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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics

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IEEE Transactions on Emerging Topics in computing 2024年 1-15页

作者： Chu, Yun-Wei Hosseinalipour, Seyyedali Tenorio, Elizabeth Cruz, Laura Douglas, Kerrie Lan, Andrew S. Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University IN USA NY USA Department of Engineering Education University of Florida FL USA School of Engineering Education Purdue University IN USA Manning College of Information and Computer Sciences University of Massachusetts Amherst MA USA

Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology that optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta- gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. The evaluation of the proposed methodology considers case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks, as evidenced by an increased average prediction quality and decreased variance across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns clustered into different student subgroups, consistent with the performance enhancements we obtain over the baselines. IEEE

关键词： Students

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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ForeScanGuard: Proactive Monitoring and Detection for Sustainable Forest Conservation 7

ForeScanGuard: Proactive Monitoring and Detection for Sustai...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Krishnan, R. Santhana Balamurugan, S. Rekha, M. Jose, R. Arul Haribabu, K. Muthu, A. Essaki Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Velammal College of Engineering And Technology Department of Mathematics Madurai India P.S.R.R College of Engineering Department of Computer Science And Engineering Sivakasi India RajaRajeswari College of Engineering Department of Electrical And Electronics Engineering Bangalore India Scad College of Engineering And Technology Department of Mechanical Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

In response to the increasing environmental threats, ForeScanGuard emerges as a vital forest monitoring system. Integrating advanced sensors and a proactive anomaly detection algorithm, it employs a 5-channel fire sensor for early fire detection, ultrasonic and vibration sensors for identifying tree cutting activities, and a BMP120 sensor to confirm tree falls. GPS modules attached to trees allow real-time location tracking, transmitted via Wi-Fi to a cloud server. The ForeScanGuard Algorithm analyzes sensor data for anomalies like fire outbreaks, illegal logging, and tree falls. Its proactive alerts facilitate rapid responses from forest officials, ensuring effective forest management. ForeScanGuard, with multiple sensors and cloud-based analytics, represents a significant advancement in smart forest monitoring, promoting conservation efforts through real-time anomaly detection and informed decision-making. This comprehensive solution enhances ecosystem protection, fostering a sustainable and proactive approach to forest conservation. © 2024 IEEE.

关键词： Deforestation

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Optimized Machine Learning-Based Intrusion Detection System for Internet of Vehicles

Optimized Machine Learning-Based Intrusion Detection System ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Limouchi, Elnaz Chan, Francois Royal Military College of Canada Department of Electrical and Computer Engineering KingstonON Canada

ISBN: (纸本)9781665430654

Internet of Vehicles (IoV) represents the application of Internet of Things (IoT) within vehicular communication environments. Internet of vehicles refers to a network of interconnected sensors, network layers, and communication systems that enable vehicles to connect with everything (V2X communication). Io V networks face numerous security challenges due to the emergence of modern types of attacks with unusual patterns. Therefore, it is a crucial and demanding task to design intelligent Intrusion Detection Systems (IDSs) for Io V networks. In this paper, we propose an optimized Machine Learning-based IDS to detect attacks in Io V networks. We deploy highly efficient Ma-chine Learning models, Light Gradient Boosting Machine, Extra Trees Classifier, and Extreme Gradient Boosting, to detect attacks in the CICDDoS2019 dataset. We apply the Synthetic Minority Oversampling Technique to resolve the issue of imbalanced data distribution of target class. A Correlation-based Feature Selection is conducted to reduce the computational cost by decreasing the number of input variables. In order to enhance the performance of the attack detection, hyperparameters are optimized using the Bayesian Optimization algorithm. The performance evaluation results show that these ML models perform well. Notably, the Extreme Gradient Boosting classifier outperforms other Machine Learning models, and our proposed solution outperforms existing systems in terms of Accuracy score. © 2023 IEEE.

关键词： Bayesian Optimization Extra Trees Classification Intrusion Detection System IoV LightGBM Machine Learning PyCaret XGboost

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Machine Learning Technique to Detect Radiations in the Brain

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computer Systems Science & engineering 2022年第7期42卷 149-163页

作者： E.Gothai A.Baseera P.Prabu K.Venkatachalam K.Saravanan S.SathishKumar Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)BangaloreIndia Department of Computer Science and Engineering Erode Sengunthar Engineering CollegeThudupathi638057 Department of EEE M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia

The brain of humans and other organisms is affected in various ways through the electromagneticﬁeld(EMF)radiations generated by mobile phones and cell phone *** variations in the brain are caused by the neurological changes due to the revelation of *** level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell *** this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of ***,Drosophila melanogaster acts as a specimen under the revelation of *** segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of *** geometrical characteristics of the brain image of that is microscopic segmented are *** results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing *** important prejudicial characteristics are given to four varieties of classiﬁers such as naïve Bayes,artiﬁcial neural network,support vector machine,and unsystematic forest for the classiﬁcation of open or nonopen micro-scopic image of *** *** results are attained through various experimental evaluations,and the said classiﬁers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection *** proposed system is an optimal approach that automatically identiﬁes the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.

关键词： Electromagneticﬁeld radiations brain morphology segmentation machine learning image processing

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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Real-Time Traffic Flow Optimization using Adaptive IoT and Data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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Recovery of Missing Radar Returns with Compressive Sensing

Recovery of Missing Radar Returns with Compressive Sensing

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Jouny, Ismail Lafayette College Department of Electrical and Computer Engineering EastonPA United States

ISBN: (纸本)9781665442282

Missing backscatter returns when using stepped-frequency radar are recovered using compressive sensing techniques. The recovered return is then used for target recognition. Compressive sensing-based signal reconstruction methods permit the recovery of the target's high range resolution profile, and consequently identifying the identity of unknow non-cooperative targets. The approach is first tested on synthetic targets, then on real commercial aircraft models with returns that could be contaminated with additive noise. © 2023 IEEE.

关键词： Compressed sensing

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