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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. Data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital technology Drainage Monitoring Fire detection Smart City Waste Management

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Voice-Controlled Navigation for TurtleBot3 using ROS Noetic and Speech Recognition 15

Voice-Controlled Navigation for TurtleBot3 using ROS Noetic ...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Kasno, Mohammad Afif Yatim, Norhidayah Mohamad Kang, Tae-Won Hee, Jo Min Nam, JunWoo Jung, Jin-Woo Dongguk University Dept. of Computer Science and Egineering Seoul Korea Republic of Faculty of Electronics and Computer Technology and Engineering Malacca Malaysia Dongguk University Dept. of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350364637

This study presents a unique approach for operating the TurtleBot3 Waffle-Pi robot with voice instructions using the ROS Noetic framework. The system uses the SpeechRecognition library to translate voice inputs into movement commands, with the goal of improving user engagement and providing an intuitive control mechanism that can be applied to a variety of domains including assistive robotics. The system naturally interfaces with ROS Noetic, which uses Python for scripting and control logic. The method entails real-time recording audio with a microphone, filtering it to remove noise, then interpreting commands with Google's speech recognition API. For the TurtleBot3, commands like 'forward,' 'back,' 'left,' 'right,' and 'stop' are assigned to matching movements. Noise filtering and command disambiguation problems are solved by modifying the recognizer's energy threshold and using a dynamic energy threshold. A complete test was carried out in controlled situations to determine command recognition accuracy, response time, and navigation precision. The results showed good reliability and effectiveness, with accurate command recognition and minimum reaction delay. © 2024 IEEE.

关键词： Speech recognition

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A Comparative Analysis for Stroke Risk Prediction Using Machine Learning Algorithms and Convolutional Neural Network Model 3

A Comparative Analysis for Stroke Risk Prediction Using Mach...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Ferdous, Most. Jannatul Shahriyar, Rifat Bangladesh University of Business and Technology Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

A critical, sometimes fatal medical disease called a stroke happens when the blood flow to a portion of the brain is broken off. In the case of stroke, urgent treatment is very essential. Nowadays, stroke is the main cause of death and impairment globally, according to WHO. In this situation, it will be very helpful if we predict the probability of stroke earlier depending on some most important features. Many researchers use different machine learning algorithms for prediction but very few researchers use stacking methods and CNN. The main contribution of this paper is to develop a stacking classifier of ensemble methods and the CNN model. In this paper, data-set is collected from Kaggle. Stroke data is imbalanced. Random oversampling is used for balancing data-set. Then most important features are find out using feature selection method, then applying different machine learning algorithms such as Logistic Regression, Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbour's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Stacking of six algorithms (Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbor's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes) and CNN. Then comparing the performances for predicting the probability of stroke during both the training and testing periods. Results show that the Stacking of six algorithms gives the highest accuracy, which is 99.89% for testing and 100% for training. © 2023 IEEE.

关键词： Convolutional neural networks

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Approaches to Predict Post Covid-19 Severity: A Detail Case Study

Machine Learning Approaches to Predict Post Covid-19 Severit...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Mathur, Richa Dutta, Ritam Bansal, Payal Kumar, Rajeev Poornima University Dept. of Computer Scinece & Engineering Rajasthan Jaipur303905 India Rajasthan Jaipur302022 India Poornima Institute of Engineering & Technology Dept. of Electronics & Comm. Engineering Rajasthan Jaipur302022 India Moradabad Institute of Technology Dept. of Computer Science & Engineering Uttar Pradesh Moradabad244001 India

ISBN: (纸本)9798350378047

The entire world has witnessed a horrific pandemic in last three years, whose long term and short-term impact to human body has been observed in large scale in these post covid-19 years. Machine learning (ML) techniques have been successfully implemented in medical field since last one decade. The emergence of post covid-19 pandemic symptoms are compelling researchers to re-search for minute detection and predict patient's probable infection rate. Long lasting chest pain, breathing issues, liver and kidney dialysis issues have been apparently visible as post covid-19 symptoms. In this paper, the journals regarding the prediction of covid-19 severity based on symptoms have been discussed. Several researchers have established deep learning-based models for the prediction. They have used the test data from the hospitals for their research process. The KNN models, ANN models, and SVM models have been discussed. The limitations of the past research process have been evaluated to be the biased test data set and missing values. The methodology that was used for the research process has been described. Mixed method of data collection was used for the research processes. The secondary data was collected from reliable sources for this research process. Considerable amount of test data has been retrieved from Kaggle database regarding post covid-19 patient's symptoms. The data analysis is performed on the Weka tool using various machine learning models. © 2024 IEEE.

关键词： COVID-19

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Automatically Suggesting Suitable Crop from Soil Samples Using Optimized Neural Networks 3

Automatically Suggesting Suitable Crop from Soil Samples Usi...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Pasha, Syed Nawaz Ramesh, D. Mohmmad, Sallauddin Deepak, Gujjula School of Computer Science and Artificial Intelligence Sr University Warangal India Kamala Institute of Technology and Science Dept. of Computer Science and Engineering Huzurabad India

ISBN: (纸本)9798350381931

A soil-centric crop recommendation system recommends suitable crops utilizing vital soil attributes as input factors. This sophisticated system incorporates parameters like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, humidity, pH, and rainfall to suggest ideal crops for cultivation. The dataset comprises diverse crops and their specific requirements for each input parameter. We implemented an optimized neural network to provide precise crop recommendations based on the provided input values. By analyzing this data, the system provides farmers with the information necessary to make well-informed decisions regarding their choice of crops, enhancing the potential for optimal yields. Our model performed well on training and testing data with an accuracy of 0.94. These results demonstrate its effectiveness and potential as a valuable tool for agricultural decision-making. This system offers farmers tailored recommendations based on soil properties, climatic conditions, and other environmental factors. © 2024 IEEE.

关键词： Crops

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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and Network Security Measures 5

Explore the Impact of Quantum Computing to Enhance Cryptogra...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Periyasamy, P.N. Kamal, N. E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Hindusthan Institute of Technology Department of Computer Science and Engineering India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383522

The revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults;specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. These applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. The detection accuracy of Quantum-Enhanced Intrusion Detection Systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. The detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms. © 2024 IEEE.

关键词： cryptographic protocol data security quantum key distribution quantum-enhanced intrusion detection

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Robust Facial Emotion Recognition System via De-Pooling Feature Enhancement and Weighted Exponential Moving Average

Robust Facial Emotion Recognition System via De-Pooling Feat...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Win, Khin Cho Akhtar, Zahid Mohan, C. Krishna Indian Institute of Technology Dept. of Artificial Intelligence Hyderabad India State University of New York Polytechnic Institute Dept. of Network and Computer Security UticaNY United States Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350350821

Facial expression recognition (FER) in an uncontrolled environment presents a formidable challenge in affective computing and human-machine interaction domains. Existing FER models fail to generalize due to the innate expression nature of the intra-class separability and inter-class compactness. Although most state-of-the-art FER research focuses on more advanced frameworks and well-separated discriminative loss functions, further exploration is needed to obtain high-quality expression features. This paper proposes a robust feature enhancement approach for FER by integrating the De-pooling Feature Enhancement and Weighted Exponential Moving Average (WEMA) of Stochastic Gradient Descent (SGD). The proposed method utilizes De-Pooling Feature Enhancement to capture and enhance the high-quality expression features, while WEMA of SGD optimizes the training process for improved stability and convergence. Our extensive experimental analysis of benchmark datasets signifies that our proposed method prevails over state-of-the-art methods, achieving superior accuracy performances of 86.43% on FER2013, 94.97% on FERPlus, 94.71% on RAF-DB, as well as 80.62% and 72.90% on AffectNet, of 7 class and 8 class, respectively. © 2024 IEEE.

关键词： Stochastic systems

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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

NeuroNet: An Attention-Driven Lightweight Deep Learning Mode...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Istyak Nabil, Hadiur Rahman Abir, Golam Rabbani Hossain, Tazdik Das, Aritra Mridha, M.F. American International University-Bangladesh Dept. of Computer Science Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI Dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet's architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet's potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging. © 2024 IEEE.

关键词： Diseases

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