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Continuous Monitoring of Web Server Assaults Using Machine Learning 2

Continuous Monitoring of Web Server Assaults Using Machine L...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Vishali, M. Mirudhula, A. Priya, A. Iswarya, M. Subramanian, Kavitha Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Because of the widespread accessibility of the weband its ease of use, every organisation nowadays uses web apps to expand its company. With the growth of Due to growing online usage, attack riskhas grown as well. An effective monitoring tools that can recognise these threats Timely is necessary. Java script, PHP, and HTML are utilisedfor website development and database use of SQL management in depth. most typical web server assaults include XSS, DOS (Denial of Service), and SQL injection Cascading Style Sheets. IDS with rules work with keywords and patterns but fail to recognise unidentified assaults. In this article, ML based model is used for leveraging web server logsto identify intrusions. It identifies the sort of assault as well as if a given log is normal or malicious. By establishing a private network with the aid of an Apache WAMP server, web server logs are produced and gathered. © 2024 IEEE.

关键词： Machine learning

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A Deep Learning Approach to Ringworm and Vitiligo Identification in Skin Imagery 5

A Deep Learning Approach to Ringworm and Vitiligo Identifica...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kamasani, Kavya Venna, Deepa Badavathula, Harshitha Ranga, Namratha Velagapudi Ramakrishna Siddhartha Engineering College Dept of Computer Science & Engineering Vijaywada India

ISBN: (纸本)9798331504403

Diagnosing skin illnesses such as vitiligo and ringworm can be difficult because of their similarity to other dermatological conditions and their different looks. The suggested approach combines Local Binary Patterns and Grey Level Co-occurrence Matrix, two potent feature extraction techniques. While Local Binary Patterns depicts the local texture patterns inside an image, Grey Level Co-occurrence Matrix analyses the spatial relationships between pixels to collect textural information. These traits are successfully used to depict the feature extraction with the unique qualities of vitiligo and ringworm lesions with accuracy 98%, having been retrieved from dermatological photographs. However, previous machine learning research produced inaccurate results. Then, for classification we tried different deep learning models like Google Net, Mobile Net, ResNet50, ResNet101, Xception, CNN, and VGG19. Among these, we found that VGG19 provided good results for us with an accuracy of about 99.19%. The pre-trained VGG19 model is improved by utilising deep learning and the extracted characteristics to reliably identify and categorise vitiligo and ringworm lesions. The model's discriminative power is increased by integrating Grey Level Co-occurrence Matrix and Local Binary Patterns features, and it maintains strong classification performance with less training data by utilising VGG19. All things considered, the suggested method presents a User Interface for the automated diagnosis of vitiligo and ringworm, giving medical practitioners a useful instrument for early identification and treatment, eventually enhancing patient outcomes and care quality. © 2024 IEEE.

关键词： Local binary pattern

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

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Ocularis Enigma - Redefining Ocular Disease Detection using Ensemble CNN Innovations 15

Ocularis Enigma - Redefining Ocular Disease Detection using ...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rekha, Baanu S. Shanmugam, Pavadharni Poorni, R. Dept.of Computer Science and Engineering Sri Venkateswara College of Engineering Kanchipuram India

ISBN: (纸本)9798331300579

The Ocular diseases are progressive and patients are unaware they have a disease it is better to recognize it and cure it before it is too *** earlier proposed model aims to use a single CNN architecture such as DenseNet121, VGG19, EfficientNet etc… which achieved less *** proposed model aims to develop an automated system based on deep learning that could aid in earlyocular disease *** proposed model can detect Glaucoma, Cataracts and Diabetic Retinopathy along with the normal eye *** utilizes contrast-limited adaptive histogram equalization (CLAHE) for pre-processing the fundus image, while a CNN classifier is used for the diagnosis of the *** enables the usage of multi-label classes to analyze coloured fundus *** of various CNN models is applied to find the model with the highest accuracy. © Grenze Scientific Society, 2024.

关键词： Deep learning

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BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Semantic Segmentation of X-Ray Images of Human Hand Bone 3

BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Seman...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Venkatesh Nagaraju, Y. Darshan, D. Sahanashree, K.J. Nagamani, P.N. Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Multiclass segmentation of hand bones in X-ray images is crucial for various medical applications, including diagnostic assistance and surgical planning. This study presents BoneSegNet, a deep learning-based segmentation model designed to classify hand bones into seven distinct anatomical classes: Distal phalanges, Intermediate phalanges, Proximal phalanges, Metacarpals, Carpals, Ulna, and Radius. Our model, BoneSegNet leverages the strengths of both convolutional neural networks (CNNs) and transformer encoders incorporating the UNet architecture. Our model architecture begins with patch and positional embeddings of the input image, followed by a series of transformer encoder layers to capture long-range dependencies and contextual information. The transformer encoder is equipped with multi-head attention mechanisms and multi-layer perceptron's (MLPs) with GELU activation and dropout for regularization. Skip connections at specific layers are stored and later utilized in the decoder. The decoding path consists of a series of deconvolution and convolution blocks designed to progressively upscale the feature maps while incorporating skip connections from the encoder. This hierarchical structure ensures detailed spatial information is preserved and enhanced. The final output layer employs a 1x1 convolution with a sigmoid activation function to produce the multiclass segmentation map. The model is trained and evaluated on a dataset of hand X-ray images. It achieves an average accuracy of 99.25%, an average Dice score of 0.81, an average mIoU score of 0.70, sensitivity of 0.79, and specificity of 0.997. These results demonstrate the model's effectiveness in accurately segmenting and classifying hand bones, making it a valuable tool for medical professionals. © 2024 IEEE.

关键词： Semantic Segmentation

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Predictive Analytics in Cardiovascular and Diabetes Risk: An Integrated Machine Learning Approach 8

Predictive Analytics in Cardiovascular and Diabetes Risk: An...

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

作者： Pawar, Hardik Hiraman Sathish, Karan Suma, B. R.V. College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Traditionally, diagnosing cardiovascular disease and diabetes relied on methods that might miss hidden risk factors. This machine learning model tackles that by analyzing vast datasets of patient health information. It considers features like blood pressure, cholesterol, and family history to uncover how they affect disease risk. This empowers both doctors and patients. Healthcare professionals can leverage this information to pinpoint individuals at higher risk and implement strategies for prevention. Patients can gain essential insights into their personal risk profiles and adopt proactive measures for a healthier lifestyle. Unlike basic methods, this model employs machine learning algorithms to manage complex interactions among various health factors affecting disease progression. It also processes data much more quickly compared to traditional risk assessment methods. Central to this model is a neural network, a computational approach that learns how factors such as blood pressure impact disease risk and the intensity of these relationships. The model's integration into a user-friendly web interface allows doctors to easily input patient data and receive clear risk assessment reports. By leveraging this machine learning approach, healthcare professionals can make data-driven decisions for improved patient care and disease prevention. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models 5

Ensemble-Based Transfer Learning for Multi-Class Plant Disea...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Lakshmi, Popuri Mohana Siva Lakshminadh, K. Reddy, K.V. Narasimha Reddy, Dodda Venkata Narasaraopeta Engineering College Dept of Computer Science and Engineering Narasaraopeta India Narasaraopeta Engineering College Dept of Information Technology Narasaraopeta India

ISBN: (纸本)9798331518097

In the world, plant diseases pose a serious threat to agricultural productivity and food security. Early, accurate, and rapid identification of plant diseases is important for con-trolling loss of crops. In the following research, transfer learning models VGG16, ResNet50, and Xception are applied to attempt overcoming this challenge of multiclass plant disease detection. To improve classification accuracy, we propose an ensemble model that combines the strengths of these pre-trained networks. Multiple plant species and disease categories were experimented on extensively on publicly available plant disease datasets. The results show that ensemble model achieves better precision, precision and recall than individual models and therefore presents a robust solution for identifying several plant diseases together as a pack. Results from the experiment demonstrate that the proposed method could be deployed in real-time agricultural systems and have potential to provide a scalable and efficient diagnostic tool for farmers and agronomists to detect plant diseases and reduce their impact. This work is among the growing body of work in AI based agricultural solutions and indicates that transfer learning and ensemble techniques are promising in precision farming. © 2024 IEEE.

关键词： Plant diseases

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SFNet: Synergistic Fusion Network for Text Localization in Natural Scene Images 3

SFNet: Synergistic Fusion Network for Text Localization in N...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Suman, Suman Champa, H.N. University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

Prior methodologies for the detection of scene text have demonstrated promising outcomes across many evaluation metrics. Nevertheless, despite the utilization of deep neural network models, their effectiveness is sometimes limited in complicated situations due to the intricate interplay of several stages and components within the pipelines, which ultimately governs overall performance. This study presents a straightforward yet robust methodology for identifying textual content in practical environments. Phrases or text lines may be predicted in images of varying orientations and quadrilateral forms using the suggested pipeline's single neural network. This eliminates the need for superfluous intermediary steps such as candidate aggregation and word division. Due to the streamlined nature of our SFNet(Synergistic Fusion Network), our primary emphasis is directed at the development of loss functions and neural network design. The suggested approach has been evaluated using a well-established dataset including ICDAR 2015. The results of these experiments demonstrate that the new technique outperforms existing techniques in terms of both accuracy and efficiency. The proposed methodology demonstrates a performance metric, namely an F-score of 0.89 while operating at a frame rate of 13.2 frames per second and a resolution of 720p using the ICDAR 2015 dataset. © 2024 IEEE.

关键词： Pipelines

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Nirnayaak: Revolutionizing Legal Research 4

Nirnayaak: Revolutionizing Legal Research

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Datta, Soumyajit Chakraborty, Arya Bhattacharya, Saptarshi Samui, Amit Chakraborty, Anurag Dasgupta, Kousik Kalyani Government Engineering College Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350389128

Nirnayaak is a novel and advanced legal software application, that is meticulously designed to expedite the retrieval of crucial legal information in an Indian Context, addressing the challenges posed by a growing caseload and inaccuracies in the legal system. Employing a groundbreaking architectural framework, it leverages OCR, Smart Text preprocessing, keyword extraction, a sophisticated LLM-based summarization and information retrieval and a feedback-based keyword ranking technique. This amalgamation of techniques enhances the accuracy and accessibility of legal data, enabling legal professionals, scholars, and the public to access pertinent information seamlessly just using natural language queries. We conducted benchmarks on popular keyword extraction techniques, demonstrating that our preprocessing techniques enhance their performance. Additionally, we evaluated LLMs to ascertain their proficiency in understanding contextual nuances. © 2024 IEEE.

关键词： Application programs

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A Comprehensive Approach to Song Popularity Forecasting 2

A Comprehensive Approach to Song Popularity Forecasting

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Kishore, N. Santhiya, P. Mohamed Askar Asib, M. Kirubaaharan, S. Kongu Engineering College Dept. Computer Science India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Assessing the potential success of a song based on its acoustic characteristics is a crucial task within the music industry. Traditionally, this has been approached by leveraging audio features independently. In this study, we take a novel approach by jointly exploiting audio features, framing as a regression on prediction task. Our methodology employs a wide architecture, enabling a comprehensive analysis. Additionally, we enhance feature sets by incorporating information about track release years. Evaluation on the Million Song Dataset, using Billboard Hot 100 inclusion as a metric for song success, reveals that our approach surpasses baseline methods and those utilizing low- or high-level features in isolation. Notably, the inclusion of release year alongside mood and vocal features further enhances predictive accuracy, highlighting the efficacy of our comprehensive model in gauging song popularity. © 2024 IEEE.

关键词： Music

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