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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Jezdimirovic, Jovana Pekez, Nenad Kovacevic, Jelena University of Novi Sad Faculty of Technical Sciences Computing and Control Engineering Department Novi Sad Serbia Systems LLC RT-RK Institute for Computer Based Novi Sad Serbia

ISBN: (纸本)9798350347722

The rapid development of digital technology has brought about the challenge of ensuring information security. Cryptography and steganography are among the various techniques available to address this challenge. These techniques come in different forms and provide reliable means of securing information and communication. Despite the existence of numerous options, new variations are still emerging. This paper concentrates on Least Significant Bit (LSB) coding, which is one of the most widely recognized and frequently utilized steganography techniques. The advantages of LSB-based coding methods are high capacity and low complexity. However, predictability is an issue. Audio files' sizes and data redundancy make audio data perfect for steganography, as it is possible to embed large amounts of secret information and easily transmit the signal via various communication channels. However, the use of LSB coding in audio steganography has a drawback due to the extreme sensitivity of the human auditory system (HAS). This means that any noise added to the audio with data embedded in the LSBs can be detected by the HAS, which is dependent on the number of LSBs used. This work presents an improvement of the LSB-based approach, the XORing of LSBs method, with an emphasis on enhanced security. The method achieves this by randomly picking the pairs of bits to be XOR'd while preserving the characteristics of classic LSB methods. To verify the method, it has been implemented on the Cirrus Logic DSP, proving that it may be efficiently used in a real-Time audio environment without interfering with HAS sensitivity levels. © 2023 IEEE.

关键词： Steganography

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Explainable Artificial Intelligence Solution for Online Retail

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Computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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Device-free human presence detection using frequency domain 1

Device-free human presence detection using frequency domain

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1st IEEE International Workshop on Consumer Electronics - Novi Sad, CE WS 2015

作者： Mrazovac, Bojan Todorovic, Branislav M. Kukolj, Dragan Temerinac, Miodrag Department for Computing and Control Engineering Faculty of Technical Sciences Novi Sad Serbia RT-RK Institute for Computer Based Systems Narodnog fronta 23a Novi Sad Serbia

ISBN: (纸本)9781509042685

Device-free human presence (DFHP) detection is a novel technique, mainly applicable to wireless sensor networks in which a human does not need to carry a wireless transceiver to be detected. Irregularities in the radio signal propagation pattern, expressed in a form of received signal strength indicator's (RSSI) variations can indicate possible presence of a human within the existing radio network. In this paper we propose DFHP detection method based on frequency analysis of RSSI variations. The main advantage of the proposed method compared to human detection method based on information entropy is the possibility to determine human presence from furniture layout change. The main drawback is a disability to recognize the absence from the presence of a motionless person in an environment. However, the combination of these two approaches defines a robust and cost-effective DFHP detection method that can be used in various smart home systems without the need for specific hardware installation and exploitation procedures. © 2015 IEEE.

关键词： Fast Fourier transforms

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Renewable energy EV charging station

Renewable energy EV charging station

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2021 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2021 and 2021 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2021

作者： Dorin, Petreus Toma, Patarau Radu, Etz Marcian, Cirstea Technical University of Cluj-Napoca Faculty of Electronics Telecommunications and Information Technology Department of Applied Electronics Cluj-Napoca Romania Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9781665402989

Due to the increased interest in electric vehicle (EV) technologies and the cost reduction of photovoltaic systems, industrial development of battery charging stations for electric vehicles based on solar energy has started. Although it is convenient to charge EVs at night, when there is less electricity consumption, there will always be consumers who prefer or need to charge their EV during daytime and even during peak hours. In this paper, an EV charging station integrating renewable energy in the form of solar energy is proposed and analyzed. Using a local battery pack, the charging station allows semi-fast and fast charging and can be installed in individual homes and apartment buildings, where the maximum power delivered by the grid is limited to 3.6kW. The charging station uses converters widely available on the market. A simulation model for this charging station is developed and various distinct operation modes are presented, validating the correct operation of the entire system. © 2021 IEEE.

关键词： Solar energy

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Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

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Computers, Materials & Continua 2021年第12期69卷 4095-4111页

作者： Mohamed Esmail Karar Omar Reyad Abdel-Haleem Abdel-Aty Saud Owyed Mohd F.Hassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt Faculty of Science Sohag UniversitySohag82524Egypt Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Mathematics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Computer and Information Science CeRDaSUniversiti Teknologi PetronasMalaysia

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural ***,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm *** this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely *** sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the *** trees are labeled based on the sensor node number to identify the infested ***,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,*** proposed infestation classifier has been successfully validated on the public TreeVibes *** includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,*** to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio *** resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,*** the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

关键词： Red palm weevils smart precision agriculture internet of things artificial intelligence

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Time measurement synchronization using GPS units

Time measurement synchronization using GPS units

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2nd International Conference on Applied Informatics and computing Theory, AICT'11

作者： Slanina, Zdenek Kasik, Vladimir Musil, Karel Department of Measurement and Control Faculty of Electrical Engineering and Computer Science Technical University of Ostrava 17. listopadu 15 708 33 Czech Republic

ISBN: (纸本)9781618040343

This article deals with methods research for synchronization pulses utilization provided by some GPS units for measurement synchronization. Initially there are problems of GPS described. Further there are drawn out realized prototypes of measurement systems based on microcontrollers where is expected milliseconds measurement accuracy followed by description GPS synchronized time source implementation in logic gate arrays (FPGA), which allows accurate measurement of microseconds and better. This article describes the theoretical premises and selected measured properties of realized solutions. In conclusion there are prepared experiments described which should be realized during research of this issue.

关键词： Synchronization

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Using of ubiquitous computing principles to develop a mobile user adaptive system framework

Using of ubiquitous computing principles to develop a mobile...

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作者： Krejcar, Ondrej Department of Measurement and Control Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava-Poruba Czech Republic

ISBN: (纸本)9781424473359

Ubiquitous computing (UbiCom) is idea which describe an information and communication technology systems which enable information and their tasks to support intelligent human computer interface, which is everywhere to use and also the technology background is invisible to user. Paper deal with a using of UbiCom principles to develop a mobile user adaptive system framework based on Predictive Data Push Technology (PDPT) Framework and Biotelemetrical Monitoring System (BMS) as two joined parts of complex User Adaptive System (UAS) framework. Main focus is in contribution of UAS to user or patient and his life quality.

关键词： Ubiquitous computing

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RT visualisation using aJile aJ-80 real time embedded platform

RT visualisation using aJile aJ-80 real time embedded platfo...

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作者： Krejcar, Ondrej Adamec, Ondrej Department of Measurement and Control Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava-Poruba Czech Republic

ISBN: (纸本)9781424473359

This work deal with a possibilities of using an aJ-80 embedded JA VA processor in real usage. Microprocessor aJ- 80 join together the advantages of programming in a higher programming language (as Java) and the system of one-chip controlling components (ATMEL, MICROCHIP, PIC, MOTOROLA etc.). Tested platform is suitable for Real Time - RT. We found several problems suitable for testing of the processor aJ-80 and its RT features to evaluate test platform. Based on the evaluation, the performance of the hardware program of the processor aJ-80 is very effective. In this respect, the product is recommended to use in many hard RT areas.

关键词： IIR filters

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

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Neural computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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