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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Anitha, D. Soujanya, Thota Chakraborty, Subhra Alkhayyat, Ahmad Revathi, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Sr University School of Agricultural Sciences Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350372892

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%. © 2024 IEEE.

关键词： Gabor filters

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Survey of Covid Vaccine Hesitancy 15

Survey of Covid Vaccine Hesitancy

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Tripathi, Kirti Carter, Vivian Fergerson, An'Darius Prakash, Duraisamy Dutt, Varun Lloyd Institute of Engineering and Technology Electronics and Communication Engineering Uttar Pradesh India Tuskegee University Department of Psychology & Sociology AL United States University of Wisconsin-Green Bay Department of Computer Science WI United States Indian Institute of Technology Mandi School of Computing and Electrical Engineering Himachal Pradesh India

ISBN: (纸本)9798350370249

Vaccination serves as vital for strengthening immunity to illnesses and diseases that occurred amid the COVID-19 pandemic. There is a significant reluctance on the part of the general public as well as medical professionals to get vaccinated despite the affordability and availability of numerous licensed vaccines. This reluctance mainly causes worries about possible problems and negative effects. The keyword 'Vaccine hesitancy during COVID-19' was the primary search keyword on the Google Scholar platform. Seventeen studies that were published recently on the COVID19 pandemic that reported vaccine reluctance were found via our search, and these have been included in this review. During this survey, we found that the acceptance or rejection of vaccines was influenced by several criteria, such as income, education, gender, age, politics, religion, work position, and ethnicity. The vaccine was frequently refused for several reasons, which involve a general dislike of vaccinations, safety concerns or the belief that a rapidly developed vaccine may be too risky, the belief that immunity already exists or that the vaccine is unnecessary because COVID-19 is relatively harmless, a general lack of trust, doubts about the vaccine's efficacy, and distrust about the vaccine's origin. This review emphasizes the factors influencing COVID-19 vaccine acceptance and participation in clinical trials globally. It also presents strategies to enhance attitudes toward vaccination drives in the future. Additionally, governments and health authorities need to improve communication efforts and build public confidence. © 2024 IEEE.

关键词： Covid-19 Ethnicity Healthcare Vaccine Acceptance Vaccine Hesitancy

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The Analysis of Neural Network Models to Distinguish AI generated faces from Real faces

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Procedia computer science 2024年 233卷 295-306页

作者： Joshita Malla Harshini Vemuri SreeDivya Nagalli S Abhishek T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Rolls-Royce Inc Bangalore India

The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security systems are unable to differentiate between a high-quality deep-fake and an actual intruder's face. For instance, the risks are quite high at an airport security checkpoint, where facial recognition is the first line of security against unauthorized entry. The primary concern here is how trustworthy the computer programs and algorithms will be in recognizing counterfeits among a plethora of actual and artificially generated faces. This necessitates the need to introduce machine learning approaches to differentiate between actual and fraudulent faces, particularly when AI-generated faces are involved. Effective artificial intelligence systems must be adaptive and change quickly in the face of more complex threats rather than simply recognizing them. AI-generated faces are becoming more convincing by the day, increasing the risk of their exploitation. This poses the need to ensure that the technology on which people rely should be robust and trustworthy in critical situations, whether it is a security checkpoint or an e-commerce site. In this perspective, this study has attempted to develop and implement Artificial Intelligence-powered solutions to detect the artificial faces while ensuring reliability for critical functions in an age where reality constantly blends with fiction.

关键词： Artificial Intelligence Deep Fake Deep Learning Facial Recognition Privacy Security

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Association rule learning for threat analysis using traffic analysis and packet filtering approach

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3245-3255页

作者： Rawat, Romil Chakrawarti, Rajesh Kumar Raj, A. Samson Arun Mani, Gayathri Chidambarathanu, K. Bhardwaj, Ramakant Computer and Communication Technology Department University of Extremadura Badajoz Spain Department of Computer Science and Engineering Sushila Devi Bansal College (SDBC) Bansal Group of Institutions (BGI) Indore India Karunya Institute of Technology and Sciences Karunya Nagar Tamil Nadu Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai India Department of Mathematics Amity University West Bengal Kolkata India

The Mirai botnet source code, which was distributed on the dark web, is still changing as malware designers modify it to produce more sophisticated varieties of the botnet—the Satori—based on the malware source code. An botnet that is tied to the Internet of Things, infested with malware, and administered by offenders creates a backdoor for cybercriminals to carry out destructive operations throughout the system. The work provides association rule learning, which reveals design characteristics and warning indications of hazards using data (a data stream) collected for darknet vulnerability research. The negative effects of threat-originating computers associated with malware scripts are investigated by looking at trends in IoT-linked applications (like destination port, TCP window lengths, and kind of service).This paper provides an study of an attack (both before and after) with the first source scripts development of a conventional IoT malware, the Satori, as well as an uncommon insight into the viral processes of an attack campaign. Trials demonstrate that the suggested system is efficient in anticipating and tracking new virus tendencies in cyberspace, demonstrating that it is a viable way for streamlining and accelerating the discovery of new illicit patterns in online activities. Satori-based mathematical modelling of destination ports, ToS, and TCP window size is provided with NICT/16 DN sensor dataset that is employed in the research. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Association rule learning Cyber-attack Dark web IoT Malware

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Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval 24

Not All Pairs are Equal: Hierarchical Learning for Average-P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Precision (AP) assesses the overall rankings of relevant videos at the top list, making the predicted scores a reliable reference for the users. However, recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally, leading to an evident gap between the training objective and evaluation metric. To effectively bridge this gap, in this work, we aim to address two primary challenges: a) The current similarity measure and AP-based loss are suboptimal for video retrieval;b) The noticeable noise from frame-to-frame matching introduces ambiguity in estimating the AP loss. In response to these challenges, we propose the Hierarchical learning framework for Average-Precision-oriented Video Retrieval (HAP-VR). For the former challenge, we develop the TopK-Chamfer Similarity and QuadLinear-AP loss to measure and optimize video-level similarities in terms of AP. For the latter challenge, we suggest constraining the frame-level similarities to achieve an accurate AP loss estimation. Experimental results present that HAP-VR outperforms existing methods on several benchmark datasets, providing a feasible solution for video retrieval tasks and thus offering potential benefits for the multi-media application. © 2024 Owner/Author.

关键词： Self-supervised learning

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Construction and optimization of a computational holographic lithography system 8

Construction and optimization of a computational holographic...

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Chen, Rui Lin, Angyi You, Jianfeng Lin, Huixiang Zhao, Wenqing Zhang, Fucai School of Control and Computer Engineering North China Electric Power University Baoding071003 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education North China Electric Power University China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Traditional lithography techniques are currently facing challenges, including high costs and the susceptibility of mask plates to damage. This research aims to elucidate the feasibility and technical constraints of a novel diffraction lithography approach. This method can reduce the size and cost of lithography machines, break through existing technological barriers, and offer new possibilities for the future development of lithography machines. We have built a basic computational holographic lithography system utilizing a 405nm laser and validated the system using holograms obtained with both classical GS algorithm and a newly proposed method. With the GS holograms, patterns with 700nm features have been successfully exposed on the photoresist. With our new algorithms, multiple holograms for the same target pattern can be generated with different settings in the algorithm. Sequential exposure of those holograms resulted in continuous lines 500 nm wide with reduced speckle artifacts, which was about a 1.4-time improvement over the result from the GS algorithm. Future research will focus on optimizing and enhancing the experimental system and the hologram calculation methods to reduce the line widths further and improve the exposure pattern quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Live Audio Recognition and Text Conversion System

Live Audio Recognition and Text Conversion System

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Pratyaksha Sharma Sumita Gupta Boddu Asmitha Bhavya Khushi Sharma Saru Dhir Sapna Gambhir Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Information Technology Amity School of Engineering & Technology Amity University UP Dept. of Computer Science College of Engineering and Computing George Mason University USA

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further.

关键词： Accuracy Text recognition Reviews Hidden Markov models Speech recognition Machine learning Documentation Natural language processing Communications technology Computational intelligence

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Enhanced Expectation-Maximization Algorithm for Smart Traffic IoT Systems using Deep Generative Adversarial Networks to Reduce waiting time 4

Enhanced Expectation-Maximization Algorithm for Smart Traffi...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Yamini, B. Jayaprakash, M. Logesswari, S. Ulagamuthalvi, V. Porselvi, R. Uthayakumar, G.S. Srm Institute of Science and Technology Kattangulathur Department of Networking and Communication Tamil Nadu Chennai India Rmk Engineering College Department of Information Technology Tamil Nadu Chennai India Rmd Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Satyabama Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Tagore Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Omr Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300093

The main focus of the work is to improve the smart traffic systems for the Internet of Things (IoT) such as traditional signals and safe driving via tracking the congestion and monitoring traffic slowly. Depending on the circumstances, intersections between roads can sometimes lead to accidents, so this problem is considered part of the research. These limitations can be overcome by using modern sensors that can reduce the traffic signal waiting time and rash driving on the National Highway (NH) road. Urban countries make use of AI-based decisions performing smart control over managing vehicles. In the proposed model the novel combination of collecting road maps coordinating points that can connect the access movements. Also IoT devices such as mobile phones, unusual network failure, and peak time traffic control by preprocessing the collected traffic dataset. Based on the dataset the detection of coverage area can be extracted using Enhanced Expectation Maximization Algorithm for ranking the variables. Initially, the classification algorithms were used and grouped as accident months, non-accident months, hill stations, and highway NH Road delay time are some of categories. Using Deep Generative Adversarial Network (DGAN) data can be balanced and used to reduce the waiting time along with predicting the smart cities' transportation for reliable customers. © 2023 IEEE.

关键词： Generative adversarial networks

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SNAF-based Interdependent E2E Network Resource Slicing Scheme for a Virtualized Network

SNAF-based Interdependent E2E Network Resource Slicing Schem...

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2022 International Conference on computing, Communication, Perception and Quantum Technology, CCPQT 2022

作者： Sebakara, Samuel Rene Adolphe Sun, Guolin Liu, Guisong School of Computer Science and Engineering University of Electronic Science and Technology Uestc Chengdu China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Swufe Chengdu China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

To meet the requirements of assorted applications from different service providers operating on a shared infrastructure, the current generation of mobile cellular networks (5G) rely on network slicing. However, the synchronization of RAN and core network slicing has not been investigated as an interdependent resource allocation problem. This paper proposes a novel End to End (E2E) resource slicing and allocation scheme. The Slice to Node Access Factor (SNAF) based E2E Slice resource provisioning scheme is proposed based on diverse users' Quality of Service (QoS) requirements for transmission delay and data rate. The SNAF, in principle, ensures proper resource provisioning and traffic synchronization, and thus allocates radio resources based on the provisioned affordable traffic and backhaul resources, and vice versa. Based on the 5G air interface, we ran a system-level simulation to assess the performance of our solution from multiple angles. Simulation findings show that our proposed SNAF-based interdependent E2E resource allocation delivers improved E2E traffic-resource synchronization and enhances QoS satisfaction with minimum resource utilization when compared to benchmarked state-of-the-art methods. © 2022 IEEE.

关键词： Synchronization

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Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery

Accurate and Efficient Urban Street Tree Inventory with Deep...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Khan, Asim Nawaz, Umair Ulhaq, Anwaar Gondal, Iqbal Javed, Sajid Khalifa University Department of Mechanical and Nuclear Engineering Abu Dhabi United Arab Emirates Namal University Department of Electrical Engineering Mianwali Pakistan Charles Sturt University School of Computing. Mathematics and Engineering Port Macquarie Australia University School of Computing Technologies Melbourne Australia Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350382204

Deforestation, a major contributor to climate change, poses detrimental consequences such as agricultural sector disruption, global warming, flash floods, and landslides. Conventional approaches to urban street tree inventory suffer from inaccuracies and necessitate specialised equipment. To overcome these challenges, this paper proposes an innovative method that leverages deep learning techniques and mobile phone imaging for urban street tree inventory. Our approach utilises a pair of images captured by smartphone cameras to accurately segment tree trunks and compute the diameter at breast height (DBH). Compared to traditional methods, our approach exhibits several advantages, including superior accuracy, reduced dependency on specialised equipment, and applicability in hard-to-reach areas. We evaluated our method on a comprehensive dataset of 400 trees and achieved a DBH estimation accuracy with an error rate of less than 2.5%. Our method holds significant potential for substantially improving forest management practices. By enhancing the accuracy and efficiency of tree inventory, our model empowers urban management to mitigate the adverse effects of deforestation and climate change. © 2023 IEEE.

关键词： Deforestation

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