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Pseudo Random Number Generator Based on Cellular Automata with Self Organized Criticality

SN computer science 2024年第5期5卷 454页

作者： Poornima, I. Gethzi Ahila Yogaraja, C.A. Venkatesh, R. Sudha, M. Swarna Vijayalakshmi, B. Computer Science and Engineering Ramco Institute of Technology Tamilnadu Rajapalayam India

In cryptography, pseudo-random numbers are crucial. The degree of strength of a cryptographic technique is directly influenced by the key’s randomness. Although numerous researchers have shown that cellular automata (CA) are effective as pseudo-random number generators (PRNGs), the fundamental CA structure is being changed to increase the efficiency in terms of randomness. Here, Langton's Ant Model and Programmable Controllable Cellular Automata (PCCA) in combination with a sand pile are proposed. Sand pile model and Langton’s Ant model are built for the generation of Rule control word for the successive iterations and Cell control word in selecting controllable cells for iterations respectively. The Controllable Cellular Automaton types used here are CCA4 and CCA5. Both Sand pile model and Langton’s Ant model exhibit chaotic behaviour. Two different rule sets are used and their results are compared. Also the frequency of each rule in the rule set are determined and found to be equally utilized. Due to the integration of these systems with cellular automata, which likewise display dynamic chaotic behaviour. Its results are greater efficiency in terms of randomness. Pseudo Random number sequence generated by this proposed PCCA is benchmarked with the statistical NIST test. The experiment shows that the resulting random sequence is highly random. Their entropies are calculated and the respective graphs are plotted. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cellular automata (CA) Cryptography Programmable Controllable Cellular Automata (PCCA) Pseudo Random number Pseudo-Random Number Generators (PRNGs)

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Customer Segmentation using Clustering Techniques: Data-Driven Approach to Enhance Marketing Strategy 5

Customer Segmentation using Clustering Techniques: Data-Driv...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sakthi Priya, G. Vignesh Saravanan, K. Hemalatha, B. Karthika, R. Kavya Srinidhi, B. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Customer segmentation is a vital strategy for businesses seeking to enhance their marketing efforts and optimize resource allocation. This research focuses on segmenting customers using the Mall Customer Segmentation dataset, which includes demographic and behavioral attributes such as age, gender, annual income, and spending score. The work begins with data preprocessing, involving the handling of missing values, encoding of categorical variables, and scaling of numerical features to prepare the data for analysis. Following preprocessing, exploratory data analysis (EDA) is conducted to visualize feature distributions and relationships, providing insights into the data's structure. The core of the research involves applying various clustering algorithms-K-Means, Hierarchical Clustering, and DBSCAN-to identify distinct customer segments. The optimal number of clusters is determined through methods like the Elbow Method and Silhouette Score. The clusters are then evaluated for quality, and their characteristics are analyzed to understand different customer segments. This segmentation enables businesses to their marketing strategies, enhance customer satisfaction, and make data-driven decisions based on customer behavior insights and demographics. © 2024 IEEE.

关键词： Sales

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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Deep Neural Networks based Detection and Analysis of Fake Tweets 4

Deep Neural Networks based Detection and Analysis of Fake Tw...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Mareeswari, G. Dinesh, Erana Veerappa Ramco Institute of Technology Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350300772

Social networking is a wedge for interchanging thoughts, individual perspectives and views but without adversely affecting the sentimental, religious, or maybe private thoughts of the group. Furthermore, the spread of fake news flashes continues to be a pattern on social networking. This paper discusses a hybrid method used towards the control over this kind of ill intentions by developing a method that analysis and also detects posts, toxic comments, or fake news inside any multimedia or text format. This particular paper will help to identify the precision of fake news utilizing Deep Neural Networks. News articles extracted from Twitter is represented as embedding vectors. The hybridized Convolutional Neural Network and LSTM (Long-Short Term Memory) framework is built to learn the contextual dependencies between the words present in news articles. We compare many methods for detecting fake news. The Natural Language Interference (NLI) designs may also be qualified. The information compilation, interpretation, and then tests procedure are clarified completely along with existing different study analyses in the identity of linguistic variants to come down with truthful and false information. Next, we check as well as train a pair of learning breakthroughs to produce exact fake information detectors. © 2023 IEEE.

关键词： Deep neural networks

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Attention Contours: A Comparative Study on 2D and Virtual Reality Attention Profile Assessment Tests 5

Attention Contours: A Comparative Study on 2D and Virtual Re...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajarajeswari, P. Madhubala, R. Gomathi, V. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350389609

Virtual Reality (VR) technology in health-care has emerged as a valuable tool for advancing diagnostic techniques and enhancing patient care. This research explores the application of VR in attention profile assessments, with a particular focus on diagnosing Attention Deficit/Hyperactivity Disorder (ADHD). ADHD poses challenges in maintaining attention, and traditional diagnostic approaches often lack real-world relevance. This study examines the efficacy of Attention Contours - a VR-based Continuous Performance Task (CPT) - in comparison to its conventional 2D (two-dimensional) counterpart for assessing attention profiles. Attention Contours VR incorporates varied virtual settings and language-neutral stimuli to improve ecological validity. User trials reveal greater engagement and fewer distractions in the VR environment, highlighting its potential for more accurate attention assessment. Participants showed a strong preference for the VR format, praising its immersive experience and enhanced usability. The findings emphasize the impact of VR in advancing attention assessment tools and suggest its promising role in ADHD assessment. © 2024 IEEE.

关键词： Virtual environments

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Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

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CAAI Transactions on Intelligence technology 2025年第1期10卷 72-93页

作者： Hazique Aetesam Suman Kumar Maji V.B.Surya Prasath Computer Science and Engineering Birla Institute of Technology MesraBiharIndia Computer Science and Engineering Indian Institute of Technology PatnaBiharIndia Department of Computer Science University of CincinnatiCincinnatiOhioUSA

Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.

关键词： hyper-laplacian prior hyperspectral images image restoration mixed noise variational approach

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Smart Health Monitoring System in Intensive Care Unit using Bluetooth Low Energy and Message Queuing Telemetry Transport Protocol 5

Smart Health Monitoring System in Intensive Care Unit using ...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Ragavan, K. Ramalakshmi, R. Srirenganachiyar, V. Gnana Priya, G. Jeyageetha, K. Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India

ISBN: (纸本)9781665474672

Smart healthcare monitoring is very essential for people who are in the Intensive Care Unit (ICU) in hospitals which provides continuous monitoring of health parameters, which cannot be afforded outside the hospitals. Because of the ever-increasing population of the world, smart health care has become increasingly crucial. The traditional patient-doctor consultation has lost its usefulness. The effective way of monitoring the patient's health within ICU has great potential to introduce Bluetooth Low Energy (BLE) and Message Queuing Telemetry Transport (MQTT) protocol. The patient's survival can be improved by the daily care of parameters such as heart rate, Hemodynamic, skin temperature, capillary refill, mental status, etc., which can be wirelessly monitored in smart way using BLE and MQTT. In addition, the Short Message Peer to Peer (SMPP) protocol could also be used for alerting the doctors if the sensor values go to a critical level as per the threshold value. It uses BLE and MQTT in monitoring the health of an ICU patient and also it will compare the ZigBee technology, which is already used in monitoring health parameters in following criteria low power, performance, cost, availability etc., with the proposed method. ZigBee will not support android version which is very important to access the smart phones. But the proposed method can support android version with low power consumption. © 2023 IEEE.

关键词： Intensive care units

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Enhancing Speech Audio Emotion Recognition for Diverse Feature Analysis through MLP Classifier 1

Enhancing Speech Audio Emotion Recognition for Diverse Featu...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vignesh Saravanan, K. Sakthi Priya, G. Muthu Kumar, K. Pon Karthika, K. Advaith, K.S. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Mepco Schlenk Engineering College Department of Computer Science and Engineering Sivakasi India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

The proposed research presents a novel approach to Speech Audio Emotion Recognition, integrating a Multi-Layer Perceptron (MLP) classifier and advanced feature extraction methods. With the growing importance of emotion recognition in speech, particularly in human-computer interaction and sentiment analysis, the study utilizes the RAVDESS dataset and explores features like Mel Frequency Cepstral Coefficients, Mel Spectrogram, Zero-Crossing Rate, Spectral Rolloff, Spectral Flux, and Chroma Vectors. The methodology, encompassing exploratory data analysis, preprocessing, and model training, achieves a notable accuracy of 89.31% on both training and testing sets. Notably, the inclusion of features such as Spectral Roll off and Chroma Vectors proves pivotal in capturing nuanced non-linear emotion patterns. Real-time audio testing further validates the model's effectiveness, demonstrating practical applicability. The developed system not only significantly contributes to advancing Speech Audio Emotion Recognition but also holds promise for real-world applications, enhancing human-computer interaction and sentiment analysis across various domains. In summary, this research introduces a robust and accurate emotion recognition system, leveraging the MLP classifier and diverse feature sets, with potential implications for a range of real-world scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Real-Time Face Mask Detection Using Convolution Neural Network and computer Vision 4th

Real-Time Face Mask Detection Using Convolution Neural Netwo...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Anusuya, V. Vignesh Saravanan, K. Vishnu Praba, V. Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India

ISBN: (纸本)9789819914784

One strategy to prevent COVID-19 as a result of the pandemic is to use a face mask. Although many countries have made it a requirement for citizens to do so, the majority of people continue to disobey this order. In the current situation, police frequently check for face masks in public locations and fine anyone found not to be wearing one. The existing system identifies the person wearing the mask or not, but our proposed method identifies the person those who are not wearing the mask properly and also identifies the multiple person wearing the mask or not. On the other side, some governments have implemented technology to identify individuals wearing face masks and communicate their information to a patrol team so that they may catch them. The proposed model identifies individuals who are in the public without face masks. The proposed approach is able to identify these persons using facial detection technologies, and the data is then combined with a database of public identity information to gather information about the individual and deliver the fine amount to his home address and mobile numbers. Using the convolution neural network (CNN) model, we have identified people wearing and not wearing masks. When compared to many other algorithms, CNN can more precisely recognize data down to the pixel level. In the implementation of our model, the activation functions for the hidden and fully connected layers, respectively, were rectified linear unit (ReLU) and softmax. Two convolution layers with 100 filters each were used. The 91.21% accurate Cascade classifier is used to recognize faces. Adam is the optimizer, and cross-entropy serves as the loss function. More than 1500 images were used to train the model, which comprises classes with and without masks. The public will start wearing masks in public areas because of the terror that this AI-based mask recognition system instils in them, helping to stop the spread of diseases that are otherwise beneficial to society.

关键词： Convolution

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