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IoT Implementation in Various Applications: A Detailed Review of Cyber Security Issues and Challenges 3

IoT Implementation in Various Applications: A Detailed Revie...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Lourens, Melanie Gangodkar, Durgaprasad Tiwari, Mohit Buddhi, Dharam Dharamvir, Dharamvir Kuchhal, Shikha Durban University of Technology Department of Human Resources Management Durban South Africa Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Uttaranchal University Uttaranchal Institute of Technology India The Oxford College of Engineering Dept. of Mca Bangalore560068 India Ece Spitm Dcrust India

ISBN: (纸本)9798350399264

Data must be protected against cybercrime as the network revolution grows and becomes more complicated. With the purpose to intentionally damage sensitive and secret information, cybercrimes cost the world economy billions of dollars. Because these crimes are perpetrated often, it is vital to increase cyberspace security to lessen and perhaps even prevent the effects of cybercrime. The internet of things (IoT) phenomena is now the subject of study, as privacy and safety are acknowledged as the primary concerns for Iot., particularly in light of the fact that they are being used in crucial contexts like healthcare systems. The current state of safety in the IoT sector is examined in this paper, along with security-related problems. We look at particular security needs and methods for resolving these problems. IoT safety is being recognised as a problem that blockchains may help with. © 2023 IEEE.

关键词： Cybersecurity

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UrduSpeakXLSR: Multilingual Model for Urdu Speech Recognition

UrduSpeakXLSR: Multilingual Model for Urdu Speech Recognitio...

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International Conference on Emerging Technologies, ICET

作者： Hira Mohiuddin Zahoor Ahmed Maha Kasi Bakhtiar Kasi Dept. of Computer Science Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

Speech recognition, a disruptive technology, has revolutionized human-machine interaction. While numerous Automatic Speech Recognition (ASR) models are publicly available via HuggingFace, the majority cater to English language. For Urdu, however, models are scarce or closed-source, with open-sourced ones often lack the robustness. Our research addresses this scarcity, focusing on the challenges posed by dialects, slangs, and accents. In this work, we introduce an innovative ASR model leveraging the pretrained XLS-R model based on Wav2Vec2.0 architecture, trained on CommonVoice corpus V11. Our approach outperforms other deep learning-based techniques both qualitatively and quantitatively, offering promising results for an under-resourced language.

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions.

关键词： Heating systems Analytical models Array signal processing Communication systems Atmospheric modeling System performance Modulation Rayleigh channels Optimization Signal to noise ratio

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Comparative Study of Customer Churn Prediction Based on Data Ensemble Approach

Comparative Study of Customer Churn Prediction Based on Data...

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2023 Intelligent Computing and control for engineering and Business Systems, ICCEBS 2023

作者： Kumar, S.Dinesh Soundarapandiyan, K. Meera, S. Sri Sairam Engineering College Department of Management Studies Tamil Nadu Chennai India B.S.Abdur Rahman Crescent Institute of Science & Technology Dept. of Commerce Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394580

Customer Churn is the prima facie problem of any company irrespective of their size and also it has a direct influence on the revenue of the companies. So this makes every company to understand the rationale behind the customer churn and predict the potential customer churn and the reasons for the same. By identifying the factors of churn, the companies will be in adept.position to retain the customers. Thence the main intention of this work is to build-up a churn prediction model using machine learning that assists the companies to predict the customers who are most likely to churn. The models were measured by adopting Area Under Curve (AUC) measure, the confusion matrix (Accuracy, Recall, Precision) and F1 Score. The ensemble modeling was also adopted in order to compare and consolidate the each models for a common outcome. The models were run through Python (Jupyter Notebook) environment on the big dataset obtained from Kaggle. This research tried out with following model algorithms: Naive Bayes Model, Discriminant Analysis, Decision Tree, Random Forest, and Logistic Regression and further to attest the outcome of the data ensemble modeling of Ada boost, Gradient Boosting and Bagging has also been used. Out of all the models Random Forest model was considered to be the best fit model due to high accuracy, specificity, AUC and FI Score. So this model can be used as a classification churn prediction model. © 2023 IEEE.

关键词： Customer Churn Decision Tree Discriminant Analysis Ensemble modeling Logistic Regression ML algorithms Naive Bayes Random Forest

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Md Shahidur Rahman Asif Ahamed Md Nuruzzaman Pranto Md Ariful Islam Abdullah Al Masum Abdullah Al-Sakib S M Arafat Rahman Rezaul Haque Shafiur Rahman Dept. of Engineering Management Westcliff University CA USA College of Technology and Engineering Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health.

关键词： Deep learning Accuracy Animals Biological system modeling Transfer learning Feature extraction Real-time systems Chemicals Diseases Farming

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems.

关键词： Wireless communication Fading channels Machine learning algorithms Transfer learning Training data Predictive models Multitasking Data models Few shot learning Channel state information

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems.

关键词： Deep learning Fading channels Adaptation models Attention mechanisms Wireless networks Transfer learning Predictive models Data models Kalman filters Long short term memory

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Targeting different defect-oriented fault models in IC testing: an experimental approach

Targeting different defect-oriented fault models in IC testi...

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Euromicro Symposium on Digital System Design

作者： N. Mirabella A. Floridia R. Cantoro M. Grosso M. Sonza Reorda STMicroelearonics s.r.l. Italy Dept. of Control and Computer Engineering Politecnico di Torino Italy

In the field of integrated circuit (IC) testing, the detection of defects is crucial to ensure the reliability and functionality of the final product. Among the variety of fault models that can be used to target the many possible defects in a circuit, delay faults (transition and path delay) have been used for many years. Lately, cell-aware testing (CAT) has been introduced as a different approach that aims to improve the detection of internal defects of standard cells: it involves using specific patterns to detect faults that could not be detected by common fault models (e.g., stuck-at and transition delay fault models). Both delay and cell-aware faults can be caused by several factors, such as manufacturing defects, environmental conditions, and aging effects. In this paper, we investigate the application of test patterns generated with the transition and path delay fault models in comparison with others developed with the cell-aware approach, in terms of fault coverage, pattern count and test generation time. Overall, the study shows that the combination of the path delay fault model and cell-aware testing can lead to improved fault coverage and lower test. The experimental results are presented over a wide range of open-source benchmarks and on a RISC-V design using a proprietary industrial technology library.

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Lightweight Direct Memory Access on FPGA Using AXI Protocol

Lightweight Direct Memory Access on FPGA Using AXI Protocol

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Adam Kidwell Matthew P. Horvath Darrin Hanna Bryant Jones Dept. of Electrical and Computer Engineering Oakland University Rochester Hills MI Intrepid Control Systems Troy MI

This paper presents a lightweight AXI DMA controller architecture useful for embedded systems that do not require fully featured DMA controllers. Simulation is accomplished with VUnit, and implementation results are obtained on a Xilinx XC7Z010CLG400-1 FPGA. When compared with Xilinx's AXI DMA controller with the same configuration, the presented controller utilizes between 16 and 82% fewer resources with comparable speed.

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Essential Features Contributing to the Success of a Food-Tech Startup

Essential Features Contributing to the Success of a Food-Tec...

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Technology & engineering management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Ajit Kumar Pasayat Jayshree Patnaik School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Dept. of Entrepreneurship and Management Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (数字)9798331515881

ISBN: (纸本)9798331515898

The emergence of startups has become a significant trend in a country's economic landscape. Among startups, notably, the domain of food technology (food-tech) startups has achieved remarkable traction within a brief span. It's critical to examine the analytics-driven elements that contribute to the success or failure of a food-tech firm. However, current literature on startup analytics has primarily focused on other startup categories and has subjectively investigated the predictive behavior of these factors. This research implemented Ant Colony Optimization and the Chi-Square test for feature selection among a wide range of features from food tech startups in India. The features selected by our proposed framework were then used to train popular machine learning classification models to predict success. The key features identified highlight how entrepreneurs prioritize factors for food tech startup success and maximize socio-economic benefits. These insights help reduce uncertainty, enhance performance, and assist founders, investors, and researchers in planning future strategies effectively.

关键词： Uncertainty Biological system modeling Food technology Machine learning Evolutionary computation Predictive models Market research Feature extraction Planning Socioeconomics

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