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检索条件"机构=Microelectronics in the Electronics and Computer Engineering ECE Department"

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Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, V. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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IOT Based Smart Home Automation Using ESP32

IOT Based Smart Home Automation Using ESP32

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International Conference on computer and Communications Technologies (ICCCT)

作者： K Mariammal R Arunadevi J Lurdhumary R Ramya M S Vinotheni YuvikaaShree S Swathi D R Department of Electronics Engineering Madras Institute of Technology Anna University Chennai India Department of Computer Science & Business System Sri Sairam Engineering College Chennai India Department of ECE Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

This paper presents the design and implementation of an IoT-enabled smart home automation system utilizing the ESP32 microcontroller to enhance home management through real-time remote control. The proposed system integrates various sensors, including temperature, humidity, motion (PIR), light, and raindrop sensors, to automate tasks based on environmental conditions, thereby improving energy efficiency and convenience. Additionally, the system incorporates an RFID-based door locking mechanism for enhanced security and supports voice commands for hands-free operation, catering to individuals with mobility limitations. A web interface allows users to monitor and control connected devices remotely via Wi-Fi, offering a seamless and user-friendly experience. Emphasis is placed on secure communication protocols to safeguard user data, while a modular architecture ensures scalability for future enhancements. This solution demonstrates the potential of IoT technologies to create energy-efficient, secure, and accessible smart home systems, addressing modern demands for sustainable living.

关键词： Temperature sensors Temperature measurement Automation Smart homes Speech recognition Energy efficiency Sensor systems Real-time systems Intelligent sensors Wireless fidelity

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Enhanced CNN Security based on Adversarial FGSM Attack Learning: Medical Image Classification

Enhanced CNN Security based on Adversarial FGSM Attack Learn...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Lazhar Khriji Seifeddine Messaoud Soulef Bouaafia Ahmed Chiheb Ammari Mohsen Machhout Electrical & Computer Engineering Department Sultan Qaboos University Sultanate of Oman Laboratory of Electronics & Microelectronics Monastir University Monastir Tunisia

Convolutional Neural Networks (CNNs) have grown in popularity for clinical image processing applications like as Covid and cancer detection. A new study, however, shows that hostile attacks with modest, unnoticeable disruptions can damage deep healthcare learning systems. This creates safety issues about using these technologies in healthcare situations. In this study, we will look at the approaches used to fight adversarial attacks on medical imaging. Next, we intend to investigate the resilience of pre-trained CNN architectures, as well as LeNet5 and MobileNetV1 models against Fast Gradient Sign Method (FGSM) attacks in a medical healthcare application-based chest X-ray dataset. We discover that pre-trained CNN models are much more sensitive to antagonistic assaults than other models, due to key feature discrepancies between them and regular models. Finally, we propose to improve the CNN’ models security by investigating adversarial training. According to the numerical results, models with lower computational complexity and restricted layers are much more safe against malicious attacks than bigger models which are commonly utilized in medical healthcare systems.

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Integration of A-I in implementation of Wire-less Webbing: A detailed Review

Integration of A-I in implementation of Wire-less Webbing: A...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kapula, Prabhakara Rao Pant, Bhasker Kanwer, Budesh Buddhi, Dharam Sagar, K.V. Daya Sinthu, S. B. V. Raju Institute of Technology Department of Ece Telangana Narsapur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Poornima Institute of Engineering and Technology India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Electronics and Computer Science Koneru LakshmA-Iah Education Foundation Vaddeswaram Andhrapradesh Guntur India Thalavapalayam Karur India

ISBN: (纸本)9798350322309

Mobile computing and Internet of Things (IoT) webbing s have been rapidly deployed in recent years, which is mostly due to the growing communication and sensing abilities of wire-less gadgets. The IoT is expected to be used as the foundation for the deployment of big specifics analysis, ubiquitous computing, and ultimately artificial reconnaissance (A-I), which will usher in a new era of specifics-driven A-I. In this context, Wire-less A-I, a revolutionary paradigm for fusing A-I as well as wire-less communications, is widely recognised as a critical enabler for future intelligent webbing growth. Wireless A-I pushes A-I frontiers to the webbing edge. In order to achieve this, we give a thorough analysis of the most recent wireless A-I projects from a specifics-driven approach. In particular, we initially suggest an unique Wire-less A-I architecture that addresses There are five mA-In specifics-driven A-I themes in wire-less webbing s: sensing, access, user gadgets, and specifics provenance. Then, we give an overview of how A-I techniques are used to address new specifics-related problems and demonstrate how A-I can enhance wire-less kind of sytem netwrk functionality for each specifics-driven A-I theme. We present an in-depth discussion on the Wire-less A-I applications in many specifics-driven sectors where A-I appears to be incredibly helpful for wire-less kind of sytem netwrk design and optimization, especially in comparison to the other related survey articles. To encourage more research in this promising field, problems for future research and future perspectives are also explored. © 2023 IEEE.

关键词： Wire

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Big Data Analytics with Deep Learning based Intracranial Haemorrhage Diagnosis and Classification Model

Big Data Analytics with Deep Learning based Intracranial Hae...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Munirathinam, T. Upadhyay, Shrikant Babitha Lincy, R. Jency Rubia, J. Beaulah Jeyavathana, R. Gopatoti, Anandbabu Bannari Amman Institute of Technology Department of Computer Science and Engineering India ECE Cambridge Institute of Technology Ranchi835103 India Sri Eshwar College of Engineering Kinathukadavu CCE Department Coimbatore India K. Ramakrishnan College of Engineering Samayapurarn Department of ECE Tiruchirappalli621112 India SRM Institute of Science and Technology Department of Computational Intelligence India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665489621

Owing to the rapid growth of medical imaging technologies, medical image analysis is entered the period of big data for proper diagnosis of diseases. Intracranial hemorrhage (ICH) means a pathological disorder that requires quick decision making and diagnosis. Computed tomography (CT) can be accurate and extremely dependable diagnosis method for detecting hemorrhages. Automatic recognition of ICH from CT scans by using a computer-aided diagnosis (CAD) method was helpful in detecting and classifying various grades of ICH. Due to the recent development of deep learning (DL) methods in image processing applications, numerous medical imaging approaches use it. This article introduces innovative Big Data Analytics with Deep Learning based Automated Brain Intracranial Haemorrhage Diagnosis and Classification Model (BDDL-IHDCM). The presented BDDL-IHDCM model examines the computed tomography (CT) scans for detecting and classifying ICH. To accomplish this, the presented BDDL-IHDCM model applies bilateral filtering (BF) technique for noise elimination procedure. The BDDL-IHDCM method employs neural architectural search network (NASNet) feature extractor with Bayesian optimization (BO) based hyperparameter tuning. For ICH detection and classification, a grey wolf optimizer (GWO) with auto encoder (AE) model is utilized in this study. To handle big data, Hadoop MapReduce will be used. To exhibit the significant performance of the presented BDDL-IHDCM method, a comprehensive simulation analysis is made by making use of benchmark dataset. The experimental outcomes indicate the betterment of the presented BDDL-IHDCM method to other DL models with maximum accuracy of 96.22%. © 2022 IEEE.

关键词： computer aided diagnosis

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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Global Identification Passport: A Unique Cloud based Passport Model

Global Identification Passport: A Unique Cloud based Passpor...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： A Ravi Raja T Anuradha Sankararao Majji Tulasi Radhika Patnala C Sushama Dhiraj Kapila ECE department Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Electrical and Electronics Engineering KCG College of Technology Chennai India Department of ECE GRIET Hyderabad India Dept of ECE GITAM Universirty Hyderbad India Department of Computer Science and Engineering School of Computing Mohan Babu University Vijayawada India Department of Computer Science and Engineering Lovely Professional University Phagwara India

DDoS attacks pose a serious risk to cloud networks because of the money they might cost if they succeed. For this reason, the Security Research Group has been hard at work creating various detection methods to lessen the blow of any future cyberattacks. Cloud computing, often known as internet computing, is a model of delivering computing as a service, in which shared, elastically scalable and virtually hosted servers are made available to users anywhere there is an internet connection. Numerous companies provide cloud services online to meet the needs of various clientele. Since most cloud users lack their own computing capabilities, they must save their data, documents, and applications in the cloud in order to perform their work efficiently. The focus of this work is on the worldwide identification procedure utilising a digital passport in the cloud. There has been and will continue to be significant growth in cloud-based innovation and advancement in the coming years. Users can benefit greatly from cloud computing by delegating complex activities to remote data centers. Whether or not consumers can access the cloud’s services is determined by the terms of the Service Level Agreement (SLA) in place between the cloud’s suppliers and the users. By detecting when a service level agreement (SLA) has been broken, cloud service providers may boost customer confidence, decrease the likelihood of incurring costly fines, and boost their bottom line. This study makes two significant contributions to the fields of service provider selection for virtual machine (VM) migrations and SLA (service level agreement) violation detection in public cloud computing. The pros and cons of a passport-free international travel system were examined. This study has compared various research methodologies and frameworks for implementing new technologies. The present study is predicated on a Cloud Passport adoption model that was developed for previous work.

关键词： Cloud computing Technological innovation Computational modeling Terrorism Logic gates Virtual machining Safety

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Cardiac Vascular Disease Prediction Using Stacked Ensemble Algorithm for Type-2 Diabetes Mellitus

Cardiac Vascular Disease Prediction Using Stacked Ensemble A...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： C. Selvarathi Manikandan. T G. Madhu Mitha Sharmila S Sengolrajan Thanasingh P. Pandiaraja Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of ECE Vivekanandha College of Engineering for Women (Autonomous) Thiruchengode Tamilnadu India Department of Electrical and Electronics Engineering Vivekanandha College of Engineering for Women Thiruchengode Tamilnadu India Department of ECE Vivekanandha College of Engineering for Women Thiruchengode Tamilnadu India Department of Electrical and Electronics and Engineering Kongunadu college of Engineering and Technology (Autonomous) Trichy Tamilnadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Cardiac vascular disease (CVD) is a most serious disease associated with highest humanity and complaint rates in the global population. The risk of developing CVD consciously increases in patients having long-term diabetes difficulties. Consequently, it is necessary to predict the association between CVD and type-2 Diabetes Mellitus (T2DM) for effective decision making to initiate appropriate treatment of T2DM patients. First, the proposed research work to examine the purpose of machine learning (ML) techniques to predict the risk of death rate in CVD patients with T2DM. Then the proposed method is mainly focusing to predict the survival rate of regularity of CVD patient's incidence with T2DM. However, the dataset is unbalanced and handling such unbalanced dataset is one of the major challenges addressed using cost sensitive learning algorithms for reducing the misclassification rate. This proposed method stacked ensemble with cost-sensitive learning that combines the prediction results of the three base classifiers to predict the survival rate of patients with cardiovascular disease and T2DM. The proposed stacked ensemble algorithm uses a 10 fold cross validation method with network search optimization to tune the hyper-parameter of the meta classifier model. The proposed algorithm is used to measure the performance of dataset being evaluated by splitting the data into 80% training and 20% testing. The analysis of outcomes displays that the survival rate of heart failure patients is better predicted using the cost-sensitive stacked ensemble learning technique to achieves precision of 93% and higher accuracy. The unbalanced class and cost sensitive learning may help to address the class imbalance issue. The incorrect weight assignment may lead to overfitting to the minority class while underperforming on the majority class.

关键词： Training Costs Accuracy Predictive models Prediction algorithms Classification algorithms Diabetes Ensemble learning Cardiovascular diseases Cancer

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Design of Cloud-Powered Type of Strategies for Management System Used in Drones

Design of Cloud-Powered Type of Strategies for Management Sy...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： A S Valarmathy Tushar Bhavsar E. Arunkumar Saloni Bansal R. Velmurugan A. Vijayaprabhu Department of Electrical and Electronics Engineering (EEE) Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Mechanical Engineering (me) IES College of Technology Bhopal M.P India Department of Electronics and Communication Engineering (ECE) Karpagam College of Engineering TN Department of Computer Engineering and Applications (CEA) GLA University Mathura Karpagam Academy of Higher Education Eachanari Coimbatore India Department of ECE Siddharth institute of Engineering and Technology Puttur Andhrapradesh India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Drones, sometimes referred to as drones that fly (UAVs), are becoming more and more popular as affordable solutions that may support creative business and civic applications in the Internet connected Things (IoT). Even while there is increasing excitement about their potential, there are still intrinsic difficulties that prevent their real-world use. The most significant of these difficulties is the restricted wireless communication range that drones have with ground stations, which limits their ability to effectively control across long distances. Furthermore, low-cost aerial platforms’ limited computing and energy resources make it difficult for them to run resource-intensive programs aboard. Our solution to these limitations is the Dronemap Organizer (DP), a cloud-based drone management platform that is service-oriented and intended to monitor, operate, and interact with drones over the Internet in a smooth and efficient manner. Numerous applications and proof-of-concepts created using DP’s architecture serve as examples of its adaptability. A thorough performance assessment research was carried out using an actual drone in a live monitoring application in order to gauge the effectiveness of DP. The study’s findings support DP’s promise for practical applications by highlighting how well it works to overcome issues with constrained processing power and communication range. In summary, the Dronemap Planner stands out as a ground-breaking solution in the field of UAV administration, especially when it comes to resolving the issues preventing the broad use of inexpensive drones. DP is a monument to the way drone technology is developing and how it is being incorporated into the wider Web of Things ecosystem by smoothly integrating computing capabilities and facilitating communications over the Internet without being limited by geography. By using this creative approach, DP not only demonstrates that it is possible to overcome current obstacles, but it also creates n

关键词： Wireless communication Cloud computing Transportation Autonomous aerial vehicles Safety Security Internet of Things

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Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things

Secure Smart Healthcare Surveillance Framework Using Fuzzy C...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Karuppaiah, Jayakumar Aarthi, C. Girirajan Jananai, M. Parameshachari, D.B. Biomedical Engineering Kalaigar Karunanidhi Institute of Technology Coimbatore India Sengunthar Engineering College Thiruchengode India Sr University Department of ECE Warangal India Kpr Instiute of Engineering and Technology Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

The emergence of IoT device networks is not limited to a specific industry, application, or environment;instead, they are assisting in the development of the country in a number of sectors, including industries, cities, agriculture, and defense. But IoT device networks have some restrictions, including those related to bandwidth, energy resources, and other resources, as well as restrictions based on the environment. The fast growth of the Internet of Things (IoT) has brought to light significant privacy protection problems. This has a tremendous impact on the large-scale IoT applications. In this study, a ground-breaking IOT computing platform for secure and knowledgeable healthcare monitoring services is introduced. Within an IoT architecture, fully homomorphism encryption (FHE), which protects data privacy, is handled and kept. For the proposed IoT framework, a distributed method for clustering-based approaches is created with the scalability to gather and independently analyse massive amounts of hetrogeneous data in distributed IoT devices before it is transferred to the cloud. In this study, the Cluster Heads (CHs) were chosen from the available IoT devices using a brand-new Efficient Ant Colony Optimization (EACO) technique. For healthcare surveillance, fuzzy C-Means clustering (FCMC) is employed. FCM allows us to recognise regular (typical) bio signal patterns. © 2022 IEEE.

关键词： Internet of things

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