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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Takkar, Sakshi Rakhra, Manik Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798331542375

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research. © 2025 IEEE.

关键词： Deep learning

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Analysis of Ensemble Machine Learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Siddharth Chaurasia Satyam Kesharwani Shanu Sharma Swati Sharma Bharti Chugh Dept. of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder Data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud.

关键词： Industries Machine learning algorithms Europe Forestry Credit cards Fraud Task analysis

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Personality Analysis Using Graphology and Image Processing Techniques 1

Personality Analysis Using Graphology and Image Processing T...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Jadon, Anshika Gutpa, Harsh Shyan, Er. Hatesh Tyagi, Aayush Raj, Tejaswi Chandigarh University Dept. of Computer Science & Engineering Mohali140413 India

ISBN: (纸本)9798331532956

This research studies the role of advanced image processing and graphology at a higher level of complexity, that is, predicting grief by interpreting handwriting. The aim of this study is to establish a reliable model, which gets a connection between the deep learning techniques and a certain set of handwriting parameters that would depict a person's mental state. This method combines the foundations of traditional graphology with the newest advancements in computer technology, such as TensorFlow and Keras, and use CNNs for feature extraction. Advanced machine learning methods including PCA, Random Forest, SVMs, and Gradient Boosting are used to improve the model's ability to classify emotional states from handwriting. The results show that the model has established discriminative capability in classification of 85% of samples with precision and recall values at 88% and 82%, respectively. This research amalgamating the traditional graphology with modern practices in psychology provides an opportunity for further enhancements in mental health evaluation through handwriting-based approaches. © 2024 IEEE.

关键词： Photointerpretation

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms

Smart Agriculture Inventory Management System Using Machine ...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Hoshiyar Singh Kanyal Harshit Jain Harsh Tyagi Satyarth Prakash Yadav Dept. of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices.

关键词： Analytical models Visualization Profitability Decision making Crops Weather forecasting Transforms Real-time systems Data models Timing

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Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

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IEEE International Conference on Big Data

作者： Xinyang Wu Andrew Horner Dept. Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model’s performance. Generated mashups used for evaluation can be found in https://***/***/.

关键词： Accuracy Mashups Instruments Music Production Big Data Harmonic analysis Graph neural networks

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Metaheuristic Techniques Optimised LSTM Network for Improved Weather Prediction

Metaheuristic Techniques Optimised LSTM Network for Improved...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Shweta Mittal Om Prakash Sangwan Dept. of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar India

Tremendous amount of meteorological data is being generated on a daily basis from a number of sources such as weather stations, balloons, satellites, sensors etc. Timely weather prediction helps people plan everyday life events. Long Short Term Memory (LSTM) networks perform extremely well for capturing dependencies in time series datasets. Number of neurons in hidden layer highly impacts the performance of the LSTM network. The hit and Trial method for selecting the neurons consumes a lot of time and resources and might not lead to a global optimum solution. In this research work, two techniques, i.e. Genetic Algorithm optimized LSTM (LSTM_GA) and Artificial Bee Colony optimized LSTM (LSTM_ABC) have been proposed and implemented to automate the selection of the hidden layer’s neurons for improved weather prediction. Proposed techniques have been implemented using DeepLearning4j library making the techniques scalable on clusters of machines. For evaluating the performance of proposed techniques, 15 years Brazil’s weather dataset has been used. The experimental results proved that the proposed LSTM_GA and LSTM_ABC techniques have the reduced MAE value of 0.0074 and 0.0075 respectively while the LSTM network without any optimization has a relatively higher MAE value of 0.0086.

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Applications of AI in Preservation of Kumaoni Culture Through Digital Archival 3

Applications of AI in Preservation of Kumaoni Culture Throug...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Dev, Vaishali Dhingra, Ansh Sharma, Shashi Kumar Koturiya, Nishant Graphic Era Hill University Dept. of Computer Science and Engineering Bhimtal India

ISBN: (纸本)9798331519582

Culture represents a society with human values and ethics. Without civilization there can be no culture. Thus it is very important to preserve our culture as it represents our values and ethics. Each region has its own characteristics culture which separates it from other regions not just geographically but in terms of traditions, art and literature. The Kumaon region in the Indian state of Uttarakhand lies in the eastern part of the state. It is blessed with a rich culture heritage and nature. The preservation of culture is done through traditional methods. The focus of this study is to use modern Artificial Intelligence techniques to achieve the preservation of culture. The Artificial Intelligence techniques which are used in this paper range from Convolutional Neural Networks, augmentation of data and fine tuning of models. To preserve it is necessary to archive the cultural elements so that it can be easily accessible in the future. The archival process can help in cataloguing the cultural items. By culture we mean traditional arts, such as Aipan, local literature texts and historical structures. The archival process seamlessly integrates the Artificial Intelligence methods to effectively preserve the cultural elements. This process can be further enhanced by fine tuning the Convolutional Neural Networks. This makes the model more robust and accurate than the existing model. The dataset quality can be further improved by using data augmentation techniques. This allows to generate more data from existing dataset. However, with the process comes some challenges like accountability and validity. This study explores these challenges which can help existing researchers in the field of culture and arts. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Happy Rita Chhikara Neeti Kashyap Dept. of Computer Science & Engineering The NorthCap University Gurugram Haryana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms’ capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape.

关键词： Support vector machines Radio frequency Machine learning algorithms Accuracy Botnet Oral communication Prediction algorithms

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A Likelihood Ratio Test of Genetic Relationship among Languages

arXiv

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arXiv 2024年

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024, CC BY.

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An Advanced computer Vision Model for Aiding Automobile in Traffic Sign Classification

An Advanced Computer Vision Model for Aiding Automobile in T...

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Computing and Data science (ICCDS), International Conference on

作者： Veera Satya Pavan Vignesh Kukkadapu Sai Prajith Goddati Parthav Gokul Cherukuri Adarsh Goluguri NV Rajareddy Dept. of Computer Science & Engineering GITAM Deemed to be University Visakhapatnam India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

Accurate traffic sign recognition is crucial for autonomous vehicles. This paper proposes a novel deep learning approach for sign classification that addresses limitations in training data. Our method employs a three-step process: strategic data augmentation with rotation limitations for realistic variations, feature extraction using parallel atrous convolution layers with varying dilation rates to capture multi-scale information, and robust feature map generation through output concatenation. This approach achieves a remarkable accuracy of 94.65%, surpassing the performance of established pre-trained models like VGG-16, ResNet-50, and AlexNet. This research contributes a significant advancement in real-world traffic sign classification in autonomous vehicles.

关键词： Training Solid modeling Accuracy Navigation Computational modeling Roads Transportation

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