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IOT Based Smart Parking System Using Ensemble Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Implementation of a Mixed Triple Logistic Map and the BB84 Quantum Key Distribution for Secure Image Communication

Implementation of a Mixed Triple Logistic Map and the BB84 Q...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Morissa, Virnika Shefira Gina Setiadi, De Rosal Ignatius Moses Universitas Dian Nuswantoro Informatics Engineering Department Semarang50131 Indonesia Universitas Dian Nuswantoro Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Semarang50131 Indonesia

ISBN: (纸本)9798350366648

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks. © 2024 IEEE.

关键词： Quantum cryptography

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Behavior Trees and State Machines in Robotics Applications

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IEEE Transactions on Software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

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Forwarding Strategy Performance Using NFD Pipeline Measurements on Vehicular Named Data Networking (V-NDN) 9

Forwarding Strategy Performance Using NFD Pipeline Measureme...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Syambas, Nana Rachmana Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers. © 2023 IEEE.

关键词： Pipelines

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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Data Analytics for Volunteer-Based Educational Application 16

Data Analytics for Volunteer-Based Educational Application

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16th IEEE AFRICON, AFRICON 2023

作者： Sherif, Omar Zaghloul, Yasmine Sharaf, Nada Informatics and Computer Science The German International University Cairo Egypt German International University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350336214

In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational Data Mining (EDM) is increasingly gaining more importance. Educational data mining is trending as it is the analysis method for analyzing educational data. It involves checking the relationship between the characteristics of students and which features affect their final grades the most. It can also involve predictive modeling by predicting the final grades of students in the future to help educational institutes rescue failing students before they actually fail. Predictive analysis was the main focus in past years where most researchers targeted predicting grades of students. However, not all educational institutes are able to collect the amount of data suitable for machine learning models to achieve good accuracy. That is why the main target of the work presented in this paper is to develop an interactive interface that gives the ability to educational institutes to check by themselves the relation between different factors using correlation mining. The output of the tool is visual with message boxes to make it understandable by users. That is the best way to discover hidden patterns and trends without the need for a large amount of data in addition to removing the cost of deploying machine learning models. The second goal of the work is to give teachers the ability to check the quality of the content of their slides to help them provide a better learning process. © 2023 IEEE.

关键词： Students

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Arabic Named Entity Recognition Using Clustered Word Embedding 19th

Arabic Named Entity Recognition Using Clustered Word Embeddi...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Sabty, Caroline Elmahdy, Mohamed Abdennadher, Slim Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Informatics and Computer Science German International University Cairo Egypt

ISBN: (纸本)9783031237928

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 572-602页

作者： Yuniarti, Wenty D. Hartati, Sri Priyanta, Sigit Surjono, Herman D. Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Information Technology Universitas Islam Negeri Walisongo Semarang50185 Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Islam Negeri Walisongo Yogyakarta55281 Indonesia

E-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. © Authors retain all copyrights.

关键词： Contrastive Learning

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Mobile Robot Tracker for the Presence of Gas in a House Prototype Using Left Hand Rule Based on PID

Mobile Robot Tracker for the Presence of Gas in a House Prot...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jayadi, Akhmad Adhinata, Faisal Dharma Putri, Novia Utami Setiawan, Ridho Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung Indonesia Institut Teknologi Telkom Purwokerto Faculty of Informatics Jawa Tengah Indonesia

ISBN: (纸本)9798350369359

The designed mobile robot can follow the arena automatically and can detect gas automatically without human assistance. This mobile robot can run from a predetermined starting point, and will detect gas at a predetermined point. This mobile robot is built with an Arduino Uno microcontroller which uses the C programming language and is equipped with 2 ultrasonic sensors and an MQ-6 sensor. The results obtained after testing in this study are the establishment of an intelligent mobile robot system that is able to navigate walls with the help of ultrasonic sensors and is able to detect gas wells. In this study using the PID value, namely kp=40ki= 0.0001kd=2 and for gas readings the MQ-6 sensor detects the presence of gas of 800 PPM. Thus, this mobile robot is expected to be implemented so that it can be used to facilitate the role of humans in detecting gas leaks based on intelligent mobile robots. © 2023 IEEE.

关键词： Gases

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