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共 595 条记录，以下是151-160 订阅

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Advanced Mechanisms of Perception in the Digital Hide and Seek Game Based on Deep learning 5

Advanced Mechanisms of Perception in the Digital Hide and Se...

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5th IEEE International conference on Smart Systems and Technologies, SST 2022

作者： Livada, Časlav Hodak, David Department of Software Engineering Faculty of Electrical Engineering Computing and Information Osijek Croatia Faculty of Electrical Engineering Computing and Information Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

the goal of this paper is to demonstrate the power of machine learning and deep learning combined with powerful tools used in creating virtual simulations. this is made possible by the Unity Game Engine, a software used for the development of 2D and 3D video games and simulations, and the ML-agents plugin, which contains algorithms for training intelligent agents. the basic principles and algorithms of Deep learning are described. Agent uses these methods to perceive the environment and solve certain tasks. In this paper, a simulation is created in which the agent tries to find hidden objects using machine learning. When the agent finds an item, a new location for the item is randomly generated. It must also complete the task in a specified amount of time. With the right implementation and parameters, the agent could successfully complete the task. © 2022 IEEE.

关键词： Reinforcement learning

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An efficient multi-functional deep learning model for effective medical image classification using skin lesion database 5

An efficient multi-functional deep learning model for effect...

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IEEE 5th International conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Nampalle, Kishore Babu Raman, Balasubramanian Indian Inst Technol Roorkee Comp Sci & Engn Dept Machine Intelligence Lab Roorkee Uttar Pradesh India

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

the automatic process of classifying a medical image plays a vital role in Computer-Aided Diagnosis (CADx). Due to the advent of Convolutional Neural Networks (CNNs) and wide usage, there has been a substantial improvement in the performance of the classification process combined with the process of implicit feature extraction. CNN requires a large amount of data, but building an extensive data set is challenging. Hence, Transfer learning appeared to resolve the same issue. Predefined models like MobileNet, VGG19, Inception-V3, and ResNet50, based on datasets with more sizes such as ImageNet, play a vital role in training and improving the performance. Extracting such unique features from medical images is a challenging task due to the different properties of images. Training a Deep Neural Network is an intensive task because it requires high configured computing machines and may require more time. Hence, this paper proposed a multi-functional deep learning architecture, including an ensemble of Logistic Regression classifiers and a MobileNet pre-trained model. Here, the input data of skin lesion images from the ISIC challenge dataset for binary and multi-class classification. Obtained results are compared with other models with the help of performance metrics.

关键词： Deep learning Training Measurement Transfer learning Feature extraction Skin Lesions

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Graph Neural Network-based Android Malware Classification with Jumping Knowledge 5

Graph Neural Network-based Android Malware Classification wi...

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5th IEEE conference on Dependable and Secure computing (IEEE DSC)

作者： Lo, Wai Weng Layeghy, Siamak Sarhan, Mohanad Gallagher, Marcus Portmann, Marius Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

this paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their interprocedural calls. thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intraprocedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. the proposed method has been extensively evaluated using two benchmark datasets. the results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.

关键词： Graph Neural Networks Android Malware machine learning

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Hyperdimensional Consumer Pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 5

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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5th International conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Goyal, Shivam Singh, Sunil K. Kumar, Sudhakar Sarin, Saket Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh Chandigarh India Asia University Taichung Taiwan Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. this research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs' unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. this approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. this multidisciplinary effort represents a paradigm shift in quantum technology's application across consumer domains. this paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. the fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential. © 2023 ACM.

关键词： Large datasets

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Detection of Adaptive Network Intrusion Employing Enhanced machine learning Systems 5

Detection of Adaptive Network Intrusion Employing Enhanced M...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Nirmala, B. Vijaya Mahadev Ravi Kiran, K. Gehlot, Anita Bhojne, Shilpa Sachin Bordoloi, Dibyhash PSNA College of Engineering and Technology Information Technology Tamilnadu Dindigul India Chandigarh University Department of AIT-CSE Punjab Mohali India UCEK Computer Science and Engineering JNTUK Kakinada India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Sandip University Department of Computer Sciences and Engineering Maharashtra Nashik India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

In a variety of situations, the use of network intrusion detection systems (NIDS) is essential for preventing computer network disturbances. Characterizing network traffic is troublesome, by and by, because of the intricacy of commuter network and assaults. the recentness, amount, and pertinence of datasets are critical contemplations to make while picking and tweaking a machine learning (ML) classifier since ML approaches in a NIDS may be influenced by different situations. the suggested method assesses recently created datasets that are designed to represent real-world circumstances. For better intrusion detection, it also offers empirical evaluations of practical, systematic ML-based NIDS with large amounts of network traffic. the evaluation method includes a comparison of different machine learning classifiers, including deep learning. Results on how the suggested method improved model efficacy for NIDS in a more real-world environment are discussed. the best outcomes were consistently attained by models using random forests and recurrent neural networks. © 2022 IEEE.

关键词： Intrusion detection

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MMArt-ACM 2022: 5th JointWorkshop on Multimedia Artworks Analysis and Attractiveness computing in Multimedia 22

MMArt-ACM 2022: 5th JointWorkshop on Multimedia Artworks Ana...

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ACM International conference on Multimedia Retrieval (ICMR)

作者： Nitta, Naoko Hu, Anita Tobitani, Kensuke Mokogawa Womens Univ Nishinomiya Hyogo Japan Natl Tsing Hua Univ Hsinchu Taiwan Univ Nagasaki Nagasaki Japan

ISBN: (纸本)9781450392389

In addition to classical art types like paintings and sculptures, new types of artworks emerge following the advancement of deep learning, social platforms, media capturing devices, and media processing tools. Large volumes of machine-/user-generated content or professionally-edited content are shared and disseminated on the Web. Novel multimedia artworks, therefore, emerge rapidly in the era of social media and big data. the ever-increasing amount of illustrations/comics/animations on this platform gives rise to challenges of automatic classification, indexing, and retrieval that have been studied widely in other areas but not necessarily for this emerging type of artwork. In addition to objective entities like objects, events, and scenes, studies of cognitive properties emerge. Among various kinds of computational cognitive analyses, we focus on attractiveness analysis in this workshop. the topics of the accepted papers cover the affective analysis of texts, images, and music. the actual MMArt-ACM 2022 Proceedings are available at: https://***/***?id=3512730.

关键词： multimedia artwork analysis attractiveness computing

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An Experimental Investigation on the Emotion Recognition Using Power Spectrum Density and machine learning Algorithms in EEG Signals 5th

An Experimental Investigation on the Emotion Recognition Usi...

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5th EAI International conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2022

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9783031283239

Emotion recognition is a technique for identifying and classifying human emotions by combining biosensing, face recognition, speech and voice recognition, machine learning, and pattern recognition. the electroencephalogram (EEG), which is based on brain signals, is used in the proposed work to identify the emotions. Here, the DEAP dataset is used to gather information about human emotional states, and machine learning algorithms like SVM, KNN, and MLP were used as the classifiers. In this proposed work, we used Power Spectrum Density as the Feature Extraction Technique and Welch’s Periodogram as the Signal Decomposition Technique. It was observed that gamma signals show more Valence, whereas Alpha signals show Arousal. To Cross validation system has been used to validate the performance of classifiers used in the work. Based on the locations of the electrode, EEG bands, and classifiers, accuracy comparisons were done. Out of the three classifiers, KNN scored more accuracy than other classifiers used. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Efficient Implementation to Reduce the Data Size in Big Data Using Classification Algorithm of machine learning 5th

Efficient Implementation to Reduce the Data Size in Big Data...

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5th EAI International conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2022

作者： RajKumar, V. Priyadharshini, G. Krishnasamy College of Engineering and Technology Cuddalore India

ISBN: (纸本)9783031283239

Big data play an enormous role in the real-world industry, such as financial, banking, government sector, and healthcare, to store the enormous amount of data stored and processed. Here, big data analytics processing develops many applications but also leads to some challenges: data security, data structure, data standardization, and data storage and transfer. Big data are working more number of data to process, and nowadays, the stored data are increased exponentially in terabytes to zeta bytes, so we will not handle those increases by filtering each data to stored classified data value, removing the replicated data and the unnecessary data stored in big data. In this research, we involved big data analytics in machine learning technique (BDML) to reduce the challenges mentioned above using a classification algorithm. they are many types of classification algorithms that are there to predict the outcome of unstructured data. the primary purpose of the research is to reduce the rising size of data stored and sent, improve data security, and create organized data using machine learning techniques. A graphical model shows our aim of research attained with exemplary efficient implementation. In future, we will improve this technique to work with various real-world applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data Analytics

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Survival Prediction of a Patient afterward a Heart Attack by machine learning 5

Survival Prediction of a Patient afterward a Heart Attack by...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Giri, Biswajit Agarwal, Suman Kumari Kumari, Nandani Majumder, Rana Gupta, Sumita Mitra, Anirban Meghnad Saha Institute of Technology Kolkata India Sister Nivedita University Kolkata India ASET Amity University Noida India

ISBN: (纸本)9798350398267

Heart attack is a major threat to human life. It occurs in one or more coronary arteries refilled by the oxygen-rich blood, which also supplies into the heart muscle, suddenly becomes blocked, and unfortunately, a few heart muscle sections can't get sufficient oxygen. In past, most patients suffered heart attacks at some stage in life. Unfortunately, some of them lost their lives due to this. When the non-survival and survival variables both are examined that determines whether a patient will survive for one more year after suffering from a heart attack. A supervised learning technique has been applied to the Echocardiogram Dataset. the experimental outcomes show that the proposed methodology applied with several data preprocessing approaches achieved a decent 94.74% classification accuracy. © 2022 IEEE.

关键词： Oxygen

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Detecting Distributed Denial of Service Attacks in Internet of things Networks Using machine learning in Fog computing 5

Detecting Distributed Denial of Service Attacks in Internet ...

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5th International conference on Engineering Technology and its Applications, IICETA 2022

作者： Hassan, Karrar Falih Manaa, Mehdi Ebady University of Babylon College of Information Technology Babylon Iraq

ISBN: (纸本)9781665472159

the number of Internet of things (IoT) devices and the data produced by these devices have increased dramatically in recent years. IoT networks have benefited from the arrival of 5G, which aims to link more devices quickly and reliably. However, many of these devices are unsecured, leaving the Internet open to assault. Since IoT devices are resource-restricted, attackers may simply mount a larger and more complex DDoS assault using IoT devices. Entropy-based detection is technique proposed by other researchers, and it has a high sensitivity and a good degree of reliability. In this paper, authors propose an entropy-based framework with the machine learning algorithms to optimize the detection result. the proposed system consists of three parts: the first part is to create a sliding time window to calculate the entropy, and the second is using the Euclidean distance to make early detection possible during the DDoS but not after the collapse. the third part is the use of machine learning algorithms (KNN and SVM), to improve the accuracy of the results in early detection. the results showed that the proposed system is able to increase the accuracy from 98.6% to 100%, and efficiently detect attacks in real-time for (ICMP, TCP, UDP, HTTP and DNS) DDoS attacks. © 2022 IEEE.

关键词： Internet of things

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