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Proceedings of the International Conference on Intelligent Systems and Network, ICISN 2023

作者： Yadala, Sucharitha Pundru, Chandra Shaker Reddy Solanki, Vijender Kumar Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology TS Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India

ISBN: (纸本)9789819947249

The presence of the Internet of Things (IoT) works with the assortment and diffusion of metropolitan information data. In any case, it can release clients’ very own security data in brilliant urban areas. Many works have been done and different security systems identifying with the precautions of distributed computing have been implemented from copious points of view. Be that as it may, they don’t propose a quantitative way to deal with dissecting and assessing protection and security in distributed computing frameworks. Accordingly, we propose another private data encryption strategy in IoT under distributed computing climate. Below IoT, as per the properties and securing time, protection data can be isolated into numerous subspaces. Because of the stream figure instrument, we plan an encryption framework model of data assortment. In the subspace, the security data is encoded and moved to the transfer hub. Subsequent to encoding, they are divided and rebuilt. At long last, we use stream figure and dual-key calculation to finish opportunity nondestructive change among plaintext and ciphertext to guarantee the trustworthiness of the encoded private data. Test outcomes illustrate that the presented strategy requires some investment in the encryption and unscrambling measure, which has superior ciphertext transformation yield impact and experiences fewer organization assaults in a similar encryption time. As far as computation cost, the proposed strategy diminishes by roughly 11%. Furthermore, it has greater security and works on the security and integrity of the protection data assortment procedure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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The Enhanced Analysis of FACT-Based Framework for Traffic Prediction in V2V Networks

The Enhanced Analysis of FACT-Based Framework for Traffic Pr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Inzimam Aadil, Mir Agarwal, Ankita Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

The vehicular-to-car (V2V) communique networks have enabled a new generation of urban transportation phase. Vehicular conversation technology can offer scalability, dependability, and accuracy in various vehicle offerings. Even though a few modern studies exist on predicting traffic behavior in vehicular networks, only some are primarily based on an analytical method., this paper presents a better analytical method primarily based on the Fuzzy Adaptive Cluster-based total model (fact) for visitor prediction in V2V networks. The truth-based total version includes an extensive fuzzy-common sense-based device to degree the continued conduct and site visitors for a selected target node and then extrapolate the conduct and site visitors for the upcoming frames. Moreover, the model also contains a k-method algorithm to prepare the visitor's statistics into clusters for analysis. The effects of the simulation indicate that the proposed truth-based version is capable of addressing greater precisely and accurately the site visitors' prediction in dynamic site visitor's conditions, attaining higher performance metrics in terms of prediction accuracy, latency, and jitter as compared to techniques, like k-Nearest Neighbor (KNN), assist Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The model has primary importance for its capability as it should be degree and expects V2V site visitors in V2V communications. © 2024 IEEE.

关键词： communique networks dynamic predicting transportation

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A fast anomaly network traffic detection method based on the constrained k-nearest neighbor 13

A fast anomaly network traffic detection method based on the...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Zhu, Zhiguang Xie, Ying Yang, Xu Hu, Wei University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China National Engineering Research Center of Classified Protection Safeguard Technology for Cybersecurity Beijing China

ISBN: (纸本)9781665462631

The k-nearest neighbor algorithm has been widely used in network anomaly detection works, but its query efficiency decreases significantly when the number of samples and feature dimensions increase. To meet the demand for real-time detection, an accurate and timely anomaly detection solution is particularly important. This paper proposes a fast anomaly traffic detection method based on the constrained k-nearest neighbor (CKNN) algorithm. The method uses equilibrium modified k-means and randomized incremental method to optimize the ball tree construction scheme. Specifically, randomized incremental method is used to solve the minimum coverage ball, which optimizes the selection process of the centeroid and radius while reducing the depth of the ball tree. And the equilibrium modified k-means method replaces the original k-center principle used in the division of subtrees, which solves the problem of unbalanced search binary tree division. Meanwhile, by reducing the required number of backtracking to search the K nearest neighbors, which reduces the classification time overhead of the algorithm. We validate the effectiveness of the method on several benchmark datasets. The experimental results show that the CKNN maintains a higher query rate without loss of detection accuracy when dealing with high-dimensional, massive sample data compared with the traditional KNN algorithm. And with the growth ratio of up to 99.68% for some samples, our method also exhibits higher detection accuracy and less time consumption compared with other machine learning algorithms. © 2023 IEEE.

关键词： Anomaly detection

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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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Image-Based Extraction of Prescription Information using OCR-Tesseract 2

Image-Based Extraction of Prescription Information using OCR...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ponnuru, Mahesh Ponmalar, Sridevi P. Likhitha, A. Sree, Tanu B. Chaitanya, Guna G. School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India School of Computer Science Vellore Institute of Technology Amaravathi522237 India

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. © 2024 Elsevier B.V.. All rights reserved.

关键词： Optical character recognition

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Construction of marketing risk perception framework based on large model agent 24

Construction of marketing risk perception framework based on...

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2024 International Conference on Cloud Computing and Big Data, ICCBD 2024

作者： Chi, Wenxin Wei, Shijie School of Computer Science and Engineering Macau University of Science and Technology China Business-Intelligence of Oriental Nations Corporation Ltd. China

ISBN: (纸本)9798400710223

The new generation of artificial intelligence technology represented by the Chat-GPT large model is profoundly affecting all walks of life. The marketing risk perception framework based on large model agents brings new opportunities for marketing risk analysis of large enterprises. The author takes China Mobile's large-model intelligent marketing entity risk management and control as an example to discuss the application exploration of large-model intelligent entities in corporate marketing risks. This article first discusses the various opportunities and effects that large model intelligence brings to corporate marketing;secondly, it elaborates on its construction framework, then analyzes its user intentions and assesses risks, and finally elaborates on its operational logic. Through the above steps, the role of the marketing risk perception framework based on large model agents in the field of corporate marketing is systematically analyzed in terms of risk prediction, risk analysis, and risk prevention and control. This will systematically and comprehensively lay a practical foundation for large model agents in the field of marketing risk perception. © 2024 ACM.

关键词： Chatbots

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Advancing Vehicular Ad-Hoc Network Solutions in Emerging Economies: A Comparative Analysis of V2V Protocols Through Simulation Studies

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SN computer science 2024年第8期5卷 1-13页

作者： Raj, Dharm Ather, Danish Sagar, Anil Kumar Department of Computer Science & amp Engineering Sharda School of Engineering & amp Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

This paper aims at moving ahead with Vehicular Ad-Hoc Network (VANET) in the emerging economies via protocol analysis of Vehicle-to-Vehicle (V2V) communication through simulation study. Given the distinct challenges in developing regions and the lack of the infrastructure and resources, the research aims to find out the feasible V2V communication protocols, applicable in VANET settings and profitable. Multiple network simulators that are widely accepted were employed in the network to perform an extensive evaluation of the primary V2V protocols, considering their performance, reliability, and scalability under various conditions that are common in emerging economies. This paper ends up with a different outcome of each protocol and some of them show a better resistance compared to the others in high-density traffic scenarios while others are more suitable to the areas with sparse, low-connectivity. The research allows for a complete evaluation of existing V2V protocols for VANET implementations in these regions with guidelines on critical points to focus on in the future deployment of VANET systems in emerging economies. This research is in line with the general efforts of improving Intelligent Transportation Systems for the developing world, which aims to realize coordinated and intelligent traffic management, safer roads and better overall traffic efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Communication protocols Intelligent Transportation Systems (ITS) Network simulation Vehicle-to-Vehicle (V2V) communication Vehicular Ad-Hoc Networks (VANETs)

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 IEEE.

关键词： Discrete cosine transforms

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Global Weak Entropy Solution of Nonlinear Ideal Reaction Chromatography System and Applications

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Acta Mathematicae Applicatae Sinica 2023年第1期39卷 109-134页

作者： Jing ZHANG Hong-xia LIU Tao PAN School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Department of Mathematics Jinan UniversityGuangzhou 510632China Department of Optoelectronic Engineering Jinan UniversityGuangzhou 510632China

The ideal reaction chromatography model can be regarded as a semi-coupled system of two hyperbolic partial differential equations, in which, one is a self-closed nonlinear equation for the reactant concentration and another is a linear equation coupling the reactant concentration for the resultant concentration. This paper is concerned with the initial-boundary value problem for the above model. By the characteristic method and the truncation method, we construct the global weak entropy solution of this initial initial-boundary value problem for Riemann type of initial-boundary data. Moreover, as examples, we apply the obtained results to the cases of head-on and wide pulse injections and give the expression of the global weak entropy solution.

关键词： ideal reaction chromatography model reactant and resultant concentration initial-boundary value problem global weak entropy solution head-on injection wide pulse injection

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Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing

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Chinese Journal of Electronics 2023年

作者： Sharmila Patil(Karpe) Brahmananda S H Research Scholar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed University Professor Department of Computer Science and Engineering GITAM Bengaluru

The prevalence of the Internet of Things(Io T) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users’ Qo S need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergencyoriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization(LF-PSO). The evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. The cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5%greater than the conventional schemes like GWSO, EHO,and PSO, when the number of iterations is 50.

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