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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： M. Manimaraboopathy Rajeshwari D R. Rathiya C B Selva Lakshmi V. Lathajothi Sangeethaa SN Electronics and communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Dept. Of Information science & Engineering The National Institute of Engineering Mysuru Department of Information Technology Dr NGP Institute of Technology Coimbatore CSE Velammal College of engineering and technology Dept. of CSE (AI & ML) Velalar College of Engineering and Technology Thindal Erode Computer Science & Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

A linguistic expression, Sarcasm generally conveys the opposite meaning of what had been said in words, which makes it a compplex process for the machines to find out the real meaning. It is mainly depends on the context, making it hard for computation task and is largely defined by the inflexion with which it is spoken, with suggestion of irony. Furthermore, sarcasm express the negative sentiments using positive word, enabling it to confuse sentiment analysis (SA) model. Sarcasm detection is a crucial challenge in natural language processing (NLP), which is required for good understanding to serve as an interface for mutual interaction between humans and machines. This article develops an Enhanced Sarcasm Detection using Grey Wolf Optimizer with Deep Learning (ESD-GWODL) technique on social media. The major intention of the ESD-GWODL method is to detect and classify sarcasm on social media. To accomplish this, the ESD-GWODL technique applies data pre-processing to transform the input data into relevant format. Besides, TF-IDF model can be used to extract word embedding. To detect and categorize sarcastic texts, the ESD-GWODL technique applies long short-term memory (LSTM) model. The ESD-GWODL technique utilizes GWO algorithm for hyperparameter tuning purposes in order to boost the sarcastic text detection. The experimental evaluation of the ESD-GWODL technique is tested using sarcasm dataset. The obtained values infer that the ESD-GWODL technique has resulted in boosted detection results.

关键词： Deep learning Sentiment analysis Social networking (online) Text detection Transforms Linguistics Feature extraction Internet of Things Tuning Long short term memory

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Long-Term Power Consumption Prediction with Two-Stage Clustering Framework

Long-Term Power Consumption Prediction with Two-Stage Cluste...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hsin-Chiao Wang Yi-Ling Chen Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

Power prediction tasks are of significant importance in time-series prediction research due to their close association with energy issues. However, when applying traditional clustering algorithms to predict power consumption using time-series data, three main problems often arise. Firstly, clustering results may be of poor quality due to insufficient users in the cluster. Secondly, users in the same cluster may have similar total power consumption values for the year, but their data may differ in some fine-grained time periods. Finally, users in different clusters may still have similar data in certain time periods. To address these issues, we propose a new Two-Stage Clustering Framework (TSCF). Our framework can divide user data into proper data segments, making it applicable even if there is only one single user, thereby addressing the issue of weak clustering performance caused by insufficient users. Additionally, TSCF segments data into finer pieces to address the issue of fine-grained dissimilarity between users in the same cluster, and groups similar data segments into the same cluster to address the issue of similar data in different clusters. Finally, TSCF leverages the model selection process to find the model that best fits current user data. Extensive experiments are conducted on real-world power consumption data of commercial users in Taiwan and the United States to compare our proposed framework with various baseline approaches. The results show that our proposed framework outperforms baseline methods by approximately 49.67% and 144.22% on the MAE and MAPE indicators, respectively.

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Enhancing Task Scheduling in Cloud Computing: A Multi-Objective Cuckoo Search Algorithm Approach 3

Enhancing Task Scheduling in Cloud Computing: A Multi-Object...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Shetty, Pallavi Veeraiah, Vivek Khidse, Swati Vitthal Rai, Mritunjay Gupta, Ankur Dhabliya, Dharmesh Dept. of Mca Karnataka Udupi India Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka Tumkur India Dr. Babasaheb Ambedkar Marathwada University Maharashtra Aurangabad India Shri Ramswaroop Memorial University Dept. of Ece UP Barabanki India Vaish College of Engineering Dept. of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Dept. of It Maharashtra Pune India

ISBN: (纸本)9798350330724

Services that are delivered and managed online employ cloud computing. Without taking into account the end user's actual location, cloud computing offers data access and storage devices. Clouds are quickly becoming as a crucial application platform for science. Due to the limitless number of nodes in the cloud environment, resources are inherently unstable, which has a significant impact on task execution and scheduling. Hardware, virtualization, distributed computing, and Internet-based service delivery improvements in recent years have all contributed to the evolution of cloud computing. Cloud computing may not entail many cutting-edge technology, but it does offer a novel approach to IT management. An essential and crucial issue in cloud systems is the proper scheduling of jobs that have been received across service providers. In cloud settings, the scheduling problem is NP-hard. The current system offers a scheduling algorithm that takes care of the main problems with job scheduling in the cloud. To plan the jobs in cloud computing for this project, the system suggested a method called Multi Objective Cuckoo Search. © 2023 IEEE.

关键词： Scheduling algorithms

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On Brooks' Theorem

arXiv

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arXiv 2022年

作者： Gopalan, Sajith Saxena, Sanjeev Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati781039 India Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur208 016 India

In this note we give two proofs of Brooks’ Theorem. The first is obtained by modifying an earlier proof and the second by combining two earlier proofs. We believe these proofs are easier to teach in computer science ...

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Seokhan Lee Bonhwa Ku Hanseok Ko Dept. Computer Science and Engineering Korea University Seoul Republic of Korea Machine Learning Big Data Institute Korea University Seoul Republic of Korea Dept. Electrical and Computer Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines.

关键词： Learning systems Large language models Diversity reception Signal processing algorithms Reinforcement learning Signal processing Acoustics Safety Speech processing

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A Machine Learning based Approach to Detect Phishing Attack

A Machine Learning based Approach to Detect Phishing Attack

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Naman Jain Prateek Jaiswal Sanyam Sharma Kavita Sharma Vishnu Sharma Galgotias College of Engineering and Technology Dept. of Computer Science and Engineering (CSE) Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Phishing is a well-known cybercrime that is increasing rapidly day by day. Phishing involves sending emails with suspicious links or their attachments that can accomplish a number of tasks and includes credentials or personal account information of the individual who experienced harm. These emails hurt the recipients, result in financial loss, and steal identities. Most fraudulent websites design the exact interface and universal resource locator (URL) as the legitimate websites. As anti-hacking persons are now in search for trustworthy and stable phishing detection methods in URLs. In this paper, the focus is on utilizing machine learning technology to identify phishing URLs. The approach involves analyzing a range of features present in both legitimate and phishing URLs. In order to identify phishing websites URLs, we use Decision Tree, random forest, Support vector machine and other useful machine learning algorithms. The conclusion of the studies shows that the considered methods perform more effectively in detecting suspicious URLs than more recent methods.

关键词： Uniform resource locators Support vector machines Machine learning algorithms Phishing Forestry HTTP Electronic mail

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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： M. Jithender Reddy Arnav Kotiyal Sorabh Lakhanpal Arti Badhoutiya T Mounika Muthuswamy Jayanthi Dept. of Computer Science and Engineering Vasavi College of Engineering Hyderabad Telangana Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara Punjab India Department of Electrical Engineering GLA University Mathura Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems.

关键词： Training Data privacy Privacy Adaptation models Accuracy Federated learning Scalability Medical services Predictive models Predictive analytics

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model

Stock Market Prediction Using Genetic Algorithm Assisted LST...

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International Conference on Computing and Networking technology (ICCNT)

作者： Shreyas Nikhil Rahul Kumar Sah Santosh Kumar Parki Til Bikram Tamang Somashekhara Reddy. D Mahesh T R Dept. of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore India

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values.

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks

End to End Model to Reduce the Inference, Jamming, and to In...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shashank Dhananjaya Narender M Sunitha R Department of Information Science and Engineering The National Institute of Engineering Mysore Department of Computer Science and Engineering The National Institute of Engineering Mysore Dept. of Artificial Intelligence and Machine Learning B. N. M. Institute of Technology

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have been working on reducing inference and the effect of jamming in the network. But still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network.

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Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study

Leukemia Cancer Classification from Abnormal Cell Images Usi...

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International Conference on computer and Information technology (ICCIT)

作者： Sadia Afrin Subrata Kumer Paul Rakhi Rani Paul Md. Momenul Haque Md Shahriar Kabir Sultan Fahim Md. Kamrul Islam Dept. of Computer Science and Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore Bangladesh

Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia.

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