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Augmented intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Soumitra Subodh Pande Mahesh C GVS Ananthnath B. Jegajothi B. Buvaneswari D. Suganthi Ericsson Global India Private Limited Bangalore Department of Artificial Intelligence and Machine Learning Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science KMM Institute of Postgraduate Studies Ramireddipalli Tirupati Department of ECE Saveetha School of Engineering Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Computer Science Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Technical Sciences Chennai

In 2019, the deadly COVID virus, which originated in Wuhan, China, spread throughout the entire world. It is considered to be one of the worst pandemics of this period. The epidemic had a significant influence on unemployment, and it may have even caused some fatalities. Its primary function is to assist in making judgements that are better, faster, and more trustworthy. The field of medicine is becoming an increasingly important one for the application of artificial intelligence and machine learning. This is particularly true for medical areas that make use of a wide variety of biomedical images, as well as diagnostic methods that rely on the collection and processing of a significant number of digital images. This study intended to find a way to detect anomalies in chest X-ray (CXR) images using only the most essential characteristics of deep learning. Earlier detection increases the likelihood of successful treatment; otherwise, the condition may even be fatal. This study will classify X-rays with a certain proportion of accuracy according to whether or not it is normal or abnormal. This will allow us to give the patient the appropriate treatment after viewing their X-ray. ResNet-101, DenseNet-169 and InceptionV3 are used to classify the CXR images. The ResNet-101 gives higher accuracy when compared with the other models.

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AKM-FCCI: Secure Authentication and Key Management Mechanism for Fog Computing-Based IoT-Driven Critical Infrastructure

AKM-FCCI: Secure Authentication and Key Management Mechanism...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Vijay Karnatak Neha Tripathi Amit Kumar Mishra Mohammad Wazid Ashok Kumar Das Mohsen Guizani Sachin Shetty Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.

关键词： Threat modeling Wireless communication Performance evaluation Authentication Organizations Critical infrastructure Safety

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Discrete Hopfield Neural Network Model for Secure Electrical Routing in VANETs to Avoid Blackhole Attacks

Discrete Hopfield Neural Network Model for Secure Electrical...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Chennaiah Kate Arvind Tudigani Sarala Raghavendra A. Lizy M. Malathi Lalit Johari Department of Computer Science and Engineering St.Peter’s Engineering College Hyderabad Telangana India Department of Computer Science University College of Science Hyderabad Telangana India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Computer Applications IFTM University Rajput Uttar Pradesh India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Intelligent Transportation Systems (ITS) depend heavily on Vehicle Ad Hoc Networks (VANETs) to establish vehicle-to-vehicle communication because these networks improve both safety performance on the roads and the reduction of traffic congestion. Black hole attacks represent one of the major cyber threats that affect the secure operation of Vehicle Ad Hoc Networks. Black hole attacks against VANET networks represent critical threats to security which can generate data packet losses along with network instability even with current security evolution efforts. Security measures based on conventional practices lead to substantial false detection situations while their response times remain slow thus preventing real-time mitigation strategies. This study develops a novel Discrete Hopfield Neural Network with Termite Alate Optimization (DHNN-TAO) model to address the mentioned issues. The Mountaineering Team-Based Optimization (MTBO) is the initial procedure that combines network nodes to establish enhanced network stability and effective transmission between nodes. Discrete Hopfield Neural Network (DHNN) identifies black hole attacks through effective detection of malicious nodes after the clustering process ends. The optimization process of hyperparameters in DHNN uses Termite Alate Optimization (TAO). After identification of malicious activity Light Spectrum Optimizer (LSO) works to find secure communication routes between attack-free network segments. The suggested DHNN-TAO attains 1600 kbps throughput, 0.42s routing delay, 7.4ms execution time, 99.1% accuracy, 0.96 packet delivery ratio, and 0.59 control overhead, surpassing the current techniques.

关键词： Accuracy Hopfield neural networks Vehicular ad hoc networks Routing Throughput Real-time systems Delays Vehicle dynamics Optimization Traffic congestion

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EM-PAD: An Effective Mechanism for Phishing Attack Detection

EM-PAD: An Effective Mechanism for Phishing Attack Detection

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Aakash Saksham Mittal Mohammad Wazid Amit Kumar Mishra Ashok Kumar Das Sachin Shetty Mohsen Guizani Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

关键词： Wireless communication Measurement Accuracy Phishing Computational modeling Banking Benchmark testing

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An Enhancement of Service Function Chaining in Metro Mobile Ad-Hoc Networks in 5G Communication Using machine learning

An Enhancement of Service Function Chaining in Metro Mobile ...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Komala C R Varuna Kumara N. Sharmila Banu S. Sumithra S. Raja Gopal ShreeshaKalkoor Mudradi Department of Information Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Artificial Intelligence and Machine Learning Moodlakatte Institute of Technology Kundapura Kundapura Karnataka India Department of Computer Science and Artificial Intelligence SR University Hasanparthy Telangana India Department of Electronics and Communication Engineering J.J. College of Engineering and Technology Trichy Tamil Nadu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Sambhram Institute of Technology Bangalore Karnataka India

A service function chaining is a location where important computer resources are stored. The hub is designed to support for sensitive applications such as mainframes, servers and server farms, and related computer resources. In addition to those server groups that support mobile network applications, there are other server groups that support network services and network applications. Network services include NTP, Telnet, FTP, DNS, DHCP, SNMP, TFTP and NFS. Network applications include IP telephony, video over IP, and video conferencing systems. Some organizational applications are logically organized into multiple layers, which are separated by the functions they perform. In this paper an algorithm was proposed to enhance the service function chaining. It focusSome layers are dedicated to supporting external functions such as client calls or serving web pages or supporting the command line interface (CLI) for applications. In some cases, external functions can be implemented based on the Internet. Other functions handle user requests and convert them into layers that can be understood by layers such as the server or database layer.

关键词： Service function chaining Web pages Telephony Routing Reliability engineering Servers IP networks

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Designing a dynamic framework for people counting using YOLO-PC

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AIP Conference Proceedings 2024年第1期2919卷

作者： Kavitha Duraipandian Balaji Radhakrishnan Padmanabhan Vishesh Ranka Department of CSE SRM Institute Of Science & Technology Chennai India Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning Chennai India

Determining the count of individuals in a public space is helpful for video surveillance and security applications. The existing system which uses the Wi-Fi CSI method to count the number of individuals in a particular space by computing the disturbance caused between the range of field of the Wi-Fi, has a number of flaws, such as the inability to process recorded footage in real time or the likelihood of inaccuracies owing to the counting of persons who aren’t there. Therefore, we propose that people counting shall be done with a novel actual people counting method that already exists named YOLO-PC, the suggested system overcomes the aforesaid difficulties.

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Multi-Modal Meta Multi-Task learning for Social Media Rumor Detection

Multi-Modal Meta Multi-Task Learning for Social Media Rumor ...

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Communication & Computing (WCONF), World Conference on

作者： R. Poornima Sateesh Nagavarapu Soleti Navva Arun Kumar Katkoori Karrar Shareef Mohsen K Saikumar Department of Artificial Intelligence and Machine Learning Malla Reddy University MaisammagudaDulapally Hyderabad Telangana Department of Computer Science & Engineering Malla Reddy Engineering College for Women Department of Computer Science and Information Technology MLR Institute of Technology Hyderabad Telangana India Department of ECE CVR College Of Engineering Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Dept of CSE koneru lakshmaiah education foundation Hyderabad Telangana India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Since you can't rest assured on the off chance that a post is lawful or not, the occupation of finding bits of hearsay has become more significant as web-based entertainment stages have developed rapidly and much virtual entertainment information has developed. They perform multiple tasks perusing medium attempts to work on the presentation of the talk revelation task by utilizing the valuable data found in the station disclosure task. Until this point, numerous ways have been proposed to make the talk revelation process simpler. In any case, three things that keep most elevated are being techniques down. (1) They just gander at printed content and overlook multi-modular data, which is a significant piece of virtual entertainment information; (2) They don't consider the distinction in that frame of mind between the station disclosure task and the gossip revelation task, and that implies they use sta data in the incorrect manner; and (3) They don't give a lot of consideration to information hiding in the station markers' semantics. To this end, we provide a framework we term Multi-modal MetaMulti-Task learning (MM-MTL) for identifying patterns across multiple data types and bits of hearsay via virtual entertainment. We make a multi-modular post-implanting subcaste that ganders at both composed and realistic material so we can utilize different modes. To tackle the issue of dividing focus between the station disclosure task and the talk revelation task, we recommend a meta-information interest strategy that utilizes some high-level Meta-network to capture the meta-knowledge lurking beneath a complex post by layering it in. They factor that in the framework to sort out how significant each answer is so we can utilize the important data that is concealed in the little station marks. Extensive testing on two Twitter standard databases confirms that our suggested approach provides state-of-the-art speed.

关键词： Social networking (online) Reviews Semantics Entertainment industry Media Multitasking Recording Object recognition Standards Testing

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Multimodal Action Quality Assessment

arXiv

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arXiv 2024年

作者： Zeng, Ling-An Zheng, Wei-Shi School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai519082 China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou510275 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangdong Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangdong Guangzhou510275 China

Action quality assessment (AQA) is to assess how well an action is performed. Previous works perform modelling by only the use of visual information, ignoring audio information. We argue that although AQA is highly dependent on visual information, the audio is useful complementary information for improving the score regression accuracy, especially for sports with background music, such as figure skating and rhythmic gymnastics. To leverage multimodal information for AQA, i.e., RGB, optical flow and audio information, we propose a Progressive Adaptive Multimodal Fusion Network (PAMFN) that separately models modality-specific information and mixed-modality information. Our model consists of with three modality-specific branches that independently explore modality-specific information and a mixed-modality branch that progressively aggregates the modality-specific information from the modality-specific branches. To build the bridge between modality-specific branches and the mixed-modality branch, three novel modules are proposed. First, a Modality-specific Feature Decoder module is designed to selectively transfer modality-specific information to the mixed-modality branch. Second, when exploring the interaction between modality-specific information, we argue that using an invariant multimodal fusion policy may lead to suboptimal results, so as to take the potential diversity in different parts of an action into consideration. Therefore, an Adaptive Fusion Module is proposed to learn adaptive multimodal fusion policies in different parts of an action. This module consists of several FusionNets for exploring different multimodal fusion strategies and a PolicyNet for deciding which FusionNets are enabled. Third, a module called Cross-modal Feature Decoder is designed to transfer cross-modal features generated by Adaptive Fusion Module to the mixed-modality branch. Our extensive experiments validate the efficacy of the proposed method, and our method achieves state-of-the-a

关键词： Damage detection

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach

Empirical Analysis of Prediction of Crime and Forecasting Wi...

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International Conference on Edge Computing and Applications (ICECAA)

作者： R Vasuki Neha Ganvir Rohita Sharma K Senthil Kumar SVHN Krishna Kumari Kamlesh Singh Department of Computer Application Mannar Thirumalai Naicker College Madurai Tamilnadu India KJ College of Engineering and Management Pune Maharashtra India Fine Arts Lovely Professional University Punjab India Department of and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of CSE (Artificial Intelligence and Machine Learning) Vardhaman College of Engineering Telangana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well.

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An Optimized Deep learning for Efficient Defect Inspection in Smart Industry with Fog Computing

An Optimized Deep Learning for Efficient Defect Inspection i...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Potu Narayana K Keerthi Myasar Mundher Adnan G K Madhura K. Ghamya Department of CSE Stanley College of Engineering and Technology for Women Hyderabad Telangana India Department of CSE Methodist College of Engineering and Technology Hyderabad Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi institute of technology Bengaluru India Dept. of CSE (AI&ML) School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

With the development of the constantly expanding Internet of Things (IoT) devices and communications infrastructure, the majority of sensors have been used, resulting in a vast amount of data. One of the most frequent instances is the manufacturer’s inspection to precisely detect product flaws; which allows for adaptive optimization of the model’s parameters. This research proposes a novel Q-learning Reinforcement learning (RL)-based Optimization Algorithm (ROA) for optimizing the Fog Computing based Convolutional Neural Network (FC-CNN) that can discover probable faulty items precisely, to develop an effective inspection system. Given the possibility of several assembly lines operating in a single plant, one of the primary issues is the real-time processing of this data. Subsequently, adjusting the CNN model’s hyperparameters significantly enhances its faults detection capabilities. The suggested FC-CNN-ROA transfers the burden from the central servers to the fog nodes, allowing it to handle very huge volumes of data. When compared with existing techniques such as You Only Look Once version4 combined with DL (YOLO v4-DL), MobileNet-V2, the proposed FC-CNNROA obtains a higher accuracy of $\mathbf{9 9. 9 7 \%}, \mathbf{9 9. 8 9 \%}, \mathbf{9 9. 7 5 \%}$, $\mathbf{9 9. 7 1 \%}$, and $\mathbf{9 9. 1 6 \%}$ for different sets of datasets such as 10,20, $30,40,50$ respectively.

关键词： YOLO Industries Computational modeling Inspection Real-time systems Convolutional neural networks Servers

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