazam","doi":"10.1109/ICAI58407.2023.10136657","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136657","url":null,"abstract":"Enterprises are facing information security threats to intranet-based infrastructure and allied systems from external as well as insider cyber actors. A lot of research has been done to identify the evil insiders and prevent their malicious acts. Moreover, there are many others challenges such as limited availability of real labeled data, variations in organizational nature and emerging zero-day attempts from insiders. Therefore, new approaches are essentially required to combat Information Security (IS) non-complaint behavior and emerging insider cyber threats. To this end, we proposed a novel information security auditing-based system for insider threat detection. Unlike traditional audit approaches, this novel approach is based on continuous auditing system. The approach also fulfills the requirements of with ISO/IEC 15408–2 auditing standard. Moreover, system also proposed deep attention neural network to classify the trusted and untrusted users based on the generated activity logs. We evaluated CAS on the defacto dataset for insider threat detection i.e., CERT. 6.2. Evaluation results show that the proposed model learns from real-world data sets to detect IS non-complaint actions to classify the untrusted insider. The proposed model achieved an accuracy of more than 97% and outpaced traditional machine learning approaches.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128098933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning based human recognition via robust Features from audio signals 基于机器学习的人类识别，通过音频信号的鲁棒特征

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136683

U. Sadique, Muhammad Suleman Khan, S. Anwar, Mehran Ahmad

{"title":"Machine Learning based human recognition via robust Features from audio signals","authors":"U. Sadique, Muhammad Suleman Khan, S. Anwar, Mehran Ahmad","doi":"10.1109/ICAI58407.2023.10136683","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136683","url":null,"abstract":"Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven Machine Learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector Machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2ndtop robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0