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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mondal, Manik Dasgupta, Sudakshina Bhattacharya, Indrajit Govt. College of Engg. & Textile Tech. Dept. of Computer Science & Engineering Serampore India Govt. College of Engg. & Textile Tech. Dept. of Information Technology Serampore India Kalyani Govt. Engineering College Dept. of Computer Applications Kalyani India

ISBN: (纸本)9798350335095

Cancer remains a major contributor to mortality rates across the globe, causing nearly 10 million fatalities in 2020. Among all cancer types, breast cancer has the highest incidence, around 2.26 million cases reported across the globe by the World Health Organization. In the last few years, breast cancer was diagnosed in approximately 2.3 million people and caused around 685,000 deaths. In this paper, our investigation entails executing a statistical scrutiny of the worldwide proliferation of breast cancer, as well as delving into various machine learning and deep learning techniques for detecting breast cancer, along with their constraints and intricacies. Additionally, we have proposed a hybrid Machine Learning (ML) approach for detecting breast cancer with utmost accuracy, which streamlines the assessment procedure presently employed by radiologists in screening mammograms. © 2023 IEEE.

关键词： Medical imaging

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DDoS Attack using SYN Flooding: A Case Study 9

DDoS Attack using SYN Flooding: A Case Study

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Kukreti, Sambhavi Modgil, Sumit Kumar Gehlot, Neha Kumar, Vinod Shree Guru Gobind Singh Tricentenary University Dept. of Computer Science and Engineering Gurgaon India

ISBN: (纸本)9789380544441

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Denial-of-service attack

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Effective Feature Selection and Soft Voting Classifier based Diabetes Detection Using Machine Learning Approaches

Effective Feature Selection and Soft Voting Classifier based...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Tania Alam Suchi Md. Anayt Rabbi Md. Abu Layek Dept. of Computer Science and Engineering Jagannath University Dhaka Bangladesh Computer Science and Engineering Jagannath University Dhaka Bangladesh

An unnecessarily increased blood glucose level is a symptom of the metabolic disorder diabetes mellitus (DM). Early diabetes detection is reduce the risk factor of kidney disease, heart failure, liver cirrhosis, it might be hazardous. Our motivation is to developed a machine learning models to forecast diabetes in the future. We applied efficient data cleaning, label encoding, normalization and pre-processing methods for better models accuracy. Prediction with synthetic minority over-sampling (SMOTE) implies the equilibrium of an unstable dataset. Different feature selection techniques such as Chi-square test, Information gain attribute evaluator, Extra trees classifier, SelectKbest, and correlation-based approach to finding the key variables. Machine learning algorithms including Random Forest(RF), K-nearest neighbor(KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting (GB), Decision Tree (DT), and Logistic Regression (LR), have been applied in the initial phase. Random Forest accuracy of 95.03% after normalizing the dataset with synthetic minority over-sampling (SMOTE) techniques. We using a voting classifier for ensemble all machine learning models in the second phase. The models achieved soft voting highest accuracy 96.69%. The findings of our models comparing with some current research indicate that it can offer greater accuracy. Now, we can accurately forecast diabetes.

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Investigating the Influence of Crisis Communication on Population Outrage amidst the Pandemic

Investigating the Influence of Crisis Communication on Popul...

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World Environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Ferguson, Izaria Lambert, Leigh Albert, Lynal Dept. of Engineering and Computer Science Tarleton State Univ. StephenvilleTX United States

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

The COVID-19 pandemic has emphasized the importance of effective crisis communication. Specifically, varied regional crisis communication has caused a substantial impact on how communities perceive the virus and its risks. Our research analyzes the COVID-19 crisis communication responses seen in the United States, Canada, France, Spain, Croatia, Israel, Belgium, and the Netherlands. Several pandemic specific societal norms were analyzed for positive and negative trends to capture population outrage, based on the various crisis communication measures implemented. The norms focused on include political intervention, mask use, travel restrictions, e-commerce, vaccination rates, and demand for disinfectants. These norms were identified because of their considerable uptick in heightened awareness in the public eye in light of the pandemic. The trends seen in these pandemic specific norms affected each population uniquely due to varying strategies adopted for crisis communication. Differences in crisis communication methods can lead to distinctive responses in outrage caused by the pandemic. For example, the United States' and the New Zealand's populations starkly juxtapose one another's crisis communication methods based on their outrage to pandemic specific societal norms. Our study investigates the influence that crisis communication has on the overall perception of the pandemic. Determining the influence that crisis communication has on population outrage can help improve future pandemic crisis communication to eventually bridge the gap between public outrage and the true risk at hand. © ASCE.

关键词： COVID-19

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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud 5

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jesy Janet Kumari, J. Thangam, S. Saleem Raja, A. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas It Department Oman

ISBN: (纸本)9798350385649

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption. © 2024 IEEE.

关键词： Quality of service

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Classification of Sarcoma Based on Genomic Data Using Machine Learning Models

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Procedia computer science 2025年 252卷 317-330页

作者： Pratham Gala Yash Pandloskar Shubham Godbole Fayed Hakim Pratik Kanani Lakshmi Kurup Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever; they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers.

关键词： Sarcoma Classification Stacking Ensemble Model Genomic Data

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Novel optimal region based robust watermarking using modified dragonfly optimization algorithm

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International Journal of Information Technology (Singapore) 2025年第2期17卷 851-858页

作者： Shamna, N.V. Kaur, Puneet Raghavendra Patil, G.E. Krishna, Nanditha P A College of Engineering Dept of CSE (IoT & amp Cyber Security including Blockchain Technology) Karnataka India EEE UIET Panjab University Chandigarh India Department of Computer Science and Engineering Faculty of Engineering and Technology Jain Deemed-to be University Karnataka Bengaluru 562112 India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

The growing concern about unauthorized access and tampering with digital images requires the development of robust and secure watermarking methods. Traditional approaches frequently struggle with achieving a balance between security, image quality, and watermark strength, especially in high-resolution images. This study addresses these issues by proposing a new watermarking technique that combines the Blowfish encryption algorithm, the Discrete Wavelet Transform (DWT), and the Modified dragonfly optimization algorithm (MDFOA). The proposed approach begins with the Blowfish algorithm encrypting the secret image, providing increased security even if the watermark is detected. High-frequency features are preserved by embedding the encrypted watermark within the LL subband of the cover image, which is generated using DWT decomposition. MDFOA is used to identify the best embedding places inside the LL subband, which improves the watermark's concealment and resilience. The same methodology is used to repeat the extraction process, providing reliable and secure data transmission throughout the process. The proposed method is a highly effective solution for secure and high-quality image watermarking. It outperforms existing techniques in terms of Peak Signal-to-Noise ratio (PSNR), Normalized correlation (NC), Bit error rate (BER), and Normalized absolute error (NAE), according to extensive experiments conducted on a variety of high-resolution images. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blowfish encryption algorithm Discrete wavelet transform Modified dragonfly optimization algorithm Watermarking technique

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Artificial Intelligence and Machine Learning Algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development

Artificial Intelligence and Machine Learning Algorithms to A...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Mithra C Showkat A. Dar Preethi A Dept. of Computer Science and Engineering School of Computing (SOC) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science and Engineeing Githam University India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. This system is built on the techniques of advanced machine Learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. The proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. This GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. The study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Machine learning algorithms Accuracy Prediction algorithms Nutrients Classification algorithms Sustainable development Crop yield Recommender systems Fertilizers Graphical user interfaces

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Accounting for systemic risk in analysis of crisis scenarios 21

Accounting for systemic risk in analysis of crisis scenarios

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21st International ISCRAM Conference, ISCRAM 2024

作者： Gonzalez, Jose J. Abbas, Reem Paulsen, Sigurd University of Agder Kristiansand Norway Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Organization Crisis Management and Information Security Kristiansand Norway

Systemic risk is ubiquitous in our increasingly globalised world owing to the interconnections within and across sectors. However, a review of National Risk Assessments performed in OECD member countries reveals that systemic risk is rarely mentioned, and it is never accounted for in those assessments. A powerful method accounts for systemic risk and identifies mitigating strategies using participatory modelling with stakeholders: strategy mapping with Strategyfinder. The risk model is represented as a directed graph consisting of risk nodes and directed edges (arrows) expressing causal influences. The method originated in the management of large, complex projects, and it has been applied to systemic risks in critical infrastructures and pandemics. We demonstrate the Strategyfinder method using a scenario describing a cyberattack on electronic communication infrastructure leading to serious consequences for health and lives. The scenario is borrowed from the report Analysis of Risk Scenarios by the Norwegian Directorate for Civil Protection. © 2024 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Cascading effects Disaster risk reduction National Risk Assessments Risk scenarios Vicious cycles

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Integrating Network Security into Software Defined Networking (SDN) Architectures 2

Integrating Network Security into Software Defined Networkin...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Polimetla, Kiran University Of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States Adobe Inc. Dept. of Information Technology AustinTX United States

ISBN: (纸本)9798350317558

The decision for introduced green and cozy networking solutions has grown as the era continues to evolve. With centralized network control and programmability functions, software applications defined as Networking (SDN) have emerged as a promising method to address this call. But, as SDN architectures become vaster, the need for strong community protection turns into even extra important. Integrating community protection into SDN structure includes enforcing safety mechanisms straight away into the manipulate aircraft, considering actual-time-grained manipulation over community site visitors. This may be done through protection guidelines and policies that can be dynamically completed and updated via the centralized controller. With the aid of leveraging the programmable nature of SDN, network protection may be, without troubles, tailored to satisfy the changing hazard panorama. The open API in SDN also permits integration with gift-day protection answers, firewalls, and intrusion detection systems, similarly, to improving the community's overall safety. © 2024 IEEE.

关键词： Architecture Dynamically Networking Protection Technology Visitors

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