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Intrusion Detection in WSN with Optimal Trained Hybrid Model: Introduction to Self-Improved Beluga Whale Optimization 4

Intrusion Detection in WSN with Optimal Trained Hybrid Model...

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4th International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Chandan, Madhavarapu Santhi, S.G. Rao, T. Srinivasa Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Annamalainagar India Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Annamalainagar608002 India Gudlavalleru Engineering College Department of Computer Science and Engineering Andhra Pradesh Gudlavalleru India

ISBN: (纸本)9798350343670

WSN security is a current area of research for many scientists. One of the best security tools for defending the network from malicious assaults or the unauthenticated access is IDS. In this article, we suggest an optimized hybrid classification model-based IDS with certain steps to follow. In preprocessing, the issue of class imbalance data is solved using SMOTE. A collection of features, including raw features, statistical features, and entropy features are extracted in the feature extraction step. Optimal feature selection is done based on the extracted feature set via proposed SI-BWO (Self Improved Beluga Whale Optimization) algorithm. Attack detection is the next stage, in which hybrid classification will be takes place by combining the CNN and DBN model. The same SI-BWO algorithm is used to perform the hybrid model's optimum training. Once an attack has been detected, the attack mitigation procedure is then carried out. © 2024 IEEE.

关键词： CNN IDS SI-BWO SMOTE WSN

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Natural Language Programming towards Solving Addition-Subtraction Word Problems for assessing AI-based offensive code generators 4

Natural Language Programming towards Solving Addition-Subtra...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Shylesh, Arepelly Reddy, Chirra Dhanush Reddy, Bokka Sai Yashwanth Kansal, Shruti Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350393354

Programming remains a dark art for beginners. The gap between how people speak and how computer programs are made has always made it hard for people to become programmers. Learning all the complex rules of programming languages can be really tough and often stops people from trying coding. But here, we have an idea to make things much easier. We're working on a special tool that lets you tell the computer what you want to do in regular human language, whether you write it or say it out loud. It will then create the actual computer code for you, like magic! This will make programming easy, efficient, and user-friendly. By combining NLP and automatic code generation, our project wants to help people express their programming ideas in a way that feels natural and easy. This will bring in a new era of coding that's friendly to everyone, making it possible for people of all kinds to bring their computer ideas to life without worrying about confusing coding rules. © 2024 IEEE.

关键词： C (programming language)

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AI Based Native Food Nutrition Prescribing Chatbot

AI Based Native Food Nutrition Prescribing Chatbot

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kumar, P. Ananthajothi, K. Duraimurugan, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Several studies on the advantage of eating mindfully have concentrated on the idea that maintaining a healthy diet can aid people in preventing the onset of chronic illness including cancer, diabetes, and obesity. It is time-consuming to learn the nutritional worth and contents of each item we eat on a regular basis. So, we require a "Intelligent Chat Bot"that uses artificial intelligence to respond to queries about food's nutritional information and other healthier substitutes. It makes use of algorithms and information databases to comprehend user input and produce responses about diet and nutrition. Natural language comprehension techniques form the basis of the chatbot, but voice synthesis and recognition are also crucial for the achievement of the agent's interactions. This Chabot offers the user the nutritional information for the foods we consume every day, together with recommendations for local food options from the closest market, and also safe chat history storage for future users' input. © 2024 IEEE.

关键词： Bot (Internet)

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Machine Learning Prognosis for Smartphone Dependency 5

Machine Learning Prognosis for Smartphone Dependency

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Prathima, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Concern over smartphone addiction has grown recently as more and more people show signs of addiction, including compulsive phone use, decreased productivity, and even physical and mental health issues. Consequently, a growing necessity exists for the creation of efficient tools to forecast smartphone addiction and pinpoint individuals susceptible to this behavior. The proposed model is crafted to predict smartphone addiction using data collected from a survey of individuals who use smartphones. The survey encompassed inquiries related to demographics, phone use patterns, and various psychological aspects like anxiety, depression, and stress. a popular and effective machine learning method, to build our model. After pre-processing the data, the model is trained and its performance assessed by its accuracy, to see whether the model could be learned efficiently. Our findings demonstrated how effective the recommended treatment was at preventing smartphone addiction. Key features crucial for predicting addiction included patterns of phone use, such as the frequency of checking notifications, daily hours spent on the phone, and the predominant types of frequently used apps. Other important factors included age, gender, and stress levels. Our designed model has multiple possible uses. The solution finds its usage by healthcare professionals to identify individuals prone to potential risks of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps that are less addictive and promote healthier phone use habits. © 2024 IEEE.

关键词： addiction healthcare machine learning prediction smart phone

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Data-Driven Decisions: A Machine Learning Perspective on Loan Authorization 6

Data-Driven Decisions: A Machine Learning Perspective on Loa...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Julian, Anitha Booshanram, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306613

The banking sectors are provided with many aspects for their operations and act as the interface for customer requests. The main objective of banks is to invest their assets in safe customers. Nowadays banks are ready to provide loans for specific customers and expect certain requirements to approve the loan requested by the customers. They implement various techniques to select the right customer. Technology had grown enough to handle such complicated tasks more easily. Machine learning technology helps to cope with this. The proposed solution works with machine learning models like Decision Trees, Logistic Regression, Linear Discriminant Analysis, Support Vector classifiers, Random Forest, K-nearest neighbors, and Naive Bayes. These algorithms help predict results by choosing the algorithm that exhibits the best accuracy. The proposed work experiments with classification models with different dataset sizes, building up a model with a greater number of data samples and a smaller number of data samples, and checking their accuracy scores as well. Applying the best option to the loan approval procedure is possible. © 2024 IEEE.

关键词： accuracy bank loans dataset different sizes linear discriminant analysis loan prediction safe customers

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A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN 3

A Deep Learning Approach to Improve Pulmonary Cancer Diagnos...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sudar, K. Muthamil Nagaraj, P. Raghavendra, V. Jaashvitha, V. Jayasri, Y. Reddy, Yerva Dasaradha Ramanjaneya Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India

ISBN: (纸本)9798350361186

It has been proposed that physicians use machine learning-based lung cancer prediction algorithms to treat indeterminate pulmonary nodules that are discovered by accident or during screening. These methods might improve decisionmaking, decrease variability in nodule classification, and decrease the proportion of benign nodules that require unnecessary monitoring or investigation. In this work, we summarise the principal lung cancer prediction methods that have been put out so far and discuss some of the advantages and disadvantages of each. We discuss some of the challenges in creating and verifying such methods and outline the path to clinical acceptance. To ensure the best possible quality of input data for the deep learning models, the preprocessing step involves anatomical area segmentation, noise reduction, and image enhancement. The process of feature extraction involves using convolutional neural networks (CNNs) to extract intricate patterns and features from the lung pictures, which are then utilized to diagnose benign and malignant tumors. Typically, a deep neural network or an ensemble of models is used as the classification network to process this information and determine the final diagnosis. A faster and better outcome for patients may result from the recommended pulmonary cancer detection system's ability to assist medical professionals in accurately identifying malignant tumors in lung imaging. Deep learning is projected to be used in lung cancer detection, which will eventually result in earlier and more effective treatment, hence reducing the burden of this fatal condition. © 2024 IEEE.

关键词： Classification (of information)

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OFFLINE REINFORCEMENT LEARNING WITH DIFFERENTIABLE FUNCTION APPROXIMATION IS PROVABLY EFFICIENT 11

OFFLINE REINFORCEMENT LEARNING WITH DIFFERENTIABLE FUNCTION ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Yin, Ming Wang, Mengdi Wang, Yu-Xiang Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering Princeton University United States

Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life ***-Of-The-Art algorithms usually leverage powerful function approximators (*** networks) to alleviate the sample complexity hurdle for better empirical *** the successes, a more systematic understanding of the statistical complexity for function approximation remains *** bridging the gap, we take a step by considering offline reinforcement learning with differentiable function class approximation (DFA).This function class naturally incorporates a wide range of models with nonlinear/nonconvex *** show offline RL with differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning (PFQL) algorithm, and our results provide the theoretical basis for understanding a variety of practical heuristics that rely on Fitted Q-Iteration style *** addition, we further improve our guarantee with a tighter instance-dependent *** hope our work could draw interest in studying reinforcement learning with differentiable function approximation beyond the scope of current research. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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In-Group Bias in Deep Learning-Based Face Recognition Models Due to Ethnicity and Age

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2023年第1期4卷 54-67页

作者： Nagpal, Shruti Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini K. Ratha Indraprastha Institute of Information Technology Delhi Department of Computer Science and Engineering New Delhi110020 India Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur342011 India University at Buffalo Department of Computer Science and Engineering BuffaloNY14260 United States

Humans are known to favor other individuals who exist in similar groups as them, exhibiting biased behavior, which is termed as in-group bias. The groups could be formed on the basis of ethnicity, age, or even a favorite sports team. Taking cues from aforementioned observation, we inspect if deep learning networks also mimic this human behavior, and are affected by in-group and out-group biases. In this first of its kind research, the behavior of face recognition models is evaluated to understand if similar to humans, models also encode group-specific features for face recognition, along with where bias is encoded in these models. Analysis has been performed for two use-cases of bias: age and ethnicity in face recognition models. Thorough experimental evaluation leads us to several insights: (i) deep learning models focus on different facial regions for different ethnic groups and age groups, and (ii) large variation in face verification performance is also observed across different sub-groups for both known and our own trained deep networks. Based on the observations, a novel bias index is presented for evaluating a trained model's level of bias. We believe that a better understanding of how deep learning models work and encode bias, along with the proposed bias index would enable researchers to address the challenge of bias in AI, and develop more robust and fairer algorithms for mitigating bias as well as developing fairer models. © 2020 IEEE.

关键词： Face recognition

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Revolutionizing E-Commerce Security: A Secured Infrastructure Utilizing BlockChain 15

Revolutionizing E-Commerce Security: A Secured Infrastructur...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Ashish Yadav, Harsh Sharma, Shanu ABES Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350370249

With the increase in technological advancement in the world, there is a huge shift going on as everyone wants to shift online and have a broader market available to them. E-commerce websites are a platform where a lot of orders and transactions are being made daily. Nowadays the market is shifting to an e-commerce platform for a bigger market and profit their business. With this technical advancement, there comes a threat to the information and data that are available, since most of the transactions are being recorded online. Thus, there is always a concern related to the security of user data, which can also be resolved using blockchain. Blockchain Technology which was proposed by Satoshi Nakamoto as a decentralized currency component of bitcoin technology. It also provides security to store data as it is immutable in nature. The main idea is that all the data of the user will be stored on blockchain so that it cannot be breached and all the information about the user remains preserved. In this article, a preliminary analysis is performed on existing work to understand the different types of security issues in the E-Commerce Sector. Further, the applicability of Blockchain is explored to provide a more secure infrastructure for the users and administration using E-Commerce services. To showcase the applicability of blockchain, here an E-Commerce platform is also developed using ShopKart, where the product information and transaction information is kept secure using Smart Contract in Blockchain. Furthermore, the cryptocurrency-based payment gateway is also introduced for the worldwide acceptability of payments. © 2024 IEEE.

关键词： Smart contract

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SemanticCuetSync at AraFinNLP2024: Classification of Cross-Dialect Intent in the Banking Domain using Transformers 2

SemanticCuetSync at AraFinNLP2024: Classification of Cross-D...

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2nd Arabic Natural Language Processing Conference, ArabicNLP 2024

作者： Paran, Ashraful Islam Shohan, Symom Hossain Hossain, Md. Sajjad Hossain, Jawad Ahsan, Shawly Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891761322

Intention detection is a crucial aspect of natural language understanding (NLU), focusing on identifying the primary objective underlying user input. In this work, we present a transformer-based method that excels in determining the intent of Arabic text within the banking domain. We explored several machine learning (ML), deep learning (DL), and transformer-based models on an Arabic banking dataset for intent detection. Our findings underscore the challenges that traditional ML and DL models face in understanding the nuances of various Arabic dialects, leading to subpar performance in intent detection. However, the transformer-based methods, designed to tackle such complexities, significantly outperformed the other models in classifying intent across different Arabic dialects. Notably, the AraBERTv2 model achieved the highest micro F1 score of 82.08% in ArBanking77 dataset, a testament to its effectiveness in this context. This achievement, which contributed to our work being ranked 5th in the shared task, AraFinNLP2024, highlights the importance of developing models that can effectively handle the intricacies of Arabic language processing and intent detection. ©2024 Association for Computational Linguistics.

关键词： Semantics

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