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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Implementing Smart Grids via Cognitive Radio: An Overview

Implementing Smart Grids via Cognitive Radio: An Overview

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kalra, Bharti Prakash, Febin Sharma, Himanshi Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Master of Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

Calidad of service issues related to antiquated communication frameworks in conventional electrical grids are addressed by the cognition smart grid connectivity paradigm. By incorporating cutting-edge communication and information technology into electric power lines, this novel strategy promotes a two-way exchange of information. However, there is a significant need for SG communications systems to manage large data volumes because to the rise in wireless services and apps, which is being driven by the growing adoption of Internet interconnected Things smart devices. Moreover, our work surpasses the state of the art in that it not only tackles the problems caused by current communication systems but also provides a proactive solution that foresees and handles the growing data needs resulting from the changing wireless application and Internet of Things (loT) landscape. The suggested hybrid design fosters adaptation and resilience in the face of rapidly advancing technology breakthroughs, marking a key step to future-proofing SG communication systems. © 2023 IEEE.

关键词： 5G Technology and Fog Computing Cognitive Radio Internet of Things (loT) Quality of Service (QoS) Smart Grid

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VoiceSense: Designing A Robust Emotion Detection System 7

VoiceSense: Designing A Robust Emotion Detection System

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mishra, Mihir Mishra, Reeta Pandey, Anjali Mintu Ishaan Gopal Department of Computer Science & Engineering Delhi Technical Campus UP Greater Noida India Iilm University School of Computer Science & Engineering UP Greater Noida India Manav Rachna International Institute of Research & studies School of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350350067

Detecting emotions from speech is an excitingarea of research that can improve how we interact with computers and understand mental health. In our study, we explore how to build a system that can understand different emotions using speech and psychological information. We gather data from various sources like TESS, Crema-D, RAVDESS, SAVEE, and our own custom dataset to create a diverse collection of emotions. To build our system, we use different types of neural networks like CNNs, RNNs, and transformers. After testing our system extensively, we find that it can accurately detect emotions about 77% of the time, which is pretty good. We also look into how our system learns about emotions, which can help us better understand how humans process emotions. Our work not only improves emotion detection technology but also has important implications for making computers more empathetic in how they interact with people. © 2024 IEEE.

关键词： Emotion Recognition

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IDT-Cascade: a novel information dissemination tree model for influential cascade detection in online social networks

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Ganai, Aaquib Hussain Hashmy, Rana Khanday, Hilal Ahmad Bhutani, Monica Department of Computer Applications Mohan Babu University Andhra Pradesh Tirupati India Information Technology Department Central University of Kashmir Ganderbal India Department of Computer Science University of Kashmir Srinagar India Bharati Vidyapeeth’s College of Engineering New Delhi India

Social media are valuable tools for content generation, knowledge exchange, and interaction. Therefore, discovering influential users or seed nodes is critical or important when it comes to enhancing information diffusion in OSNs. In this paper, we propose the IDT-Cascade model, a way to infer distinctive cascades relying on structural characteristics, starring the height of trees of information diffusion. The model incorporates a tool in Python that identifies and analyses Information cascades by using Degree centrality and Breadth-first search (BFS). Going by the information in the tables above, a threshold defined on the basis of average cascade height is suggested for declaring influential cascades. The model was then tested using real-world networks such as the Student Government, Galesburg, and Strike structure to show that it could identify significant entertainment cascades. For example, out of two cascades included in the Student Government set, we determined that two of them were influential while demonstrating the suggested model’s applicability. As a result, the proposed IDT-Cascade model is a highly efficient, computationally tractable approach to cascade detection with implications for improving communication plans in organizational and educational settings. This work contributes to the literature by presenting improvements over current methods of cascade modeling as well as offering a reliable apparatus for studying social networks. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Cascade modelling Influential cascades Information diffusion Information dissemination trees Seed node detection Social network analysis Structural height parameter Target-based learning

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Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest 1

Cybersecurity Threat Detection in Smart Cities using Box Plo...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical Systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 IEEE.

关键词： Bot (Internet)

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Big Data Graph Node Importance Using Page Rank 1

Big Data Graph Node Importance Using Page Rank

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Roseline, R. Vasudevan, B. Clementking, A. Rani, S. Sivakumar, T. Srinivasan, C. PG Department of Computer Applications Cuddalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers. © 2024 IEEE.

关键词： Data accuracy

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Predictive Analytics for Stroke Prevention: A Machine Learning Perspective 10

Predictive Analytics for Stroke Prevention: A Machine Learni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Mattas, Puranjay Savar Mishra, Pradeep Kumar Singh, Himanshu Dev School of Engineering and Technology Sharda University Department of Computer Science and Applications Uttar Pradesh Gr. Noida India

ISBN: (纸本)9789380544472

The objective of this study is to develop a machine learning model (ML) to obtain the risk of stroke in patients. We used a dataset of 4,981 patients to train and test the model. Decision Tree Classifier (DTC) was used as the ML model, which is a widely used algorithm for classification problems. Before training the model, we cleaned the dataset by removing inaccurate and incomplete data to ensure that the model is not fed with faulty data. This confirms that the model can make accurate predictions based on the available data. After cleaning the data, we trained the DTC model and tested it on the same dataset. Our model performance shows that this model can accurately predict the risk of stroke in patients with high accuracy. The outcome of this study can be beneficial for both patients and medical professionals. Patients can be informed of their risk of stroke and can take preventative measures to reduce their risk. In conclusion, we successfully developed an ML model using a Decision Tree Classifier to obtain the risk of stroke in patients. Our study demonstrates the potential of ML in healthcare and its application in predicting the occurrence of diseases. This article can serve as a foundation for future research on improving the accuracy of stroke risk prediction models, which can lead to better patient outcomes and improved healthcare practices. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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Quantum Cryptography Techniques for Enhancing Security in IoT Communications

Quantum Cryptography Techniques for Enhancing Security in Io...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naga, Suresh Bysani Venkata Kotiyal, Arnav Battula, R.S.S. Raju Patil, Harshal Dharangutti, Yogesh Natrayan, L. Eightfold.ai Ex - Cohesity CA United States Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Aditya University Department of Computer Applications Surampalem India Iilm University School of Computer Science & Engineering Greater Noida India Department of Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha Kuthambakkam India

ISBN: (纸本)9798350375442

A system of wirelessly communicative sensors embedded in a person's clothing. In order to remotely monitor and treat a patient's health, sensor devices are embedded in clothing, skin, and the human body itself. Healthcare, the military, sports, and remote treatment are just a few of the modern WBSN applications. It improves people's quality of life by allowing for more precise and quicker disease diagnosis as well as more timely care and more effective treatment. Even if a patient is far away from medical care, WBSN can still save their life. Despite the numerous benefits that WBSN technology brings to healthcare systems, there are legitimate concerns about security and privacy when it comes to collecting and transmitting physiological data about humans in an open setting. Ensuring the security of physiological data during transmission in a wireless environment is a challenging but crucial task. Key distribution, authentication, data integrity, and data confidentiality are just a few of the many security issues plaguing the WBSN. In tackling the WBSN security issues, this research contributes significantly. Secure transmission in wireless networks and high attack resistance are both provided by the cryptographic computational process. Nevertheless, the unsecure transmission of keys across networks is the sole vulnerability of public and private key cryptography methods. Attackers can easily crack traditional key sharing protocols used by communicating parties. Intruders can compromise the system's security in its entirety if they obtain the secret key. In WBSN, a secure way to generate and distribute secret keys is required. The EBB84QCP is a method for creating and sharing secret keys between communicating parties. Rather than using two prime numbers, the Enhanced and Modified RSA Cryptosystem (EMRSACS) suggests using four prime numbers for key generation, encryption, and decryption. Data transmission in wireless networks still involves a secret quantum key that is

关键词： Quantum cryptography

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The λNF Problem 2nd

The λNF Problem

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2nd International Conference on Cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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