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Distributed computing and Optimization Techniques (ICDCOT), International Conference on

作者： N L Aravinda R Archana Reddy Bala Dhandayuthapani V Jithendra P R Nayak Ahmad Husseen Electronics and Communication Engineering Department Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Engineering SR University Warangal Telangana India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas branch Oman Department of CSE Srinivas Institute of Technology Valachil Mangalore India The Islamic university Najaf Iraq

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN).

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SkinLesion-STN: Enhancing Skin Lesion Classification using Spatial Transformer Networks

SkinLesion-STN: Enhancing Skin Lesion Classification using S...

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Artificial Intelligence Trends and Pattern Recognition (ICAITPR), International Conference on

作者： K. G. Suma M. Geetha Yadav R. Hendra Kumar Navnath Kale Gurram Sunitha Chinthapatla Pranay Varna School of CSE VIT-AP Amaravati India Department of CSE Institute of Aeronautical Engineering Hyderabad Telangana India Department of CSE K M M Institute of Technology & Science Tirupati A.P. India School of Computer Engineering MIT Academy of Engineering Alandi India Department of CSE School of Computing Mohan Babu University Tirupati A.P. India Department of CSE Sree Vidyanikethan Engineering College Tirupati A.P. India

ISBN: (数字)9798350368000

ISBN: (纸本)9798350368017

SkinLesion-STN offers a promising solution to enhance skin lesion classification by explicitly addressing spatial variations in skin lesion images. This research paper proposes SkinLesion-STN, a novel deep model for type detection from images of skin lesions. SkinLesion-STN is a customly designed Spatial Transformer Network aimed to improve the performance of automated lesion type classifiers. Integration of STNs into the architecture contributes to improved classification accuracy and robustness, making it a valuable addition to dermatology and medical image analysis domains. The novelty of SkinLesion-STN lies in its potential to improve performance of skin lesion type detection. Thus, leading to early detection of skin cancer and reducing false negatives and false positives. The efficiency of SkinLesion-STN model is verified with extensive experiments on HAM10000 benchmark dataset. SkinLesion-STN is evaluated against EfficientNet-B0 to evaluate its performance. SkinLesion-STN has achieved good results in terms of specificity and accuracy whereas EfficientNet-B0 achieved good specificity over SkinLesion-STN. With more fine-tuned hyper parameters, SkinLesion-STN may perform better in terms of specificity also. SkinLesion-STN demonstrated an accuracy of 0.980 against EfficientNet-B0 with 0.917 accuracy. The results demonstrated superior performance, thereby establishing the superiority of STNs for skin lesion detection and classification tasks. Overall, this research is aimed to contribute to the advancement of computer-aided diagnostic systems for dermatology, ultimately benefiting patients and healthcare providers alike.

关键词： Solid modeling Accuracy Dermatology Transformers Skin Robustness Lesions Medical diagnostic imaging Image classification Skin cancer

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Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

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Computational Intelligence and Neuroscience 2022年 2022卷 8501738页

作者： Krishnaraj, N. Sivakumar, B. Kuppusamy, Ramya Teekaraman, Yuvaraja Thelkar, Amruth Ramesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Karnataka Bangalore562106 India Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches. © 2022 N. Krishnaraj et al.

关键词： Generative adversarial networks

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Repairing with Zero Skip Cost

arXiv

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arXiv 2024年

作者： Zhang, Wenqin Chee, Yeow Meng Dau, Son Hoang Etzion, Tuvi Kiah, Han Mao Luo, Yuan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore School of Computing Technologies RMIT University MelbourneVIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio...

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Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

arXiv

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arXiv 2023年

作者： Long, Jing Chen, Tong Hung, Nguyen Quoc Viet Xu, Guandong Zheng, Kai Yin, Hongzhi The University of Queensland BrisbaneQLD Australia Griffith University Gold CoastQLD Australia University of Technology Sydney SydneyNSW Australia School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Engineering of China Chengdu Sichuan China

As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users’ sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users’ mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension & number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling © 2023, CC BY.

关键词： Learning systems

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Human Activity Recognition Using Improved Cat Swarm Optimization Algorithm and Convolutional Neural Network

Human Activity Recognition Using Improved Cat Swarm Optimiza...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Yerrolla Chanti Ammar Hameed Shnain Ramesh Banoth Rana Veer Samara Sihman Brahmatej Rupavath C. Sushama School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India Indiana Wesleyan University Marion USA Department of CSE School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Widespread computing has a lot of ability to offer users customised services and support, especially as more people use smartphones for everyday tasks. In the past ten years, there has been a significant advancement in Sensor-Based Human Activity Recognition (HAR), which detects the movements of the user and emerged as a crucial component of pervasive computing. Thus, accurate HAR plays a significant role in numerous applications. For accurate HAR, the current research proposes an Improved Cat Swarm Optimization Algorithm based Convolutional Neural Network (ICSO-CNN) for tuning the parameters of CNN for optimal solution. Using various performance measures, the present research examines the performance of the proposed ICSO-CNN algorithm. The test results demonstrated that the ICSO-CNN outperforms the compared methods, including CNN with Long Short-Term Memory (CNN-LSTM), Particle Swarm Optimization based CNN (PSO-CNN), and CNN-BiLSTM with an accuracy of 99.79%.

关键词： Pervasive computing Accuracy Particle measurements Convolutional neural networks Human activity recognition Particle swarm optimization Tuning Optimization Smart phones Sports

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Decentralized Framework for Collection and Secure Storage of Google Street View Data: Case Study

Decentralized Framework for Collection and Secure Storage of...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Sanjaya Mallikarachchi Bonnie Ho Iyad Kanj Oshani Seneviratne Isuru S. Godage Jarvis College of Computing & Digital Media School of Computing DePaul University Chicago IL USA Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA Department of Engineering Technology & Industrial Distribution Texas A&M University College Station TX USA

Image data plays a pivotal role in the current data-driven era, particularly in applications such as computer vision, object recognition, and facial identification. Google Maps ® stands out as a widely used platform that heavily relies on street view images. To fulfill the pressing need for an effective and distributed mechanism for image data collection, we present a framework that utilizes smart contract technology and open-source robots to gather street-view image sequences. The proposed framework also includes a protocol for maintaining these sequences using a private blockchain capable of retaining different versions of street views while ensuring the integrity of collected data. With this framework, Google Maps ® data can be securely collected, stored, and published on a private blockchain. By conducting tests with actual robots, we demonstrate the feasibility of the framework and its capability to seamlessly upload privately maintained blockchain image sequences to Google Maps ® using the Google Street View ® Publish API.

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Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation

arXiv

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arXiv 2024年

作者： Shi, Long Tang, Chuanqing Deng, Huangyi Xu, Cai Xing, Lei Chen, Badong The School of Computing and Artificial Intelligence China The Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province Southwestern University of Finance and Economics Chengdu611130 China School of Computer Science and Technology Xidian University China The Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster’s combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hierarchical framework includes a two-phase aggregation process: the intra-view and inter-view aggregation hierarchies. In the intra aggregation, we assume that each view is comprised of common information shared with other views, as well as its specific information. We then aggregate both the common and specific information. This aggregation phase is useful to eliminate the feature noise inherent to view itself, thereby improving the view quality. In the inter-view aggregation, we design an attention mechanism at the evidence level to facilitate opinion aggregation from different views. To the best of our knowledge, this is one of the pioneering efforts to formulate a hierarchical aggregation framework in the trusted multi-view learning domain. Extensive experiments show that our model outperforms some state-of-art trust-related baselines. One can access the source code on https://***/lshi91/GTMC-HOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A nonconvex framework for structured dynamic covariance recovery

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 9056-9146页

作者： Katherine Tsai Mladen Kolar Oluwasanmi Koyejo Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL University of Chicago Booth School of Business Chicago IL Department of Computer Science Beckman Institute for Advanced Science and Technology and Statistics University of Illinois at Urbana-Champaign Urbana IL

We propose a flexible, yet interpretable model for high-dimensional data with time-varying second-order statistics, motivated and applied to functional neuroimaging data. Our approach implements the neuroscientific hypothesis of discrete cognitive processes by factorizing covariances into sparse spatial and smooth temporal components. Although this factorization results in parsimony and domain interpretability, the resulting estimation problem is nonconvex. We design a two-stage optimization scheme with a tailored spectral initialization, combined with iteratively refined alternating projected gradient descent. We prove a linear convergence rate up to a nontrivial statistical error for the proposed descent scheme and establish sample complexity guarantees for the estimator. Empirical results using simulated data and brain imaging data illustrate that our approach outperforms existing baselines.

关键词： dynamic covariance structured factor model alternating projected gradient descent time series data functional connectivity

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DRAM-Locker: A General-Purpose DRAM Protection Mechanism against Adversarial DNN Weight Attacks

arXiv

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arXiv 2023年

作者： Zhou, Ranyang Ahmed, Sabbir Roohi, Arman Rakin, Adnan Siraj Angizi, Shaahin Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Computer Science State University of New York BinghamtonNY United States School of Computing University of Nebraska–Lincoln LincolnNE United States

In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the capabilities of in-DRAM swapping combined with a lock-table to prevent attackers from singling out specific DRAM rows to safeguard DNN’s weight parameters. Our results indicate that DRAM-Locker can deliver a high level of protection downgrading the performance of targeted weight attacks to a random attack level. Furthermore, the proposed defense mechanism demonstrates no reduction in accuracy when applied to CIFAR-10 and CIFAR-100. Importantly, DRAM-Locker does not necessitate any software retraining or result in extra hardware burden. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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