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AI Sentries: Evaluating Machine Learning Models for Superior Phishing Email Detection 8

AI Sentries: Evaluating Machine Learning Models for Superior...

8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Sahit, S. Vaishnavi, S. Vaibhav Reddy, A. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

Email Phishing Detection focuses on studying how to process emails and spot threats using machine learning and advanced natural language processing. It aims to shield people and businesses from real email misuse. This study tries to fix some problems that earlier research had faced. It does this by making better machine learning detectors to spot fake emails. These detectors use different data sets and look at how to pull out key info. They also try to adapt to new tricks scammers use. The solutions put forward use new methods. These include tweaking features, combining different learning models, and using deep learning to boost accuracy while checking emails in real-time. The study also looks at how to use natural language processing (NLP) to understand what emails mean. This helps catch more fake emails. By looking at both how emails are written and what they mean, the system can spot scams more. This cuts down on false alarms and makes the system more reliable. This well-rounded approach not pushes the boundaries of spotting fake emails but also creates a strong system that can change as cyber threats do. This ensures protection that lasts and can bounce back from attacks. © 2024 IEEE.

关键词： Phishing

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Detection and Prevention of Tomato Leaf Disease using Convolutional Neural Network and Inception Net 3

Detection and Prevention of Tomato Leaf Disease using Convol...

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

作者： Ajitha, E. Nivedha, V. Parvathi, B. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350361186

A state-of-the-art technique for analyzing images that is still relatively new but yields consistent results is Deep Learning(DL). Many DL techniques are used for leaf disease categorization. Regardless of the cuisine, tomatoes are a staple in every kitchen and are one of the most popular vegetables. It ranks third in terms of widespread production, behind sweet potatoes and potatoes. The number and quality of tomato crops are, however, impacted by several illnesses. An approach to crop disease diagnosis by deep learning is used. For disease categorization and detection, a Convolutional Neural Network is employed. Two pooling and two Convolutional layers are employed inside the model. The suggested model beat pre-trained InceptionV3, and VGGI6, according to the experiment findings. 98% training accuracy and 88.17% testing accuracy were attained by the CNN model. © 2024 IEEE.

关键词： Convolutional neural networks

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Hyperspectral Image Classification based on Cycle GAN and EfficientNet 4

Hyperspectral Image Classification based on Cycle GAN and Ef...

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

作者： Reddy, Tatireddy Subba Anusha, Sheguri Reddy, Enugu Vishnu Vardhan Shivamani, Vuppala Venkata Mrudula, V. B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India

ISBN: (纸本)9798350343670

Because of the existence of narrow contiguous bands, Hyperspectral Remote Sensing is a sophisticated remote sensing technique that improves the capacity of precise categorization. Given the availability of duplicate information, the massive quantity of continuous bands observed in hyperspectral photos raises the processing complexity. The primary data sets have a limited number of labeled pixels;however, we need additional pixels for improved hyperspectral picture categorization. As a result, the main hurdles are dimensionality reduction and a restricted number of labelled samples. To address these issues, we introduced the Kernel PCA approach for dimensionality reduction, the Cycle GAN technique for increasing labelled samples, and the EfficientNet technique for classification. These strategies will be more accurate than existing techniques for hyperspectral picture categorization. © 2024 IEEE.

关键词： Cycle GAN Hyperspectral image Indian Pines Kernal Principal Component Analysis (KPCA) Salinas scene semi-supervised classification University of Pavia

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Classification of Mental Workload Using 1D-CNN with Transfer Learning 1

Classification of Mental Workload Using 1D-CNN with Transfer...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Chaturvedi, Sushil Ahirwal, Mitul Kumar Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350365016

Mental workload (MWL) can be explained as the ratio of a participant's mental effort to their brain capability when performing activities. A significant challenge in the field of MWL classification for brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) is the variability among individuals. It commonly takes unnecessary time to retrain the classification model from starting for a new subject. To address the individual variations among different subjects, a one-dimensional convolutional neural network along with transfer learning (1D CNN-TL) is proposed in this work. The end-to-end 1D-CNN learns the common features of EEG signals and, to tackle the problem of difference in EEG for individuals, a transfer learning technique is used that does fine-tuning on fully connected (FC) layers for a new participant having less training data. The average test accuracy (mean±stdv) of 65.18± 8.37 is achieved over the Cross-session mental workload dataset. The suggested method can achieve satisfying results for new unknown data with less time and fewer training data. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Placement of Radio Frequency Energy Harvester for IoT-Based Applications 8

Optimal Placement of Radio Frequency Energy Harvester for Io...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Binsiya, P.N. Mini, S. National Institute of Technology Goa Department of Computer Science and Engineering Goa Cuncolim403703 India

ISBN: (纸本)9798331531539

The Internet of Things (IoT) is redefining how we interact with our surroundings, from smart home gadgets to crucial industrial applications. The increasing deployment of IoT devices has necessitated efficient and sustainable energy solutions. Harvesting energy from radio frequency (RF) signals has developed as a sustainable way to power IoT devices by converting ambient RF signals into usable electrical energy as we are surrounded by powerful electromagnetic waves in the 5G communication era. Capturing maximum power in RF energy harvesting is a primary challenge due to the low power density of radio frequency energy. Positioning the RF harvester in the ideal location is very important for maximizing the capture of ambient RF waves. A Particle Swarm Optimization (PSO) based method is deployed for the optimal positioning of RF energy harvesters in IoT environment to maximize energy capture and device performance. The PSO algorithm is employed to find near-optimal solutions by simulating particle movements in a multidimensional search space. This optimization considers factors such as RF signal intensity, frequency and the spatial distribution of RF signal sources to determine the optimal locations for deploying energy harvesters. The placement strategy dynamically adjusts to maximize energy harvesting by moving towards the RF source with highest available power while maintaining a balance between coverage and energy harvesting. Results indicate that optimal harvester placement significantly enhances energy efficiency and extends the operational lifespan of IoT networks, thereby contributing to more sustainable and autonomous IoT deployments. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Classification of Brain-MRI Images Using a Stacked-Deep-Network Ensemble Model into Multiple Region-Based Classes 1st

Classification of Brain-MRI Images Using a Stacked-Deep-Netw...

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1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Kumar, Deepika Srivastava, Varun Gupta, Shilpa Jamil, Akhtar Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Science and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Computer Science and Engineering JIMS Engineering Management and Technical Campus Uttar Pradesh Greater Noida India Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9783031823763

Brain tumour besides being lethal can also affect other human organs on a long-term basis if not detected at an early stage. Based on the region of its presence and speed of growth, it can be classified as Glioma, Meningioma. or Pituitary. In the given work, a Stacked Neural Net approach for the classification of brain tumour is presented. The images are first pre-processed using Histogram equalization and One Hot Encoding. Thereby, the improved images are fed into 3 popular pre-trained deep networks viz. Res-Net-50, VGG-16, and Xception as level-0 models in parallel. The features extracted are then fed into a neural network as level 1 learner for the final classification. A publicly available Figshare dataset has been used for testing the performance. The suggested approach has been thoroughly evaluated against its base models using accuracy, precision, and recall metrics and also the performance has been analysed using accuracy and loss curves along with epochs. The suggested model achieved a validation and training accuracy of 97.3% and 98.12% respectively. It has been observed that the suggested model referred here as Stacked-Neural-Net outperforms when compared to the conventional deep learning methods and state-of-the-art methods. An improvement of 3.35%, 1.3% and 1.7% is achieved over Inception ResNetV1, a basic CNN and Alex-Net CNN models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Sleep Disorder Detection Using Deep Learning

Sleep Disorder Detection Using Deep Learning

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Reddy, K. Rakesh Kumar Abinaya, K. Ram, G.L.V. Satya Sai Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331513894

Since sleep disorders can have a major impact on general well-being, classifying them is crucial to enhancing human health. Experts have historically categorized the stages of sleep, but this is a challenging and error-prone process. By more efficiently assessing, tracking, and diagnosing sleep disturbances, accurate machine learning algorithms (MLAs) can be beneficial. This study uses the publicly accessible Sleep Health and Lifestyle Dataset to evaluate deep learning algorithms and traditional MLAs for the categorization of sleep disorders. Thirteen characteristics pertaining to sleep and everyday activities are included in the 400row dataset. A genetic algorithm was employed to adjust the parameters of the machine learning models in order to maximize their performance. The Artificial Neural Network (ANN) method was evaluated in the study. Significant performance differences were found in the results, with the ANN obtaining the best classification accuracy of 92.92%. In addition, it outperformed the other algorithms tested with high precision (92.01%), recall (93.80%), and F1-score (91.93%). © 2025 IEEE.

关键词： Sleep research

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Emotion Classification to Augment the Emotional Intelligence of Autistic Individuals 5

Emotion Classification to Augment the Emotional Intelligence...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Guruvammal, S. Kesavamoorthy, R. Anju, M.I. Chellatamilan, T. Jegatha Deborah, L. Jain University Dept. of Cse Kanakapura India Cmr Institute of Technology Dept. of Cse Bengaluru India Srm Institute of Science and Technology Dept. of Ece Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India University College of Engineering Computer Science and Engineering Tindivanam India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

The collaboration of machine learning (ML) algorithms and cognitive psychological frameworks supports the enhancement of emotional intelligence. In light of Plutchik's emotional wheel, this study investigates how to identify human emotions. By utilizing 24 different emotions, 8 high-intensive (secondary) emotions are identified. We employed 9 different classification algorithms K-nearest neighbors (KNN), decision trees (DT), naïve bayes (NB), support vector machine (SVM), Logistic regression, random forest (RF), artificial neural network (ANN), gradient boosting machines (GBM), and bagging classifier (BC) to identify the emotions accurately and the performance of them are compared. This research uses the labelled dataset consisting of 11 columns as similar word 1, similar word 2, typical sensation 1, typical sensation 2, opposite intensity low, opposite intensity medium, opposite intensity high, tells, helps, emotion and cluster. © 2024 IEEE.

关键词： Random forests

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Advancement in EEG Signal Feature Extraction for Enhanced Seizure Detection and Prediction 8

Advancement in EEG Signal Feature Extraction for Enhanced Se...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Khattar, Sonam Kaur, Ravinder Chandigarh University Department of Computer Science Mohali India Thapar Institute of Engineering and Technology Department of Computer Science Punjab Patiala140413 India

ISBN: (纸本)9798331505462

Epileptic seizure is a highly severe neurological disorder that abruptly interrupts the daily life of those afflicted. In order to advance the technology utilized for managing epileptic seizures, new diagnostic procedures have concentrated on creating electroencephalogram (EEG) techniques that are based on machine learning and deep learning models. The main aim of this research is to focus on some of thecommonly used methods for feature extraction as mentioned in the literature. It will also delve into potential uses of these methods. There are many ways described in this article to process and extract features from EEG signals. Those include time, frequency, decomposing, time-frequency and spatial. Finally, we look at different areas where artificial intelligence is utilized including assistive technology, classification of brain related diseases brain-computer interface systems and integration of machine learning techniques. These applications contribute to a general architecture for analyzing EEG signals. Lastly, potential directions in EEG signal analysis involving feature extraction will be discussed. This study's findings will guide researchers find out which machine learning models that are most effective when it comes to deep learning as well as how feature extraction can be done so that it can enhance detection accuracy of epilepsy seizure based on EEG data. © 2024 IEEE.

关键词： Frequency domain analysis

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Classification of diabetic macular edema severity using deep learning technique

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Research on Biomedical engineering 2022年第3期38卷 977-987页

作者： Kumar, Amit Tewari, Anand Shanker Singh, Jyoti Prakash Department of Computer Science & Engineering National Institute of Technology Patna Patna India

Purpose: Diabetic macular edema (DME) is a kind of hard exudates lesion seen near the diabetic macular region of the retina. DME causes visual loss and may result in complete blindness;early identification and treatment may be able to cure this. Identification of DME at an early stage is a challenging and error-prone task. To address this issue, the article presents a methodology that uses the notion of transfer learning to identify cases of DME from retinal fundus images. Methods: A pre-trained DenseNet121 is used in this technique to extract the useful set of feature vectors from the fundus images, which are then fed into a few additional fully connected layers and then into the classification layer to classify DME instances. A total of 577 fundus training images from 3 DME classes were used to train the proposed model, and 103 fundus testing images were used to verify the proposed model for classifying them into one of the three DME cases. Results: The suggested model is trained and tested on the Indian Diabetic Retinopathy Image Dataset (IDRiD). With the test images, the results demonstrate that the proposed model outperformed the state-of-the-art models presented in "Diabetic Retinopathy – Segmentation and Grading Challenge" held at ISBI-2018 with an accuracy of 86.4%. Conclusion: The proposed model diagnoses DME at an early stage for timely treatment and helps to reduce the workload of ophthalmologists. © 2022, The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering.

关键词： Grading

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