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An AI-Centred Adaptive Cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information system (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring system (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Drowsiness detection system using the movement of the eye of a human through machine learning algorithms in order to avoid accidents 3

Drowsiness detection system using the movement of the eye of...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jothi Mary Jenifer, A. Goswami, Subhashish Rahul, B.G. Sakthika, T. Mahajan, Dewank Pant, Bhasker Affiliated to Bharathidasan University Department Computer Science Tiruchirappalli India Dev Bhoomi Uttarakhand University Department of Computer Science Uttarakhand Dehradun India Koneru Lakshmaiah Educational Foundation Department of Civil Engineering India Zoology A.P.C. Mahalaxmi College for Women Thoothukudi India University of Michigan Data Analyst United States Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

This function is meant to keep individuals safe in the event of an accident. In this study, the development of ADAS is shown, with a focus on driver fatigue identification as the main goal of decreasing traffic fatalities. In this situation, we use artificial intelligence to anticipate the condition and sentiments of the operator, improving traffic safety. For this research, we'll use CNN, which can accurately detect driver fatigue from driving images. In this study, we can employ the Electrocardiographic to measure weariness psychologically. Artificial intelligence refers to a system's ability to learn and grow without having to be programmed. We also employ OpenCv, Keras, and Tensorflow, among other machine learning algorithms [1].Road accidents are rising quickly each year as a result of vehicle technology and mechanical developments that allow for fast driving. Road accidents claim the lives of 1.35 million people worldwide every year, with 151 thousand fatalities reported in India alone in 2016. From this, drivers are at fault for almost 78 percent of traffic incidents. Drunk driving, over speeding, and sleepiness are the main causes of these incidents, with drowsiness accounting for roughly 40% of all *** are aware of the dangers of driving after drinking, but they are not aware of the dangers of tiredness since there are no tools available to gauge driver *** a motorist is distracted while driving, it slows down their response time and affects how they steer. In order to address this issue, we'll employ machine learning to determine whether or not the driver is sleepy [2]. By setting a threshold number, we can determine if the driver is tired or not. Typically, when someone feels sleepy, the rate at which his or her eyes blink slows *** application offers a method for scanning facial features and, after recognising the face, utilising those crucial cues for eye tracking. This guarantees that the driver has complete control over his

关键词： Electrocardiograms

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A Food Package Recognition and Sorting system Based on Structured Light and Deep Learning 3

A Food Package Recognition and Sorting System Based on Struc...

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3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

作者： Liu, Xuanzhi Liang, Jixin Ye, Yuping Song, Zhan Zhao, Juan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong University of Finance & Economics China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9798400707704

Vision algorithm-based robotic arm grasping system is one of the robotic arm systems that can be applied to a wide range of scenarios. It uses algorithms to automatically identify the location of the target and guide the robotic arm to grasp it, which has more flexible features than the teachable robotic arm grasping system. However, for some food packages, their transparent packages or reflective materials bring challenges to the recognition of vision algorithms, and traditional vision algorithms cannot achieve high accuracy for these packages. In addition, in the process of robotic arm grasping, the positioning on the z-axis height still requires manual setting of parameters, which may cause errors. Based on the above two problems, we designed a sorting system for food packaging using deep learning algorithms and structured light 3D reconstruction technology. Using a pre-trained MASK R-CNN model to recognize the class of the object in the image and get its 2D coordinates, then using structured light 3D reconstruction technique to calculate its 3D coordinates, and finally after the coordinate system conversion to guide the robotic arm for grasping. After testing, it is shown that the method can fully automate the recognition and grasping of different kinds of food packages with high accuracy. Using this method, it can help food manufacturers to reduce production costs and improve production efficiency. © 2023 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0770-4/23/07.

关键词： Object detection

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Research on tomato diseased detection Based on Improved YOLOv5s 3

Research on tomato diseased Detection Based on Improved YOLO...

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3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

作者： Yu, Haibo Li, Xiaochen Geng, Shuai Yao, Changyin Niu, Datian School of Science Dalian Minzu University China

ISBN: (纸本)9798400707704

In order to solve the problem of backward, inefficient and insufficiently accurate tomato leaf disease recognition technology, an improved YOLOv5 algorithm l is proposed to recognize tomato leaf disease related tasks. While keeping the lower computational amount of the model, the detection speed and algorithm performance are improved at the same time. The feature extraction structure of the traditional YOLOv5s network is improved, and by replacing the original backbone network, with a lightweight backbone network mainly based on MobileNetV3, it strengthens the extraction of the deep features of the image, and improves the effect of the detection of the object, and CA attention is added to the HEAD mechanism in the head to improve the target leakage detection problem and help the model to locate and recognize more accurately, improve the loss function and introduce the SIOU loss function to improve the regression prediction accuracy. Compared with the traditional YOLOv5s, the improved YOLOv5 algorithm reduces the number of parameters by nearly 80%, the floating-point computation by 86%, and the accuracy is close to the original level. The experimental results show that under certain conditions, the improved YOLOv5 algorithm significantly improves the efficiency, performance, and robustness of the algorithm with a very small increase in complexity. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Feature extraction

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Secure Data Reduplication system with Efficient and Reliable Multi-Key Management in Cloud Storage 3

Secure Data Reduplication System with Efficient and Reliable...

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3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Jayaram, B. Suguna, T. Fairooz, Shaik Arul Selvam, P. Vimal, S. Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Malla Reddy Engineering College Department of Cse -AIML Telangana Secunderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350336238

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. Data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues. © 2023 IEEE.

关键词： Sensitive data

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Gesture Controlled Virtual Mouse and Keyboard Using OpenCV 3

Gesture Controlled Virtual Mouse and Keyboard Using OpenCV

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3rd international conference on Emerging Techniques in Computational intelligence, ICETCI 2023

作者： Nazeer, Mohd Shri Akshita, G.A. Priyadharshini, Allapuram Krishna, Chinna Chinannagari Bala Anusha, A. Amreen, Azra Vidya Jyothi Institute of Technology Department of Artificial Intelligence Hyderabad India

ISBN: (纸本)9798350300604

A gesture-controlled computer makes it easier to connect with computers by utilizing hand gestures, voice commands, and eye movements. In this gesture approach computers seldom ever require direct contact, static and dynamic hand gestures, voice assistance, and eye motions can be used to virtually control all operations. In the existing systems it uses different parameters for gesture control such as voice, eye and hand. In this research paper, we are implementing a hybrid model used to perform gesture controlled by using voice, hand and eyes. We are implementing a Convolutional Neural Network (CNN) model by using Media Pipe running on top of pybind 11. The webcam, which is the only piece of hardware mandatorily required in the proposed algorithm, is used to record images, recognize hand gestures, and eye movements, and receive voice instructions using the Pyttsx3 module. It has been observed that the proposed algorithm provides more efficient results. it provides different form of gestures under hybrid model with only webcam as the input resource, it can be utilized for smart security and surveillance system. © 2023 IEEE.

关键词： Eye movements

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Bio-Inspired Optimization with Transfer Learning Based Crowd Density detection on Sparse Environment 3

Bio-Inspired Optimization with Transfer Learning Based Crowd...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Poongodi, A. Kopperundevi, N. Poorani, S. Tiwari, Mohit Rao, B. Srinivasa Abilash, N. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Computer Technology-UG Kongu Engineering College Perundurai India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India Gokaraju Rangaraju Institute of Engineering & Technology Computer Science and Engineering Hyderabad Bachupally500090 India Annai Vailankanni College of Engineering Mechanical Engineering Kanyakumari District Pottalkulam India

ISBN: (纸本)9798350322842

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection. © 2023 IEEE.

关键词： Deep learning

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Third international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2023

Third International Conference on Advanced Manufacturing Tec...

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3rd international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2023

ISBN: (纸本)9781510674806

The proceedings contain 47 papers. The topics discussed include: design of seedling picking mechanism for automatic transplanting machine for nodular vegetables;a wide beam tri-polarized antenna design;fabricating microlens arrays using moving mask projection lithography;an efficient inverse lithography technology method based on detail-enhanced Pix2Pix network;optimization design of air filter housing structure based on computational fluid dynamics;simulation of a rubber isolator with three-dimensional vibration reduction characteristics;research and calculation on the selection of 5 kinds of desulfurization and denitrification triple box agitator;short-life turbine blade rack extension analysis and life prediction;study on the deformation measurement method of crane main beam based on photoelectric detection;and dynamic characteristics analysis of wind turbine gear system considering bearing defects.

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Formal Modeling and Verification of the Security Mechanism in Smart Home Authentication 3

Formal Modeling and Verification of the Security Mechanism i...

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3rd international conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wang, Luyu Wang, Guoqing Zhuang, Lei Yang, Rui School of Computer Science and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9798350331387

para>With the development of the Internet of Things (IoT), smart home systems have become an essential component of daily life, greatly enhancing its convenience and comfort. However, issues related to data security and effective identity authentication mechanisms have also emerged. To date, a plethora of research has been conducted in this area. After comprehensive research and comparison, the most refined authentication mechanism currently is the dynamic trusted lightweight authentication mechanism (DTL) based on blockchain. By analyzing the DTL mechanism and using model checking theory and the principles of timed automata, the sequence diagrams of the mechanism were extracted, and variables were constructed. Using UPPAAL, a timed automaton model for the DTL mechanism was established. UPPAAL's verifier was utilized to validate its security properties, such as deadlock-free, confidentiality, integrity, access control, and freshness. It was discovered that there is a deficiency in the consideration of time, specifically in terms of freshness. By analyzing the counterexamples from the sequence diagrams and property verification, corresponding improvements were suggested. The model was subsequently refined and rebuilt to meet the freshness requirements, providing direction for the further enhancement and development of the DTL mechanism. © 2023 IEEE.

关键词： Model checking

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CO-RCNet: Optimized ResCapsNet for COVID-19 detection from chest CT scan images 23

CO-RCNet: Optimized ResCapsNet for COVID-19 Detection from c...

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3rd international conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Li, Zhihua Xing, Qiwei Jiang, Zhengang School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Institute for Educational Big Data & Artificial Intelligence Qiongtai Normal University Haikou571100 China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China

ISBN: (纸本)9798400700200

Corona Virus Disease 2019 (COVID-19) has high infection rate, leading to rapid spread worldwide. Computed tomography (CT) imaging technology is one of the important COVID-19 detection ways and it could provide the physicians diagnosis with major basis. To address the problems that traditional convolutional neural network (CNN) could not capture the spatial information relationship between different features and the shortcoming of original Capsule Network extracting insufficient depth features, this paper proposes a novel optimized residual capsule COVID-19 detection model, named as CO-RCNet and achieves the classification for COVID-19 and non-COVID-19 CT chest scanning images. To demonstrate the advantages and effectiveness of our CO-RCNet model, we perform the ablation experiments to compare with the state-of-the-art COVID-19 detection approaches. The results shows that our method has the highest evaluation metrics and a detection accuracy of 0.9940. That is to say, our method has the best performance. Therefore, the proposed CO-RCNet model can be utilized as the effective COVID-19 detection tool. © 2023 ACM.

关键词： Classification (of information)

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