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OCSK-SMOTE: An improved minority oversampling technique for unbalanced dataset using One-Class SVM and K-means 2

OCSK-SMOTE: An improved minority oversampling technique for ...

引用

2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Pandey, Utkarsh Shukla, Sanyam Wadhvani, Rajesh Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal462003 India

ISBN: (纸本)9781665437899

Classification of dataset that have an imbalance in there classes still a challenge to the real world problem and a lot of research has been done and continuously being done to get a resolution to this problem, SMOTE has achieved tremendous triumph in this regard and in this paper we have focused on extension of SMOTE and developed an algorithm to overcome its shortcomings, The Highlight of the algorithm is that it removes the outliers from the Dataset and then proceeds to oversampling by first dividing into clusters of majority and minority and then oversampling only in clusters where there is more minority data so to also tackle data distribution problem of class imbalance, to test the proposed method it has been tested on 10 UCI datasets with varying degree of class imbalance ratio. © 2022 IEEE.

关键词： Classification (of information)

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OSHA: A General-purpose and Next Generation One-way Secure Hash Algorithm 22

OSHA: A General-purpose and Next Generation One-way Secure H...

引用

22nd IEEE/ACIS International Conference on computer and Information science, ICIS 2022

作者： Patgiri, Ripon National Institue of Technology Silchar Dept. of Computer Science & Engineering Assam Cachar788010 India

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers;hence, there is no way to recover and reverse the process for the adversaries. © 2022 IEEE.

关键词： Hash functions

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Design and Development of Human Following Autonomous Airport Baggage Transportation System

Design and Development of Human Following Autonomous Airport...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Shashank, M. Shvejan Saikrishna, P. Acharya, G. Prasad Reddy, Srikar Lavanya, P. Sreenidhi Institute of Science and Technology Dept. of Computer Science and Engineering Hyderabad India Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9781665495295

This paper aims to the design and development of a miniaturized model of a baggage carrying robot that can autonomously navigate from its parking place to the passenger at the airport and follow the passenger by avoiding the obstacles in its path. In airports, trolleys are used by the passengers to carry the baggage during check-in or exit. It becomes very difficult when the passenger has to carry a large amount of luggage as well as need to take care of their children or elderly people. Under these circumstances, the proposed autonomous robot, here named Smart Luggage Robot will be of great help. The robot is equipped with a GPS system that navigates the robot from its parking location to the passenger who has booked it using a mobile app. After loading the baggage, the user/passenger enable the app and the robot follows the passenger up to the check-in or exit point. The robot finally generates the bill based on the distance and weight of the baggage. This paper discusses the hardware and software components used to build the robot, and also presents an obstacle avoidance algorithm for the robot to follow the passenger. The built prototype has been successfully tested at the airport. © 2022 IEEE.

关键词： Robots

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations 2

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Li, Jiahong Chen, Yiyuan Wang, Yichi Ye, Yiqiang Sun, Min Ren, Hao Cheng, Weibin Zhang, Haodi College of Computer and Software Engineering Guangzhou China Cornell University Dept. Computer Science New York United States Shenzhen University College of Computer and Software Engineering Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350373257

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses. © 2023 IEEE.

关键词： Pulmonary diseases

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Mitigating DDoS Attacks using SDN-based Network Security Measures 2

Mitigating DDoS Attacks using SDN-based Network Security Mea...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Polimetla, Kiran University Of North Dakota Grand Forks School of Electrical Engineering and Computer Science North Dakota United States Adobe Inc Dept. of Information Technology AustinTX United States

ISBN: (纸本)9798350317558

Distributed denial-of-service attacks represent a significant threat to the delivery and integrity of network services. The attacks inundate a specific community or tool with excessive internet traffic, making it impossible for legitimate users to access. Given their intricate and ever-changing characteristics, conventional community safety functions, firewalls, and intrusion detection structures cannot effectively mitigate these threats. Consequently, it may be necessary to implement inexperienced and flexible solutions to counter DDoS attacks. A practical approach is the implementation of software-defined networking solutions for network security. Software-defined networking (SDN) prioritizes the ability of a community to have control over and efficiently manage the flow of data, as well as the capacity to manipulate centrally and program network devices. By utilizing SDN, community safety capabilities can dynamically alter the network's structure, routing, and location of net traffic to mitigate DDoS attacks. The feature allows for redirecting malicious internet traffic to a designated scrubbing centre, where these harmful websites can be filtered out before they reach the intended network or device. © 2024 IEEE.

关键词： Complexity Denial-of-carrier Dynamically Firewalls Malicious Websites SDN

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A Novel Approach for the Detection of Tea Leaf Disease Using Deep Neural Network

A Novel Approach for the Detection of Tea Leaf Disease Using...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Datta, Saikat Gupta, Nitin Dept. of Computer Science and Engineering National Institute of Technology Hamirpur HP Hamirpur177005 India

India is among the biggest tea exporter in the world. However, tea leaf diseases caused by persistent pathogen exposure result in considerable crop yield losses around the world. Detection of the disease of tea leaves at early stages can reduce the damage of tea output. Detecting the disease with the naked eye can be inefficient and counterproductive. Convolutional Neural Networks (CNNs) are commonly used to implement an effective method for the image classification. In detection of plant disease, the use of CNN is widespread. Therefore, in the proposed work, a Deep CNN having multiple hidden layers is considered for the classification of diseased tea leaves into different categories. This helps the network in detecting more number of features and thereby improving the accuracy in disease detection. The classification is done consisting of the following categories of leaves;Gray Blight, Algal Spot, Brown Blight, Helopeltis, Healthy Leaves and Red Spot. Further, a labeled dataset consisting of 5867 diseased and healthy tea leaf images have been created and uploaded on Kaggle. The suggested method demonstrates that the model is able to accurately detect the kind of persistent tea leaf disease with a 96.56% accuracy. The accuracy of the following disease classes are as follows, Algal Spot has an accuracy of 98.23%, Brown Blight has an accuracy of 97.98%, Gray Blight has an accuracy of 93.46%, Healthy classes of leaves has an accuracy of 99.10%, the Helopeltis disease class has an accuracy of 98.98% and Red Spot has an accuracy of 92% The model that is proposed in this literature is far superior than the existing methods in terms of accuracy. Furthermore this model can be adopted to work with various IoT devices to deploy in real world applications and the architecture of this model can be used to train different crop images to classify their diseases. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Transformed Deep Spatio Temporal-Features with Fused Distance for Efficient Video Retrieval 4

Transformed Deep Spatio Temporal-Features with Fused Distanc...

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4th International Conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Banerjee, Alina Kumar, Ela Ravinder, M. Indira Gandhi Delhi Technical University for Women Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665499026

For the goal of video retrieval, this research proposes wavelet transformations on deep spatiotemporal characteristics. The component-wise similarities between the query video feature and prototype video feature are calculated because level 1 wavelets extract two components from any signal or feature vector. The ultimate dissimilarity for determining the top 1 and top 5 accuracy is created by fusing these differences. The outcomes demonstrate that the suggested technique performs better than a baseline strategy. The following strategy for improvement can be investigated further by employing fast learning networks that are trained on the training sets of both data sets to provide better classification of the query as well as the prototype feature vectors, which would enhance the retrieval accuracy. © 2022 IEEE.

关键词： Wavelet transforms

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Smart Vehicle Parking System on Fog Computing for Effective Resource Management 1

Smart Vehicle Parking System on Fog Computing for Effective ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Kumara, Shubham Prasad, Nimmala Hari Monika, M. Tuli, Harshit Supreeth, S. Rohith, S. REVA University School of CSE Bengaluru India REVA University School of Computer Science and Engineering Bengaluru India Nagarjuna College of Engineering and Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9798350323795

The rapid advance in the number of vehicles on the road led to a corresponding growth in demand for parking spaces. However, conventional car parking systems are inefficient and time-consuming. Smart Parking systems also require immediate response, and low latency, which may be impacted by data transfer delays to and from the cloud. Cloud computing can provide infrastructure-level services. Also, the smart parking system is a sensitive application that can lead to disaster if an immediate decision has not been taken. To address these challenges, this research proposes the smart car parking system using fog computing that reduces latency. The suggested smart vehicle parking system uses a combination of sensors, cameras, and edge devices to collect and process parking-related data. This connection generates an enormous bundle of data that is required to be saved and processed. Fog computing widens cloud services to the end of the network to cut down latency and congestion. The major challenge in implementing Fog computing is managing the resources, which involves applications to determine each edge gadget effectively, which reduces latency and increases throughput. The proposed simulation that portrays IoT and Fog nodes and evaluates the consequences of resource management approaches in terms of QoS parameters. The proposed hybrid markov system have been utilized in various parking scenarios, including shopping centres airports, and residential areas. The paper contributes to the development of a smart vehicle parking system by proposing a computing-based approach using fog that optimizes resource utilization and provides real-time processing for parking management. © 2023 IEEE.

关键词： Internet of things

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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Artificial Intelligence, computer, Data sciences and Applications (ACDSA), International Conference on

作者： Deepthi Tabitha Bennet Preethi Samantha Bennet Priya Thiagarajan Sundarakantham. K Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better.

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Deep Learning-based Secure Machine-to-Machine Communication in Edge-Enabled Industrial IoT 3

Deep Learning-based Secure Machine-to-Machine Communication ...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Mankodiya, Harsh Jadav, Nilesh Kumar Tanwar, Sudeep Gupta, Rajesh Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India U. V. Patel College of Engineering Ganpat University Dept. of Computer Engineering Mehsana India

ISBN: (纸本)9781665462006

A still in-progress technology of the Industrial Internet of things (IIoT) involves the use of machine-to-machine (M2M) for communication. It can serve the whole purpose of automation in industries by integrating it with smart systems. However, hindrances in such a system lie in the security area. Traditional systems can be easily compromised by developing competent algorithms. The modern system with robust security sometimes fails to sustain the network traffic and load at deeper layers in the communication systems. To overcome the aforementioned issue, we propose including a trained neural network that can interpret incoming machine data streams in this study. It isolates malicious machines from normal machines by identifying attacks based on machine requests. Hence, this can prevent security breaches early. The performance of the trained neural networks is evaluated considering standard metrics such as F1-score and accuracy scores. The proposed model significantly improves the detection rate of malicious machine requests. © 2022 IEEE.

关键词： Internet of things

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