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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chaudhary, Jitendra Kumar Balakrishnan, S. Kapila, Dhiraj Hasan, Dler Salih Shafeeq, Mohammad Vishwakarma, Sunil Kumar School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand Bhimtal India Department of Commerce SRM Institute of Science and Technology Ramapuram campus Chennai89 India Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Information Tech. College of Science University of Salahaddin-Erbil Iraq Department of Computer Science and Engineering BBD University Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology U.P. Kanpur India

ISBN: (纸本)9798400709418

A comprehensive overview and implementation of a credit card fraud detection system using machine learning for feature selection system. In an era where digital transactions have become the norm, the need for robust fraud detection mechanisms has never been greater. This research explores various machine learning techniques and data preprocessing methods to develop an effective and efficient system for identifying fraudulent credit card transactions. The research begins by discussing the significance of credit card fraud and its detrimental impact on financial institutions and consumers. It then delves into the fundamental concepts of machine learning and data preprocessing, providing insights into the methodologies used to tackle this complex problem. The heart of the research lies in the design and implementation of the credit card fraud detection system. The research details the selection and fine-tuning of machine learning algorithms, the creation of a reliable dataset, and the evaluation of model performance using appropriate metrics. Additionally, research discusses strategies for handling imbalanced data, a common challenge in fraud detection. Furthermore, this research addresses the ethical and privacy considerations associated with handling sensitive financial data. It emphasizes the importance of compliance with legal regulations and ethical standards while implementing such systems. This research demonstrates a practical and effective approach to credit card fraud detection using Python and machine learning. © 2023 ACM.

关键词： Regulatory compliance

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Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction 2

Crop Classification on Hyperspectral Images Using Residual N...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Vivekanandhan, V. Al-Farouni, Mohammed Shreyas, A.V. Kumar, Gotte Ranjith Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA). © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of IoT-Based Automated Hydroponic Smart Farms: An Urban Farming Perspective 5th

A Comparative Study of IoT-Based Automated Hydroponic Smart ...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Jain, Swati Kaur, Mandeep School of Engineering and Technology Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819906000

With the increase in world population and the simultaneous decline in the resources available for farming, meeting the food demands of all the inhabitants is a substantial challenge. Adopting advanced technologies to maximize agricultural yield with the judicious use of available resources is the means to tackle these challenges. Internet of Things (IoT), one such budding technology, has made automation possible in various facets of human lives and changed the way human–machine interaction takes place. IoT is a network of inter-related things which have the ability to transfer data within each other and the surrounding environment. Integration of IoT technology with modern farming facilitates various cost-effective intelligent farming methods such as indoor farming and precision agriculture which, subsequently, leads to a reduction in resource wastage and optimal utilization of resources such as land, water, labor and fertilizers. This paper aims to review the most recent papers in the IoT-assisted smart farming domain. The intention is to provide a comparative analysis of all the recent developments in the area of IoT-based hydroponic smart farms to future researchers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hydroponics

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture 57

DRCTL: A Disorder-Resistant Computation Translation Layer En...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Zhou, Heng Wu, Bing Cheng, Huan Liu, Jinpeng Lei, Taoming Feng, Dan Tong, Wei School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology China

ISBN: (纸本)9798350350579

The memristive Computing-in-Memory (CIM) sys-Tem can efficiently accelerate matrix-vector multiplication (MVM) operations through in-situ computing. The data layout has a significant impact on the communication performance of CIM systems. Existing software-level communication opti-mizations aim to reduce communication distance by carefully designing static data layouts, while wear-leveling (WL) and error mitigation methods use dynamic scheduling to enhance system reliability, resulting in randomized data layouts and increased communication overhead. Besides, existing CIM compilers di-rectly map data to physical crossbars and generate instructions, which causes inconvenience for dynamic scheduling. To address these challenges of balancing communication performance and reliability while coordinating existing CIM compilers and dy-namic scheduling, we propose a disorder-resistant computation translation layer (DRCTL), which improves system lifetime and communication performance through co-optimization of data layout and dynamic scheduling. It consists of three parts: (1) We propose an address conversion method for dynamic scheduling, which updates the addresses in the instruction stream after dynamic scheduling, thereby avoiding recompilation. (2) Dynamic scheduling strategy for reliability improvement. We propose a hierarchical wear-leveling (HWL) strategy, which reduces communication by increasing scheduling granularity. (3) Communication optimization for dynamic scheduling. We propose data layout-Aware selective remapping (LASR), which helps dynamic scheduling methods improve communication lo-cality and reduce latency by exploiting data dependencies. The experiments demonstrate that HWL extends lifetime by 100.3-205.9 x compared to not using WL. Even with a slight lifetime decrease compared to the state-of-The-Art WL (TIWL), it still supports continuous neural network training for 7 years. After applying LASR to HWL, the number of execution cycles, energy consumption

关键词： program compilers

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Automated Ship Classification and Tracking in Satellite Imagery using Advanced Deep Learning Models 14

Automated Ship Classification and Tracking in Satellite Imag...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sruthi, K. Vijai, Anupa Sujee, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350335095

The identification of ships using satellite imagery has been a subject of continuous research due to its vital importance in maritime surveillance and navigation. It is challenging to recognize ships in satellite photographs due to the wide range of ship sizes and the complex backgrounds. In this study, we propose a system for classifying ships that combines the benefits of two deep-learning models, YOLOv3 and DenseNet. We began using YOLOv3 because it is a well-liked object identification model. However, we discovered that YOLOv3 is unable to recognize small objects, such as miniature ships, in satellite photographs. To get around this problem, we employed DenseNet, which is known for its ability to distinguish both large and small objects. However, DenseNet requires a significant amount of RAM for computations, which might be a disadvantage when resources are few. To get around this limitation, we coupled ResNet with DenseNet, a popular deep learning model recognized for its processing efficiency. As demonstrated by our recommended solution, which effectively recognizes ships in satellite data by combining the advantages of YOLOv3, DenseNet, and ResNet, ResNet and DenseNet working together achieved greater accuracy and more efficient use of memory. Our results demonstrate the efficacy of our approach and the potential applications to real-world issues like maritime surveillance and navigation. © 2023 IEEE.

关键词： Ships

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Audio Deepfake Detection Using Deep Learning 12

Audio Deepfake Detection Using Deep Learning

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Anagha, R. Arya, A. Narayan, V. Hari Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369861

The capacity to identify real audio recordings from their modified counterparts is essential in the age of sophisticated digital manipulation for maintaining security and trust in a vari- ety of applications, from media forensics to voice authentication systems. This research aims to create a deep learning model that can distinguish between authentic and altered audio files, with an emphasis on identifying audio deepfakes. The study uses Mel spectrogram representations and data augmentation techniques to effectively extract features from the ASV spoof 2019 dataset and train models. Convolutional neural networks (CNNs) comprising a number of layers, including convolutional, pooling, batch normalization, ReLU activation, dropout, global average pooling, and a dense classification layer are used as the foundation of the design. The Adam optimizer is used to optimize the model once it has been trained using binary cross-entropy loss, and a variety of metrics, such as accuracy, F1 score, ROC curve, and AUC, are used to track its performance. By making it easier to identify audio deepfakes, this project will ultimately increase the security and integrity of audio data in the digital world. © 2023 IEEE.

关键词： Feature extraction

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Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network 7th

Spatio-Temporal Prediction in Epidemiology Using Graph Conv...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Siji Rani, S. Dhanyalaxmi, Panickar Akshay, A.S. Ananthakrishnan, K.M. Siva Sankar, A. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819937608

Over the last decade, e-commerce has expanded significantly. An effective recommendation system is needed for better information filtering as more products are available online, particularly clothing and fashion accessories. The literature already includes a number of different recommendations for clothing. However, new problems like computation complexity and data’s exponential growth appear. The recommendation model must be updated frequently because trends change quickly. This paper proposes a collaborative filtering-based recommendation system. The Nearest Neighbor PageRank (NNPR) ranking algorithm, which creates individualized recommendations, combines the PageRank algorithm and the user’s closest neighbors. Unlike the Alternating Least square method, the suggested model is assessed by utilizing the (ALS) algorithm. The experiments use a dataset of Amazon fashion reviews, and the results are recorded in terms of Hit Ratio (HR) and Average Reciprocal Rank (MRR). NNPR has been shown to outperform ALS in active users and cold starters scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Navigating the Brain: Unveiling Alzheimer's Disease Using Convolutional Neural Network 7

Navigating the Brain: Unveiling Alzheimer's Disease Using Co...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Dinesan, Krishnpriya Sreesankar, S. Devesh Kumar, V.V. Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

There is no recognised treatment for the neurological illness known as Alzheimer's disease (AD). Early diagnosis and suitable treatment are advantageous. Deep Learning algorithms have proven successful in several areas, including the diagnosis of AD. This study achieves good accuracy (training: 86.34%, validation: 86.45%) for AD identification using MRI data and a convolutional neural network (CNN). The accuracy, quick processing, and population-level generalizability of the CNN architecture demonstrate its clinical use in categorising Alzheimer's disease. The system created in this study makes use of MRI scan images that were trained on the Kaggle dataset, highlighting how crucial consistent data is for model analysis and evaluation. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Cognitively Robust Speech Diarization and Overlap Detection: An Advanced Model for Granular Analysis of Audio Segments

Cognitively Robust Speech Diarization and Overlap Detection:...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Ajay, Ashin K. Prathilothamai, M. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Multiple speakers conversing at once, or overlapping speech, poses a significant challenge in various speech analysis applications. The accuracy of handling this phenomenon greatly affects tasks such as speaker identification and speech recognition. Current methodologies categorize overlapping speech using individual techniques like MFCC (Mel Frequency Cepstral Coefficients), LFCC (Linear Frequency Cepstral Coefficients), and STFT (Short-time Fourier Transform), yielding certain results. In this research, we propose a comparative study that refines features using MFCC, LFCC, STFT, and wavelet transform, combined with a CNN (Convolutional Neural Network) model for classification. By capturing both time and frequency data, the Wavelet Transform holds promise for improving the precision of overlapping speech classification. To capture complex patterns and variations, the CNN model develops hierarchical representations. Our findings indicate that implementing MFCC in the CNN model yields superior results compared to LFCC. Additionally, the Wavelet transform outperforms the STFT transform in terms of performance and effectiveness. Furthermore, this paper places emphasis on the implementation of an algorithm that focuses on voice diarization and overlap detection. The objective is to accurately detect and categorize overlapping voice segments within an audio file, enabling a better understanding of its structure. The algorithm estimates the overall duration of each group by combining consecutive chunks with the same categorization, providing valuable insights into the temporal distribution of overlapping speech. Leveraging this data, the algorithm predicts the starting and ending periods of overlapping voice segments, facilitating the localization of overlaps. © 2023 IEEE.

关键词： CNN LFCC MFCC Overlapped speech speech diarization STFT Wavelet transform

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