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Optimizing Urban Mobility: A Comprehensive Framework for Smart Parking Systems Integration Using IoT and Sensor Technologies

Optimizing Urban Mobility: A Comprehensive Framework for Sma...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Sayak Sen Unmesh Mukherjee Subhabrata Banerjee Aparna Biswas Sutapa Ray Sayantani Chowdhuri Dept. of Electronics and Communication Engineering Institute of Engineering & Management Kolkata India Department of Computer Science & Engineering Institute of Engineering & Management Kolkata India

Smart Parking Systems have gained prominence as a pivotal component of urban infrastructure, addressing the persistent challenge of limited parking spaces in burgeoning cities. This abstract delves into the evolution and functionality of Smart Parking Systems. Leveraging technology such as microcontroller, sensors, IoT, and data analytics, these systems optimize parking space utilization and enhance the overall parking experience. By providing real-time information to drivers, reducing traffic congestion, and minimizing environmental impact, they contribute to more sustainable urban development. This paper explores the various components and benefits of smart parking systems, showcasing their potential to revolutionize urban mobility and improve quality of life. As cities continue to grow, smart parking systems offer a promising solution to the parking conundrum, fostering a smarter, more efficient, and eco-friendly urban environment.

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Quantum Inspired Zeroshot Classification For Adverse Drug Reactions Detection From Social Media Reviews

Quantum Inspired Zeroshot Classification For Adverse Drug Re...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Arijit Dey Jitendra Nath Shrivastava Dept. of Computer Applications B. P. Poddar Institute of Management and Technology Kolkata WB India Dept. of Computer Science and Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

A deep learning model enhanced with quantum transformer has been introduced in detection of adverse reactions of drugs within the reviews collected from social media written in English language. This proposed model enhanced the human language understanding for detecting adverse reactions of drug. Quantum transformers are used for position embedding and attention purpose. Variational quantum circuit encoded the human language and identify the relevant components of the input reviews as class feature vectors to develop a relationship network for each drug correspond to the condition and review comments on which probability distribution functions applied to detect ADRs found in a medicinal product or not. The correctness of advanced model architecture built on quantum transformer is quite encouraging, and it performs better than other cutting-edge learning models. Achieved an accuracy of 93% with a loss of 0.0393 with this proposed model. Though it is too early to declare that quantum computing has completely replaced. This proposed quantum enhanced transformer helps to show the direction towards quantum enhanced hybrid model design in research.

关键词： Drugs Quantum algorithm Reviews Social networking (online) Computational modeling Transformers Natural language processing Vectors Quantum circuit Integrated circuit modeling

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A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning 3

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Jagendra Dabas, Partibha Bhati, Sunita Kumar, Santosh Upreti, Kamal Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India Dr Akhilesh das Gupta Institute of Technology and Management Dept. of Computer Science & Engineering Delhi India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India Department of Computer Science Ghaziabad India SRIT-Autonomous Anantapur Department of Computer Science Engineering India

ISBN: (纸本)9798350342338

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. © 2023 IEEE.

关键词： Cloud Data Centers Deep Reinforcement Learning Edge Computing Energy Efficiency Smart Surveillance

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers 1

An Effective Classification of Ddos Cloud Based Attack Throu...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Goel, Pragati Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Delhi-NCR Dept. of IT Ghaziabad India NCRD'S Sterling Institute of Management Studies Nerul Dept. of Computer Application Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Ch. Charan Singh University Campus Dept. of CS&E Meerut India

ISBN: (纸本)9798350345957

The term 'cloud computing' refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process. © 2023 IEEE.

关键词： Decision trees

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Effectiveness of computer-based e-Learning and Virtual Reality Training System for the Preparatory Phase of a Training on Maintenance Procedure

Effectiveness of Computer-based e-Learning and Virtual Reali...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Filippo Gabriele Pratticò Debora Di Cosmo Matteo Piviotti Giuseppe La Rosa Fabrizio Lamberti Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy Leonardo S.p.a Turin Italy

This work investigates the effectiveness of two training systems based on consumer hardware technologies, a first one using computer-Based Learning (CBL) and the other exploiting Virtual Reality (VR). A user study was executed in order to compare the two training and analyze the most suitable approach for the learning of preparatory material in the context of an industrial assembly and maintenance (IMA) procedure. The results highlighted that, although trainees using VR experienced higher levels of cognitive processing and attention, the knowledge gain of CBL was comparable to that of VR for the preliminary phase training. Nonetheless, VR was still able to provide better learning gains in terms of procedural skills compared to CBL.

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Digital Medicine Assistant-Health Application for Expiry Tracking

Digital Medicine Assistant-Health Application for Expiry Tra...

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2021 IEEE International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications, CENTCON 2021

作者： Bhat, Sharanya S. Shraddha, K. Bhavana, A.S. Rao, Shreyas Suresh Sahyadri College of Engineering and Management Dept. of Computer Science and Engineering Karnataka Mangalore India

ISBN: (纸本)9781665400176

Medicines play an important part in each and everyone's life where some of them relieve us from diseases, disorders or pains and some of them are very essential lifesaving ones. Some of them are rarely available and need to be priorly ordered in remote places and also some are very ex-pensive. One of the most common problems is that we all tend to miss the expiry of our medicines or sometimes the strips get ripped off and the information related to the tablet or the medicine becomes unavailable. Most of us spend our time writing down the same in notepads and waste time in labelling the medicines. For maintaining appropriate dosage infor-mation of the medicines, many try to organize their prescriptions but there are always chances of losing them. Notepads, keynotes and alarm reminders may be an easy solution that strikes to us at the first thought but is not efficient. There is no way of getting reminded about the expiry of any of the medi-cines that's written in a notepad. Many of the conventional apps provide a feature to get reminded about the medicines to be taken daily or provide a one-time notification for the expiry of the medicines that are in its entry which the users may clear off from the mobile's notification panel as a result of having a busy day. Also, most available applications provide an expiry tracking feature more in general than only for medicines. This paper is about an application that provides more convenient and effective solution to the problem and to the best of our knowledge is the first application designed to remind its user through reminder notifications about the expired and about to expire medicines. © 2021 IEEE.

关键词： application dosage expensive expiry medicines notepads reminder notifications remote places

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Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review

Importance of Machine Learning and Deep Learning Algorithms ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Engineering and Natural Sciences Maltepe University İstanbul Turkey Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Chattanooga Chattanooga TN US

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors.

关键词： Deep learning Machine learning algorithms Earthquakes Radon Neural networks Estimation Prediction methods

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Adaptive control Using a Quaternion Wavelet Neural Network

Adaptive Control Using a Quaternion Wavelet Neural Network

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International Workshop on Robot Motion and control (RoMoCo)

作者： Gerardo Martínez-Terán Eduardo Bayro-Corrochano Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico Institute of Automatic Control and Robotics Poznan University of Technology

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

Robotics and haptic systems have allowed new and diverse applications in the field of medicine, such as assisted surgery and teleoperation which have increasingly stringent requirements for accuracy, convergence, and low computational consumption. In this paper an adaptive PID control law (Proportional Integral Derivative controller, PID), of indirect architecture is presented for movement paths in a haptic system of open chain, where the identification of the plant is through a quaternionic wavelet neural network (Quaternion Wavelet Neural Network, QWNN) for tune the PID values, this allows the optimal movement into the regions of the workspace.

关键词： Uncertainty Quaternions Neural networks Surgery Robustness Haptic interfaces Topology

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Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

Systematic Literature Review: Evaluating Effects of Adversar...

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Energy, Power, Environment, control, and Computing (ICEPECC), International Conference on

作者： Saira Akram Sibghat Ullah Bazai Muhammad Imran Ghafoor Shah Marjan Muhammad Hamza Syed Ali Asghar Shah Dept. of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Computer Science Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in such a way that they would be able to take decisions spontaneously, however on the other side attackers attempt to manipulate the results generated by these models which makes the application of these models difficult in security-critical areas including classification of medical images, autonomous system installed in vehicles, street lights, malware detection and identification of a person as criminal or innocent. Advancement in research proved the vulnerability of these classifiers due to adversarial attacks that can alter their result in training as well as in testing phase of the model. Causative attacks are the training phase attacks whereas the attacks done at testing phase are the exploratory attacks. This systematic literature Review (SLR) is conducted to gain in-dept. knowledge of adversarial attacks, which is the most effective type of exploratory attack, parameters on which these attacks are based, along with the most conventional methods of generating adversarial attacks.

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