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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Santhosh, B.J. Adithya, H.R. Akash Kumar, G.S. Hemanth Gowda, R.M. Jaswanth, H.V. BGS Institute of Technology Adichunchanagiri University India 20CSE001 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE004 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE024 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE032 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri university India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Pests are one of the biggest hazards to crops since they significantly reduce the amount of food produced. Furthermore, by enabling farmers to take the necessary preventive measures, early and accurate pest identification can help minimize damage and improve crop quality. Inspection takes a lot of time and labour because many pest species seem to be identical to one another. This work presents a novel deep-learning technique called Faster-Pest Net, which aims to overcome the limitations of physical pest inspection. Using the Mobile Net as its foundation network, adescriptively enhanced Faster-RCNN technique is created, calibrated on pest samples to identify crop pests across many categories, and dubbed Faster- PestNet. ©2024 IEEE.

关键词： Adversarial machine learning

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An Ensemble-Based Approach for Cyber Attack Detection in Financial Systems 5th

An Ensemble-Based Approach for Cyber Attack Detection in Fin...

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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Dhanisht Srivastava, Harshit Bhamare, Kartik S. Kumar, Malay Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad India

ISBN: (纸本)9783031609343

Digital commerce is the process of exchanging money or assets using electronic or digital channels such as the internet or mobile devices. Instead of physical cash or checks, individuals or businesses can use digital platforms to transfer money, make payments or conduct financial transactions securely and efficiently. As more and more people conduct financial transactions online, the risk of cyberattacks targeting financial institutions will increase. This article presents an integrated approach to detecting cyber-attacks in the financial sector. The proposed method combines learning models including decision trees, random forests, and gradient boosting to increase the accuracy and robustness of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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A Survey on Data Security using Reversible Cellular Automata

A Survey on Data Security using Reversible Cellular Automata

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Degala, Divya Priya Athithan, Senthil Chinnasamy, P. KL University Department of Computer Science and Engineering Vijayawada India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350348217

Now a days, without network and advanced computer technologies we can't able to transfer data among users. Among this the security is the biggest issue with the CPS system. The major issue is figuring out how to increase these systems' security while still doing it effectively. In order to overcome the issues this survey proposes several techniques for providing security of cyber-physical systems using reversible computing and cellular automata. A Cyber-Physical System(CPS) is an intelligent system used for data storing, transferring and protecting. To protect data, many machine(ml) and deep learning(dl) algorithms have been designed, which require more computation, dissipating more *** solve this, we are offering an public key encryption using reversible cellular automata to enhance the security of data in Cyber-Physical Systems © 2023 IEEE.

关键词： Cyber Physical System

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The Potential of 1D-CNN for EEG Mental Attention State Detection 5th

The Potential of 1D-CNN for EEG Mental Attention State Det...

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5th International Conference on Machine Learning, Image Processing, Network Security, and Data sciences, MIND 2023

作者： Velaga, NandaKiran Singh, Deepak Department of Computer Science and Engineering National Institute of Technology Raipur CG Raipur India

ISBN: (纸本)9783031622168

In cognitive neuroscience, attention detection via electroencephalogram (EEG) signals is a crucial task, especially when looking into brain-computer interfaces (BCIs). However, the current biggest challenge in BCI research is the accurate classification of EEG signals. The old methods, such as visual assessment, were neither standardised or statistically analysed. Because of these limitations, many techniques have been created to quantify and extract meaningful data from EEG signals. It has been shown that machine learning algorithms can reliably classify attention states using EEG data. Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) are two examples of such methods. However, more study is required to ensure these algorithms are as precise and reliable as possible. In light of the challenges presented by EEG signal classification, we have employed state-of-the-art deep learning algorithms to enhance the identification of mental attention states. Specifically, we have used a sophisticated deep learning model that integrates spatial dependency analysis, namely 1D Convolutional Neural Networks (CNN). By leveraging the power of 1D CNN, our approach is able to capture intricate spatial patterns within EEG signals, allowing for robust feature extraction and improved classification accuracy. Our model has undergone rigorous training on a diverse dataset comprising 34 EEG signals from 5 participants across different conditions. The model successfully differentiates between distinct mental states (focused, unfocused, drowsy) by analysing EEG data gathered in the experimental setting, with an impressive accuracy of 98.47% and a loss of 0.0144. This study underscores the importance of leveraging advanced computational approaches to unlock the valuable information contained within EEG signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electroencephalography

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Analysis of Bio Inspired Based Hybrid Learning Model for Software Defect Prediction

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SN computer science 2024年第7期5卷 1-22页

作者： Shankar, Sahana P. Chaudhari, Shilpa Shashikant Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University Belgaum) Bengaluru India Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

The software’s quality can be ensured through software testing, which is one of the critical methods. However, it was found that testing consumes more than half of the project's total expenses. Effective and efficient software testing utilizes minimal software resources to find as many flaws in the software system as possible. This paper presents research on software defect prediction using hybrid machine learning algorithm. The algorithm incorporates the benefits of supervised as well as unsupervised learning methodology that improves the accuracy of defect prediction. The study uses publicly available datasets from the open source PROMISE repository provided by NASA MDP to perform machine learning analysis and maximize accuracy. In particular, the study suggested a hybrid learning model and conducted a comparative analysis among Naïve Bayes, Multilayer Perceptron, Random Forest, CNN, Adaboost, and KNN on four different datasets namely CM1, KC1, KC2 and PC1. With respect to F1-score, accuracy, precision, and recall, the results show that the hybrid method performed better than the other methods. The study achieved an accuracy of 93.67% (BAT + CNN) on PC1 dataset, 92% on (Bat + CNN) for CM1 and KC1 respectively. This indicates that the hybrid learning technique of (BAT + CNN) is more potent than other methodologies in prediction of defects. The research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. One useful approach for predicting software defects is the hybrid machine learning method, which can lead to improved software quality and lower maintenance costs. This aids the software testing phase by predicting the faulty modules with better accuracy thereby reducing the testing effort. The main objectives of the research include applying different machine learning algorithms and methods to the given datasets and determine the most effective ones. © The Author(s), under exclusive licence to

关键词： Bat CNN PROMISE Sparrow Search Squirrel Search Testing

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Refining of Learning in Human Decision Making Models: A Simple Step towards Machine Unlearning

Refining of Learning in Human Decision Making Models: A Simp...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Shrivastava, Alekh Ahirwal, Mitul Kumar Maulana Azad National Institute of technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350364088

In this paper the challenge of human decision-making (HDM) modeling by gambling task is targeted, specifically focusing on the Iowa Gambling Task (IGT). Traditional models train on data generated during gameplay, where decisions are made by the subject, although at times these decisions are random, leading to the model's inadequate understanding of natural human behavior. Here, we proposed a novel approach by utilizing realtime data from the IGT to model the HDM process involving uncertainty, risk, and ambiguity. Methodology in this study incorporates decay reinforcement learning for expectancy value updates, a prospect utility function for calculating utility values, and a time-dependent choice rule to determine the deck selection in the next trial. Additionally, mean square deviation (MSD) is employed to measure the disparity between actual and simulated data. By imposing restrictions on the continuous selection of decks, particularly limiting the number of consecutive selections, is a way towards concept of unlearning through the faulty pattern in data. The significance of this research lies in its ability to mitigate the impact of random deck selections during model training. By imposing constraints and aligning the model with more realistic human decision patterns, our approach offers a refined and accurate representation of human decision-making in gambling tasks. © 2024 IEEE.

关键词： Reinforcement learning

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A Hybrid Text-Image Response System for Enhanced Customer Interaction Using Advanced Deep Learning Models 4

A Hybrid Text-Image Response System for Enhanced Customer In...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Ashok, P. Pathmanaban, J. Rajadurai, S. Yuvaraj, B. Bharathraj, S.M. Nawaz, S. M. Mustafa Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

In recent times, the integration of chatbots into customer interaction systems has transformed the way businesses engage with their customers. As businesses worldwide seek to implement chatbot systems to improve customer relationships, the evolution of these chatbots extends beyond text-based capabilities. With advancements such as Google's Gemini pro and OpenAI's GPT-4, chatbots now possess the ability to comprehend image-related queries. However, the challenge lies in developing interactive chatbots tailored to specific needs, capable of providing responses not only in text but also with relevant images - a feature that can significantly enhance problem-solving for small businesses. This paper proposes an innovative system designed to address user queries using both textual responses and pertinent images. Drawing inspiration from recent research, we present a novel approach that leverages the power of Lang chain, Meta's Llama-2-7b chat model, coupled with sentence transformers all-MiniLM-L6-v2 model for mapping the right image for the user query. This amalgamation of advanced models aims to create a sophisticated chatbot system adept at delivering informative responses enriched with both text and visual content. Our system represents a significant step forward in customer interaction, offering a versatile solution applicable across diverse domains, including customer service, education, and research. © 2024 IEEE.

关键词： all-MiniLM-L6-v2 model Chatbots Hugging-Face Sentence transformers Image-Based Data Innovative Approach Interactive Chatbot Large-Language Models Llama-2-7b chat model Multimodal Interaction Natural Language Processing Textual Conversations User Queries

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Recommendation System Using Neural Collaborative Filtering and Deep Learning 5th

Recommendation System Using Neural Collaborative Filtering a...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Shah, Vaibhav Anunay Kumar, Praveen Department of Computer Science and Engineering Parul University Vadodara India Department of Computer Science and Engineering Indian Institute of Technology Chennai India

ISBN: (纸本)9789819906000

Recommender systems have transformed the nature of the online service experience due to their quick growth and widespread use. In today’s world, the recommendation system plays a very vital role. At every point of our life, we use a recommendation system from shopping on Amazon to watching a movie on Netflix. A recommender system bases its predictions, like many machine learning algorithms, on past user behavior. The goal is to specifically forecast user preference for a group of items based on prior usage. The two most well-liked methods for developing recommender systems are collaborative filtering and content-based filtering. Somehow, we were using the traditional methods, named content-based filtering (CB) and collaborative-based filtering (CF), which are lacking behind because of some issues or problems like a cold start and scalability. The approach of this paper is to overcome the problems of CF as well as CB. We built an advanced recommendation system that is built with neural collaborative filtering which uses implicit feedback and finds the accuracy with the help of hit ratio which will be more accurate and efficient than the traditional recommendation system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Learning Automata and Reputation for Task Scheduling in Fog Computing 15

Learning Automata and Reputation for Task Scheduling in Fog ...

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

作者： Rout, Rashmi Ranjan Sarvabhowma, Sanapala Parimi, Priyanka National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

Fog computing suffers from performance degradation in the absence of nodes' trust, due to a large number of fog nodes for various applications. Further, some fog nodes may mislead and pretend to have more resources than they have to gain higher task processing qualifications resulting in a delay in completion of the task. This significantly harms users' interests and has a negative impact on service quality. Therefore, in this work, we propose a reputation-based resource allocation and task scheduling strategy at the fog nodes. A reputation mechanism for the fog nodes has been designed based on the resources provided and processing time for various tasks. A learning automata based strategy has been proposed to schedule the tasks based on the reputation of the fog nodes. The efficacy of the proposed mechanism has been proven through simulation by considering the successful task completion rate. © 2024 IEEE.

关键词： Degradation Schedules Cloud computing Processor scheduling Learning automata computer architecture Delays Resource management Edge computing Qualifications

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Design and Implementation of a 4-Antenna Rx Beamforming System with Real-Time Calibration

Design and Implementation of a 4-Antenna Rx Beamforming Syst...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Lai, Je-An Lin, Shou-Sheu Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (纸本)9798350389210

Real-time calibration for a beamforming receiver is a popular topic. We designed and implemented a 4-antenna Rx beamforming system with real-time calibration. This system sends an out-of-band calibration signal from the transmitter (Tx) side to calibrate the phase error of in-band received signals in real time at the receiver (Rx) side. The system was successfully implemented and validated on the M3force Software Defined Radio (SDR) platforms. It was verified using not only the accuracy of the direction of arrival (DOA) on the beampattern but also the array gain from the error vector magnitude (EVM) of the demodulated signals with beamforming in the receiver. The system achieved 4.60 dB of array gain and 2.9 degrees of the accuracy of DOA with calibration. © 2024 IEEE.

关键词： Direction of arrival

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