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Exploring the Role of Mining Wireless Framework in Identifying Human Privacy Vulnerabilities in Internet of Things Networks

Exploring the Role of Mining Wireless Framework in Identifyi...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Dhanalakshmi, R. Venkatramanan, C.B. Rajasekar, B. Basha, S. Mahaboob Soundari, A. Gnana Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai602105 India Sona College of Technology Department of Electrical and Electronics Engineering Tamil Nadu Salem636005 India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9781665462631

One of the most promising technologies with global impact is the Internet of Things (IoT). As IoT systems continue to mature and be implemented, they have the potential to profoundly alter how human societies produce goods, conduct business, and live their daily lives. The number of active Internet of Things gadgets and software has increased exponentially during the last few years. People appreciate IoT's advantages, but its security and privacy concerns are not being addressed at the same rate as the technology itself is progressing. Our research shows that even when wireless IoT communication is encrypted, sensitive user data may be leaked. Using a wireless sniffer stationed outside the user's home, we may infer the user's activities while at home. In this research, we offer a strategy for wireless eavesdropping that may be used too many forms of user privacy. Initially, we identify IoT events by extracting characteristics from the packet sequence. Then, we deduce the user's actions and, in certain cases, their emotional state based on the identified IoT occurrence. In addition, we may deduce the user's lifestyle, and IoT apps by studying the wireless context, which is uncovered by mining thedependencies of the IoT. By disclosing this information, the user's privacy is violated, and the attack surface that an attacker may exploit to take control of the smart home is widened. We use the SmartThings platform to test our eavesdropping methodology and apply it. The results show that our eavesdropping method is both practical and efficient. © 2023 IEEE.

关键词： Internet of things

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

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Journal of Intelligent Learning Systems and Applications 2024年第3期16卷 274-315页

作者： Virgile Simé Nyassi Franklin Tchakounté Blaise Omer Yenké Duplex Elvis Houpa Danga Magnuss Dufe Ngoran Jean Louis Kedieng Ebongue Fendji Department of Mathematics and Computer Science Faculty of Science University of Ngaoundr Ngaoundr Cameroon Department of Computer Engineering University Institute of Technology University of Ngaoundr Ngaoundr Cameroon

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

关键词： Social engineering Hidden Markov Model Vishing Voice Mining

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Electric Vehicles as Flexible Assets for Households and System Operators: Survey Results

Electric Vehicles as Flexible Assets for Households and Syst...

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International Conference on Smart Systems and Technologies (SST)

作者： Nemanja Mišljenović Matej Žnidarec Goran Knežević Danijel Topić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

The transition towards electric vehicles (EVs) is a pivotal response to mitigate environmental concerns and reduce reliance on fossil fuels. This survey-based research aims to provide comprehensive insights into the stance of EV users in Croatia on using EVs as assets for flexibility in operation. Users are surveyed about their EV characteristics and habits, emphasizing charging preferences and possible subsidizing. Because of their larger degree of freedom in operation, family home users are further surveyed about their attitude toward load management, investments for additional sources of electricity and flexibility in operation, and advanced electricity billing models to ensure greater savings. Results show a very conservative stance of users toward actions that deteriorate their comfort level. Further on, users that dwell in a family house are prone to investments if it can generate additional savings and maintain or even increase their comfort level. This survey provided valuable findings for researchers in modeling EVs as a flexible asset in energy management studies, especially in the emerging field of energy communities.

关键词： Surveys Electricity Water heating Transportation Electric vehicles Washing machines Standards Load modeling Resistance heating Investment

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Enhancing Network Intrusion Detection with Deep Learning: A Comprehensive Analysis

Enhancing Network Intrusion Detection with Deep Learning: A ...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Mahbubur Rahman Md. Shahriar Rahman Chinmay Bepery Md. Atikqur Rahaman Md. Samsuzzaman Department of Computer Science and Information Technology Patuakhali Science and Technology University Patuakhali Bangladesh Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

With the growing sophistication of cyberattacks, traditional intrusion detection systems (IDS) are often inadequate. This research addresses these shortcomings by leveraging deep learning techniques to enhance the detection of network intrusions. Previous studies have either retained unnecessary features, leading to biased results, or overly simplified feature sets, resulting in low accuracy. This study systematically refines feature selection and employs advanced deep learning models to improve detection performance. The result is a robust IDS that mitigates bias and enhances accuracy, offering a significant improvement over existing methods. Our findings provide valuable insights into the effective application of machine learning for network security.

关键词： Deep learning Support vector machines Accuracy Computational modeling Training data Network intrusion detection Network security Nearest neighbor methods Feature extraction Principal component analysis

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Sustainable Energy Evolution Using IoT-Enhanced Carbon Capture and Storage for Oil Recovery 6

Sustainable Energy Evolution Using IoT-Enhanced Carbon Captu...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Valli, K. Vijaya Naga Swathi, G. Lumina Julie, R. Saravanan, S. Sujatha, S. Malarvizhi, E. Srkr Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissions and optimize hydrocarbon recovery. This paper examines IoT-enabled CCS for EOR and carbon-neutral oil and gas operations. Real-time data capture and analysis using IoT technologies optimize CCS operations. Installing IoT sensors and devices across the EOR and CCS infrastructure lets operators monitor CO2 injection rates, reservoir pressure, temperature, and fluid composition. The CCS process is precisely controlled using data, improving carbon capture efficiency and lowering operating costs. Remote monitoring and management of carbon storage sites using IoT in CCS for EOR improves environmental sustainability. The collected CO2 is securely and efficiently contained, reducing leakage and environmental effects. Preventing CCS infrastructure problems with IoT-driven predictive maintenance ensures long-term operational dependability. Securely storing collected CO2 offsets oil and gas emissions and contributes to carbon neutrality. EOR process optimization with IoT technologies may boost hydrocarbon recovery, making the switch to greener energy sources easier. IoT-enabled CCS for EOR has the potential to produce carbon-neutral oil and gas operations. The system improves CCS efficiency and sustainability using real-time data, remote monitoring, and predictive maintenance. Integration of IoT with CCS reduces carbon emissions and promotes the industry's worldwide sustainability objectives. © 2024 IEEE.

关键词： Carbon capture and utilization

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Generation of Photo Slideshow with Song based on Closeness between Concept of Lyrics and That of Images

Generation of Photo Slideshow with Song based on Closeness b...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mei Hashimoto Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes a method that allows users to easily convert a large number of still images into movies by displaying photos in sync with memories or favorite songs, which offers a new media viewing method for users to look back at the vast number of photos in their photo folders. We assume here that users select a song and have images stored in local PC. One method of synchronization involves selecting images based on elapsed time of song. However, since lyrics convey meaning, it can be better to display the images that align with both lyrics’ meaning and images’ meaning matched. In order to do this, we need the space which shares the concept of both lyrics and images, and select colors as the concept. Displayed images are determined based on the distance between points that are mapped from words and images to color space. Specifically, the color concepts from words are extracted using the Color Image Scale, and that from images are determined as the most frequent colors by cluster analysis. Experimental results show that it is possible to easily create movies where images with colors that match the mood of the lyrics are displayed.

关键词： Image color analysis Mood Refining Asia Information processing Color Media Motion pictures Synchronization

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Real-time Glass Recycling Quality Assurance and Contamination Reduction with IoT and Random Forest algorithm 7

Real-time Glass Recycling Quality Assurance and Contaminatio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Balasubadra, K. Shadaksharappa, B. Seeni, Senthil Kumar Sridevi, V. Thamizhamuthu, R. Srinivasan, C. R.M.D. Engineering College Department of Information Technology Chennai Tamil Nadu Kavaraipettai India Sri Sairam College of Engineering Department of Computer Science And Engineering Bengaluru Karnataka Anekal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The recycling industry is crucial to environmental protection and resource conservation. Glass recycling has great sustainability potential, but quality assurance and contaminant reduction are major challenges. Integrating the Internet of Things (IoT) with the Random Forest algorithm, this work offers a new solution to these difficulties. IoT devices are used in glass recycling collection, transportation, sorting, and processing. These devices measure glass quality, contaminants, temperature, humidity, and other parameters in real-time. Data is sent to a central system for analysis and decision-making. Random Forest, a machine learning method that handles complicated, multidimensional datasets, powers the system. This algorithm learns from past data and updates with real-time data. It detects contaminants, foreign elements, and faults in recycled glass. This permits fast remedial action, enhancing recycled glass quality and decreasing contamination. Application of this mechanism yields two outputs. First, it improves glass recycling efficiency and produces high-quality glass for remanufacturing. Second, it significantly minimizes the environmental and economic implications of contamination and low-quality glass, which may limit recycling yields and increase waste disposal. In comparison to traditional approaches, the proposed approach that utilizes the IoT and the Random Forest algorithm reduces contamination and ensures the quality of glass recycling by 30%. © 2024 IEEE.

关键词： Internet of things

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Green Measurements for Software Product Based on Sustainability Dimensions

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computer Systems science & engineering 2022年第4期41卷 271-288页

作者： Komeil Raisian Jamaiah Yahaya Aziz Deraman Department of Computer Isfahan(Khorasgan)BranchIslamic Azad UniversityIsfahanIran Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Ocean Technology Engineering&Informatics Universiti Malaysia Terengganu21030TerengganuMalaysia

Software is a central component in the modern world and vastly affects the environment’s *** demand for energy and resource require-ments is rising when producing hardware and software *** study reveals that many studies focused on green hardware;however,limited efforts were made in the greenness of software *** software products are necessary to solve the issues and problems related to the long-term use of soft-ware,especially from a sustainability *** a proper mechanism for measuring the greenness of a particular software product executed in a speciﬁc environment,the mentioned beneﬁts will not be ***,there are not enough works to address this problem,and the green status of software products is uncertain and *** paper aims to identify the green measurements based on sustainable dimensions in a software *** second objective is to reveal the relationships between the elements and measurements through empirical *** study is conducted in two ***ﬁrst phase is the theoretical phase,where the main components,measurements and practices that inﬂuence the sustainability of a software product are identiﬁ*** second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achiev-ing green software *** study has revealed seven green measurements of software product:Productivity,Usability,Cost Reduction,Employee Support,Energy Efﬁciency,Resource Efﬁciency and Tool *** relationships are statistically signiﬁcant,with a signiﬁcance level of less than 0.01(p=0.000).Thus,the hypothesised relationships were all *** contributions of this study revolve around the research perspectives of the measurements to attain a green software product.

关键词： Green software product sustainability dimension empirical study green measurements

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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