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application and Analysis of Phishing Website Detection in machine learning and Neural Networks 4th

Application and Analysis of Phishing Website Detection in Ma...

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4th international conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Pooja, A. S. S. V. Lakshmi Sridhar, M. Ramesh, G. CSE Department Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana500090 India

ISBN: (纸本)9789811636523

Web Phishing asks the user to connect to a malicious website. the number one goal of this assault is to steal crucial statistics from the user. Intruder builds websites similar to those that look like the original website. It allows attackers to access Sensitive data such as username, password, details of debit cards etc. this paper aims to review many of the phishing detection strategies recently suggested for the website. this will also provide a high-level description of various forms of phishing detection techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Websites

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted machine learning Model

A Robust Development of Calorie Prediction Methodology based...

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Intelligent Technologies (CONIT), international conference on

作者： G Naga Venkata RamaKrishna S Sreelakshmi Meenakshi Diwakar S. Ramkumar T Vinithra Banu T. thilagam Faculty of Engineering University of Technology and Applied Sciences Al Musannah Sultanate of Oman Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of CSE (AI and ML) S.A.Engineering College Chennai Tamil Nadu India Department of Electronics and communication engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering S.A Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Modern people’s hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it’s not hard to keep track of one’s calorie intake. there aren’t many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. this paper presents an innovative ML model for accurate calorie prediction; it’s called the Improved learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we’ll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object’s known volume to determine the food item’s volume. Once the volume has been determined, the food item’s mass may be determined using formulae. then, the connection between weight and calories can be used to compute the food item’s calories.

关键词： Obesity Schedules Accuracy Target recognition Computational modeling Predictive models Feature extraction

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14th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

14th International Conference on Artificial Intelligence in ...

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14th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

ISBN: (纸本)9783031901669

the proceedings contain 28 papers. the special focus in this conference is on Artificial Intelligence in Music, Sound, Art and Design. the topics include: Search-Based Negative Prompt Optimisation for Text-to-Image Generation;Exploring the application of AIGC in Ink-Wash Animation Creation:A Case Study of Dragon Gate;AI in Music and Healthcare: A Comparative Survey;combining Local Search and Directed Mutation in Evolutionary Approaches to 4-Part Harmony;exploiting the Temporal Order of Sound Features for Onset Detection;towards Human-Quality Drum Accompaniment Using Deep Generative Models and Transformers;an Ensemble Approach to Music Source Separation: A Comparative Analysis of Conventional and Hierarchical Stem Separation;balancing Indeterminacy and Structure: Neural Text Generation for Artistic Inspiration;Exploring Bridges Between Algorithmic and AI-Generated Art;future Sight: Fine-Tuning Language Models for Dynamic Story Generation;All YIN No YANG: Geometric Abstraction of Oil Paintings with Trained Models, Noise and Self-reference;exploring Multi-objective Evolution for Aesthetic and Abstract 3D Art;music Similarity through Geometric Overlap;graph Neural Network vs Feature-Based Folk Music Evolution Analysis;Generating Virtual Landscapes and Environmental Narratives with StyleGAN2;towards the Automatic Evaluation of Legibility for Graphic Design Posters;short Video Interestingness: A machine learning Approach to Determine Creative Cues in Audiovisual Production;automated Selection and Ordering of Clip Sequences for Music Videos Based on Tonal Tension and Visual Features;evolving the Embedding Space of Diffusion Models in the Field of Visual Arts;steering Large Text-to-Image Model for Kandinsky Synthesis through Preference-Based Prompt Optimization;emotioNotes Dataset: Decoding Emotions in Classical Music through Concert Program Notes;aesthetic Biases and Opacity Tactics in the Training of Visual Artificial Intelligence Models;yin-Yang: Developing Motifs with L

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Review of machine learning Methods for the Survey on HCC Scenario and Prediction Strategy 4

Review of Machine Learning Methods for the Survey on HCC Sce...

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4th international conference on Trends in Electronics and Informatics, ICOEI 2020

作者： Gogi, Vyshali J. Vijayalakshmi, M.N. Rv College of Engineering Department of Master of Computer Application Bangalore Karnataka India

ISBN: (纸本)9781728155180

Liver Cancer is one of the serious disorders which when not diagnosed on time may lead to death. Hepatocellular Carcinoma (HCC) is malignant tumor which needs to be treated with priority. HCC is diagnosed generally when it reaches more progressive stage. HCC is diagnosed starting with the clinical trials, laboratory studies, tumor grading, imaging studies depending on the symptoms exhibited by the patient. Medical data contains massive hidden parameters which can be analyzed using machine learning algorithms. the current work focusses on study of various previous studies carried out in prediction of HCC using machine learning techniques like classification, regression and artificial neural networks. © 2020 IEEE.

关键词： Data mining

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Random Walker based Automated Phase Discrimination and Morphological Insights in Ultra-High Carbon Steel

Random Walker based Automated Phase Discrimination and Morph...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Varnit Jain Vidyapati Kumar Ankita Mistri Mohit Jain Amitava Choudhury Manojit Ghosh Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology (IIEST) SHIBPUR West Bengal India Department of Mechanical Engineering Indian Institute of Technology Kharagpur West Bengal India Department of Mechanical Engineering Indian Institute of Technology Dhanbad Jharkhand India Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Image analysis is an important process required for characterization in different engineering domains. this work presents a novel image processing methodology to segment phases and quantify grain morphology in ultra-high carbon steel (UHCS) micrographs. Unlike conventional global thresholding methods, an interactive random walker technique which leverages user-defined markers for localized phase discrimination was deployed. Adaptive histogram equalization enhances contrast to identify seed markers representing distinct phases. the random walker algorithm propagates labels from these seeds by minimizing an edge-weighted cost function. this interactive approach provides precise phase boundaries despite variability in grain morphology. the segmented output enables statistical grain size, shape, and orientation quantification. the study demonstrates the UHCS micrograph data for an input grayscale image, and the mean area, mean intensity, and mean orientation of all the grains are computed to be 716.667 px 2 , 129.449, and -53.648, respectively. By combining accessible preprocessing, interactive localization, and rigorous postprocessing, our pipeline automates the characterization of microstructural features from image customizable nature of this physics-based machine learning technique makes it widely applicable for robust characterization of varied material microstructures from images.

关键词： Image segmentation Histograms Image analysis Shape Morphology machine learning Microstructure

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application of Multimodal machine learning for Image Recommendation Systems 11th

Application of Multimodal Machine Learning for Image Recom...

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11th international conference on Analysis of Images, Social Networks and Texts , AIST 2023

作者： Foniakov, Mikhail Bardukov, Anatoly Makarov, Ilya HSE University Moscow Russia AIRI Moscow Russia

ISBN: (纸本)9783031670077

In the era of information overload, making decisions about various aspects of our lives has become increasingly challenging. the rise of the internet and online platforms has provided access to vast amounts of information, including recommendations based on user preferences. this paper focuses on the development of a unique multimodal recommender system for images, leveraging machine learning and deep learning techniques. the results demonstrate the effectiveness of a complex recommender system, with an average accuracy of 0.814. the system outperforms algorithms based solely on CLIP embeddings or BERT vectors, showcasing the advantages of incorporating multiple modalities. the strengths of the model lie in its robust recognition of CLIP vectors and efficient processing of image features. However, there is room for improvement in text features and embeddings, suggesting the need for more detailed textual information and additional data sources. the innovation of this system is to utilize both image data and text descriptions to provide personalized recommendations. Images are vectorized and combined with relevant metrics, while text descriptions are obtained through object recognition or dataset text. the model incorporates various data, such as location, creation date, and event information, to enhance the recommendation process. Overall, this study highlights the potential of multimodal recommender systems for enhancing recommendation quality and providing users with a personalized experience. Ongoing efforts to refine the model with diverse data and parameter optimization can further improve its performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recommender systems

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Cross-Platform Empirical Analysis of Mobile application Development frameworks: Kotlin, React Native and Flutter 22

Cross-Platform Empirical Analysis of Mobile Application Deve...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Bhawna Suri Shweta Taneja Isha Bhanot Himanshi Sharma Aanchal Raj CSE Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science Bhagwan Parshuram Institute of Technology India Department of Computer Science Bhagwan Parshuram Institute of Technology GGSIPU Delhi India India Department of Computer Science Bhagwan Parshuram Institute of Technology Guru Gobind Singh Indraprastha University India

ISBN: (纸本)9781450399937

Mobile application development is currently seen as one of the most indispensable skills due to the recent boom in the population using smartphones and tablets. Due to the abundance and diversity of devices and platforms, developing applications for mobile devices is a complicated task involving multiple alternatives, technologies, and trade-offs. the development of applications in a cross-platform framework has become more prevalent in recent years. However, it is less clear which of the three major frame-works for android development, React Native, Flutter, and Kotlin is the most efficient in general *** this work, we have analyzed empirically the performance of three mobile application frameworks: Flutter, React-Native and Kotlin. three tic-tac-toe games are designed using Flutter, React-Native and Kotlin, and their performance is compared on the basis of various parameters such as CPU usage, Memory usage, packages and their installations to understand the advantages and disadvantages of cross-platform and native mobile development. Our work also aims to assist developers in determining which framework is most suited to their requirements by comparing them in areas such as environment, development languages and ease of use. the results have indicated that Kotlin has a slight advantage over the two cross-platform technologies, Flutter and React Native. Memory usage of React-Native and Flutter were approximately 13.3% and 1.6% more than Kotlin respectively. Similarly, the application sizes of React-Native and Flutter are 98.06MB and 93.32 MB respectively, with Kotlin coming in last at 11.06 MB. In future, further investigation of the topics covered in this work is required to ensure and enhance the findings.

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Prediction of Drug Permeability to the Blood-Brain Barrier using Deep learning 21

Prediction of Drug Permeability to the Blood-Brain Barrier u...

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4th international conference on Biometric Engineering and applications, ICBEA 2021

作者： Achiaa Atwereboannah, Abena Wu, Wei-Ping Nanor, Ebenezer University of Electronic Science and Technology of China China

ISBN: (纸本)9781450387651

In our quest to design new drugs, conventional means of designing drugs characterized by experiments is a time and resource demanding process. It involves exploring many molecular combinations, to find potential drugs to target indications. machine learning (ML) techniques upon their fantastic achievement in many application domains such as computer vision and Natural Language Processing etc., are recently, being utilized in the research of drugs. With the surge in compound databases, ML and Deep learning (DL) have shown great promise in various fields of drug discovery including pharmaceutics, biotechnology and physical chemistry. It is not incredible that these techniques are being incorporated in BBB studies. Predicting the permeability of drug compounds to the Blood-Brain Barrier (BBB) is indispensable for Central Nervous System (CNS) drug discovery, thus this work leverages two DL models in distinguishing between drugs that are BBB permeable and those that are not. the first architecture is a Fully-Connected Neural Network (FCNN) and the second is based on Convolutional Neural Network (CNN). Both algorithms were evaluated using two distinct CNS drug datasets. Our DL models generalize well, on both datasets and achieve competitive advantage over other ML and in silico techniques used in drug Blood-Brain Barrier Permeability (BBBP) prediction studies. the best performing model was the FCNN achieving AUROC of 99.5% on the first benchmark dataset. © 2021 ACM.

关键词： Blood

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Geographic information enterprise risk management methodology taking into account multiple risk factors 23

Geographic information enterprise risk management methodolog...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yu Sang Shenghua Xu Cong Ma An Luo School of Marine Technology and Geomatics Jiangsu Ocean University China Research Centre of Geo-Spatial Big Data Application Chinese Academy of Surveying and Mapping China

ISBN: (纸本)9798400709517

Analyzing and assessing quality risk is essential to fully utilize the value of geographic information data. this paper proposes a more comprehensive approach to geographic information enterprise risk management. First, enterprise risk factors are categorized based on guidance provided by ENISA. Second, entities related to the geographic information enterprise are categorized, including enterprises, people, locations, qualifications, and products, and ensuring that attributes related to the risk factors are covered. the risk factors of the geographic information enterprise are then categorized according to the risk categorization methodology provided by ENISA. A number of aspects such as financial risk, credit risk, data risk, personnel risk, regional risk and qualification risk are used to ensure a comprehensive analysis of the risks faced by geographic information enterprise. Finally, the DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability) model is applied as a risk assessment tool to evaluate the potential risks faced by geographic information enterprise and provide guidance for the development of appropriate risk management strategies. this approach will help geographic information enterprises to better manage and respond to various risks.

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An Integrated Approach for Suicidal Tendency Detection

An Integrated Approach for Suicidal Tendency Detection

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： V. Baby Dama Sudheshna Desu Sarvani Sahithi Vesangi Saketh Reddy Regatte Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Suicidal Tendency or the intention to kill oneself and end their life is a catastrophic situation which is mostly unknown by any person in the victim’s life. In many studies it is evident that, victims tend to kill themselves either to end their pain, pressure or to have a relief that they are not going to live in this world anymore. Current suicidal tendency detection methods include numerous machine learning and deep learning approaches using clinical data or online social media, provide a solution to this existing problem. this paper aims to present a multi-modal approach that could help the victim’s close ones to know if he/she is suffering from suicidal tendency. Providing a multi-faceted method that can detect this tendency and intimate the family, friends or the close ones beforehand can prove to be a boon for the invention. three different approaches like Facial Gesture Recognition, Voice Pattern Recognition and Text Pattern Recognition are imbibed in the Android Mobile application and used as modules to determine suicidal intent in a complementary manner. Finally, the limitations and future scope are extrapolated to provide an overview for the future research.

关键词： Deep learning Technological innovation Social networking (online) Pain Process control Gesture recognition computer architecture

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