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25th International Arab Conference on Information technology, ACIT 2024

作者： Hassan, Tahir Mansour, Khalid Waseemullah, Waseemullah Memon, Muhammad Qasim Ned University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Arab Open University Faculty of Computer Studies Bahrain Sindh Bhitshah Pakistan

ISBN: (纸本)9798331540012

Climate change is considered a global disaster that has wreaked havoc worldwide. Climate change conditions are primarily driven due to emission of carbon dioxide and other greenhouse gases. Around the globe, several countries are facing serious challenges related to global warming and climate change. The adverse effects of global warming and climate change are recently observed in the form heat waves, floods, extreme rainfall events and drought conditions. The escalating CO2 emission level causes threats of food security and water scarcity. To overcome this, timely and accurate CO2 emissions forecasting is essential for developing effective mitigation strategies in such a manner that CO2 emission impacts can be reduced. The aim of this study is to forecast the total CO2 emissions of upcoming years using various statistical, machine learning models such as ARIMA (Auto-Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Linear Regression, and Decision tree regressor. We had forecasted CO2 emission on the basis of influencing factors, which highly contributes in CO2 emission. The performance of the models is validated using multiple evaluation metrics such as MAE, MSE RMSE,MAPE and R2 square. The results show that CO2 emission is highly affected by fossil fuel consumption. In particular, Linear regression performs better among all applied models, with R2 value 0.93 and MAE, RMSE and MAPE values 5.52, 6.47 and 0.03, respectively. Our research delineates the results and insights that will assist concerned authorities, policymakers and environmental agencies. The present study also aim to inform authorities, helping them to better understand the future of CO2 emission trajectories and make timely efforts and interventions to tackle the climate change and global warming challenges. © 2024 IEEE.

关键词： Low emission

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Büyük veri üzerine perspektif bir bakiş

Büyük veri üzerine perspektif bir bakiş

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2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019

作者： Demirol, Doygun Das, Resul Hanbay, Davut Department of Computer Technologies Genc Vocational School Bingol University Bingol Turkey Department of Software Engineering Technology Faculty Firat University Elazig23119 Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey

ISBN: (纸本)9781728129327

Data, which is the new trend of 21st century, has made difficult to extract meaningful information from itself because of its increasing volume and variety Big data platforms and tools to analyze the size and type of data that cannot be processed by traditional methods have brought new perspectives to data analytics. Big Data are gathered from non-Traditional sources such as wireless sensors, blogs, social media, emails etc. In addition, companies can make better insights of large data sets by using large data analysis mechanisms. In this study, the definition of big data, its relationship with other technologies, technologies used in big data area and general information about analysis techniques are given. © 2019 IEEE.

关键词： Data mining

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Emotion recognition using brain activity 08

Emotion recognition using brain activity

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9th International Conference on computer Systems and Technologies and Workshop for PhD Students in Computing, CompSysTech'08

作者： Rothkrantz, Leon J.M. Horlings, Robert Datcu, Dragos Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Faculty of Military Sciences Netherlands Defence Academy Netherlands

ISBN: (纸本)9789549641523

Our project focused on recognizing emotion from human brain activity, measured by EEG signals. We have proposed a system to analyze EEG signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice. In order to perform this assessment, we have gathered a dataset with EEG signals. This was done by measuring EEG signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion. We found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classification rates of 32% for recognizing the valence dimension from EEG signals and 37% for the arousal dimension. Much better classification rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.

关键词： Emotion Recognition

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Crowd Emergency Evacuation Simulation Time Analysis via Obstacle Optimization Strategy 1

Crowd Emergency Evacuation Simulation Time Analysis via Obst...

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1st International Conference on Material Processing and technology, ICMProTech 2021

作者： Hamizan, S. Roselina, S. Habibollah, H. Yusliza, Y. Lizawati, M.Y. Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Malaysia

The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with obstacle using Anylogic tool and analysis of evacuation time validated using SPSS. The results show that the placement of obstacles near the exit way indeed can reduce the evacuation time and complies with the anti-arching phenomenon during evacuation. © 2021 Institute of Physics Publishing. All rights reserved.

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Reconfigurable universal SAD-multiplier array

Reconfigurable universal SAD-multiplier array

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2005 Computing Frontiers Conference

作者： Calderon, Humberto Vassiliadis, Stamatis Computer Engineering Laboratory Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology P.O. Box 5031 2600 GA Delft Netherlands

ISBN: (纸本)1595930183

In this paper, we investigate the collapsing of some multioperand addition related operations into a single array. More specifically we consider multiplication and Sum of Absolute Differences (SAD) and propose an array capable of performing the aforementioned operations for unsigned, signed magnitude, and two's complement notations. The array, called a universal array, is divided into common and controlled logic blocks intended to be reconfigured dynamically. The proposed unit was constructed around three main operational fields, which are feed with the necessary data products or SAD addition terms in order to compute the desired operation. It is estimated that a 66.6 % of the (3:2)counter array is shared by the operations providing an opportunity to reduce reconfiguration times. The synthesis result for a FPGA device, of the new structure, was compared against other multiplier organizations. The obtained results indicate that the proposed unit is capable of processing in 23.9 ns a 16 bit multiplication, and that an 8 input SAD can be computed in 29.8 ns using current FPGA technology. Even though the proposed structure incorporates more operations, the extra delay required over conventional structures is very small (in the order of 1% compared to Baugh&Wooley multiplier). Copyright 2005 ACM.

关键词： Arrays

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Analysis of Spam Detection Using Integration of Logistic Regression and PSO Algorithm 4

Analysis of Spam Detection Using Integration of Logistic Reg...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Ponmalar, A. Rajkumar, K. Hariharan, U. Kalaiselvi, V.K.G. Deeba, S. Sri Sai Ram Institute of Technology Department of Information Technology Tamilnadu Chennai India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru India Chandigarh University Apex Institute of Technology Department of Computer Science and Engineering Punjab Mohali India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9781665414470

Content-based text classification system can automatically categories the text document into predefined limited classes. But the e-mail document classification is a challenging process in the modern internet environment. The e-mail documents are lightly signified in a great dimensional features space, creating a learning process, and the generalization (abstraction) process is problematic. Spam is unsolicited mail, the bulk of spam mails sent by the spammers who use vast e-mail programs to cover their characteristics and send the spam mails every day with no money. The spam mail directs various effects, including exposing unwanted images, decreasing company productivity, blocking Internet Service Providers' (ISP) networks, etc. Additionally, the spam mail contains an infection, which is gotten ready for some fake movement. Newline PSO is mighty in taking care of multivariable issues, where factors take on genuine qualities that are taken as new lines an independent method to arrange the datasets and contemplate the PSO-based Classifier for Multiclass Data Sets. Spam has become the foundation of decisions utilized by digital crooks to spread vindictive payloads, for example, infections and Trojans. Community-oriented spam recognition methods can manage the enormous scope of electronic communication information subsidized through numerous causes, and special issues needing exposure to electronic communication information. Distance-protecting hash standard arrangements utilized to save the security of electronic communication information while empowering mails orders to detect spam recognition. PSO, a Big Data security safeguarding synergistic spam identification stage based on top of a standard Map Reduce office. Without PSO feature selection, the training accuracy would be less. With the help of the best dataset fit, the classification result will be high. © 2021 IEEE.

关键词： Decision trees

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Personalized learning in hybrid education

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Scientific reports 2025年第1期15卷 18176页

作者： Alaa O Khadidos Hariprasath Manoharan Adil O Khadidos Mohammad N Alanazi Fuhid Alanazi Shitharth Selvarajan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia. Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia. Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Chennai India. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia. College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 13318 Saudi Arabia. Faculty of Computer and Information Systems Islamic University of Madinah Madinah 42351 Saudi Arabia. Department of Computer Science Kebri Dehar University Kebri Dehar 250 Ethiopia. ShitharthS@kdu.edu.et. Department of Computer Science and Engineering Chennai Institute of Technology Chennai India. ShitharthS@kdu.edu.et. Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India. ShitharthS@kdu.edu.et.

The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students. However, many existing methods suggest different techniques for evaluating learners in a hybrid mode, where obtaining clear data sets can be difficult. In the teaching and learning approach, if the defined data set from experts is clear, decisions regarding the learning characteristics of students can be made in a shorter period. In the proposed method the PLA framework categorizes learners into four engagement-based clusters using a three-dimensional sensor model and machine learning classifiers. A dual-controller mechanism (master-slave) dynamically adjusts communication intervals and optimizes video transmission, reducing latency and packet loss. The methodology is validated using MATLAB-based simulations with a dataset of 1,700-5,000 learners, analyzing throughput, delay, packet loss, and cost efficiency. The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers.

关键词： Machine learning (ML) Personalized learning approach (PLA) Teaching learning Video classes

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Wi-Fi access point discovery system for mobile users

Wi-Fi access point discovery system for mobile users

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International Joint Conference on computer Science and Software engineering

作者： Nakai, Yuto Ohshima, Kohta Tajima, Koji Terada, Matsuaki Faculty of Engineering Tokyo University of Agriculture and Technology Tokyo Japan Institute of Engineering Division of Advanced Information Technology Computer Science Tokyo University of Agriculture and Technology Tokyo Japan

ISBN: (纸本)9781457706875

Wi-Fi access provides a very convenient way for mobile terminals to connect the Internet. However, locating points where connections can be made is not always easy. In this paper, we propose a Neighbor Wi-Fi Access Point Advertisement System. The features of our system are as follows: (1) A personal access point function operates on mobile terminals that continuously detect and collect information on nearby WiFi access points (APs) within its radio detection range. (2) Detected and collected Wi-Fi AP information, such as locations and network names, are shared between mobile terminals in proximity with each other. (3) Neighbor Wi-Fi AP information can then be advertised to clients through their personal access points. In our system, a client is a mobile terminal that does not have access privileges for nearby APs. In such cases, clients will not connect to the Internet through the restricted APs;they will simply obtain information regarding APs that are available nearby, yet outside their normal detection range. We developed a prototype system and have confirmed the usability of its basic functions by experiment. © 2011 IEEE.

关键词： Wi-Fi

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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