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Integration of Process Optimization and Automation: A Way to AI Powered Digital Transformation 1

Integration of Process Optimization and Automation: A Way to...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kotte, Kulasekhara Reddy Thammareddi, Latha Kodi, Divya Anumolu, Venkateswara Rao Arun Kumar, K. Joshi, Shubham INC SLRI Solutions India India Cyber Security Senior Data Analyst Truist Financial India Technical Architect ERP Solutions- Information Technology India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

One of the most important factors facilitating AI-powered digital transformation is the combination of process automation and optimisation. Organisations may improve operational efficacy, save costs, and streamline procedures by utilising cutting-edge technology like robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). While automation seeks to reduce human participation in repetitive and rule-based operations, process optimisation concentrates on improving current business processes to attain improved productivity. The development of intelligent, adaptable, and self-improving systems is made possible by the combination of these two methodologies. Predictive findings, real-time decision-making, and customised customer experiences are further made possible by AI-driven analytics. This connection promotes innovation, scalability, and agility across sectors, giving businesses a competitive edge in the quickly changing digital market. The article examines the main principles, advantages, and difficulties of this shift and provides a road map for businesses looking to automate and optimise processes using AI in order to attain operational excellence and sustainable growth. © 2025 IEEE.

关键词： Decision making

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Colon Cancer Tissue Classification Using ML 6

Colon Cancer Tissue Classification Using ML

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Tripathi, Ashish Kumar, Kuldeep Misra, Anuradha Chaurasia, Brijesh Kumar Amity University Lucknow Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Bond University Center for Data Analytics Bond Business School Gold CoastQLD Australia Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350346961

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today's world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world's most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC-VAL-HE-7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.. © 2023 IEEE.

关键词： Image classification

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Disease Prediction using Convolution Neural Network with Radial Basis Function

Disease Prediction using Convolution Neural Network with Rad...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Penchala Prasad P Santhosh Kumar Bala Vikram Chandra G Department of Computer Science and Engineering(Data Science) Rajeev Gandhi Memorial College of Engineering and Technology Nandyal India Department of Computer Science and Engineering G Pulla Reddy Engineering College Kurnool India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Breast cancer is the second most occurring cancer in women, therefore detection of this particular type of cancer at an early stage is important. A small increase in breast cancer will risk in people with Parkinson's Disease (PD) and therefore Breast cancer screening is recommended to the people who are suffering from PD. The occurrence of PD in India is expected to double by 2030 and the main objective is to predict the PD and breast cancer diseases by using the available technological advancements to significantly improve the patient's survivability. The present research collects the relevant dataset from the standard repository database called UCI machine learning for an effective prediction process. The existing methods were not able to handle more complex datasets as they include noisy and imbalanced data. The proposed Convolution Neural Network with Radial Basis Function (CNN-RBF) is used for the present research to handle the issues of complex noisy data and imbalance data. The results attained from the proposed CNN-RBF showed robust as CNN extracts the prominent features for disease prediction and RBF fits the feature values in the dimension space thereby overcoming the problem of imbalance and noisy data. The proposed CNN-RBF obtained better accuracy of 98.97 % for the PD prediction and 97.98 % of accuracy for the breast cancer prediction when compared to the existing Deep CNN and RBF models.

关键词： Accuracy Neural networks Machine learning data collection Feature extraction Breast cancer Convolutional neural networks Noise measurement data mining Medical diagnostic imaging

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A BOUND FOR THE IMAGE CONDUCTOR OF A PRINCIPALLY POLARIZED ABELIAN VARIETY WITH OPEN GALOIS IMAGE

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Proceedings of the American Mathematical Society, Series B 2022年第26期9卷 272-285页

作者： Mayle, Jacob Department of Mathematics Statistics and Computer Science University of Illinois Chicago 851 S. Morgan Street Chicago 60607 IL United States

Let A be a principally polarized abelian variety of dimension g over a number field K. Assume that the image of the adelic Galois representation of A is an open subgroup of GSp2g (Ẑ). Then there exists a positive inte...

Let A be a principally polarized abelian variety of dimension g over a number field K. Assume that the image of the adelic Galois representation of A is an open subgroup of GSp_2g (Ẑ). Then there exists a positive integer m so that the Galois image of A is the full preimage of its reduction modulo m. The least m with this property, denoted m_A, is called the image conductor of A. Jones [Pacific J. Math. 308 (2020), pp. 307–331] recently established an upper bound for m_A, in terms of standard invariants of A, in the case that A is an elliptic curve without complex multiplication. In this paper, we generalize the aforementioned result to provide an analogous bound in arbitrary dimension. © 2022 by the author(s).

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Discovering Genetic Subtype of Glioblastoma from MRI Scans using Sparse Autoencoders

Discovering Genetic Subtype of Glioblastoma from MRI Scans u...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Fazal, Sumaiya Sabir, Armeena Ur Rehman, Hafeez Fast National University Department of Computer Science Peshawar Pakistan Higher Education Department Peshawar Pakistan Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Doha Qatar

ISBN: (纸本)9798350315974

Glioblastoma is a highly aggressive and deadly form of brain cancer. It is characterized by the presence of multiple genetic subtypes, each with its own unique set of molecular and clinical features. These subtypes can have a significant impact on patient prognosis and treatment response, making it important to accurately identify and classify them. Magnetic resonance imaging (MRI) images are a widely used modality for detecting and staging brain tumors, including glioblastoma. The genetic analysis using imaging has not been widely explored due to glioblastoma's heterogeneity and aggressive growth patterns. The heterogeneous appearance of MGMT signatures in an MRI image is difficult to characterize using MRI-specific features, as the presence of MGMT is associated with specific tumoral locations and enhancement patterns. In this work, we propose a method for discovering genetic subtypes of glioblastoma from MRI images using sparse autoencoders. Sparse autoencoders are a type of unsupervised deep learning algorithms that can learn to represent high-dimensional data in a lower-dimensional space while preserving important features and patterns. We use an autoencoder algorithm to learn a compact representation of the MR images, and then apply sparsity parameters to classify the images into different genetic subtypes based on their representation. We evaluate the performance of our method using a large dataset of MRI images from glioblastoma patients and show that it can accurately identify and classify the different genetic subtypes of the disease. Our findings suggest that the proposed approach has the potential to improve the diagnosis and treatment of glioblastoma by providing a more nuanced understanding of the underlying biology of the tumor, which ultimately can provide more personalized and targeted treatment options for patients. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

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Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springe

关键词： Internet of things

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Benign overfitting in two-layer ReLU convolutional neural networks for XOR data 24

Benign overfitting in two-layer ReLU convolutional neural ne...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xuran Meng Difan Zou Yuan Cao Department of Statistics and Actuarial Science University of Hong Kong Hong Kong Department of Computer Science University of Hong Kong Hong Kong Department of Statistics and Actuarial Science University of Hong Kong Hong Kong and Department of Mathematics University of Hong Kong Hong Kong

Modern deep learning models are usually highly over-parameterized so that they can overfit the training data. Surprisingly, such overfitting neural networks can usually still achieve high prediction accuracy. To study this "benign overfitting" phenomenon, a line of recent works has theoretically studied the learning of linear models and two-layer neural networks. However, most of these analyses are still limited to the very simple learning problems where the Bayes-optimal classifier is linear. In this work, we investigate a class of XOR-type classification tasks with label-flipping noises. We show that, under a certain condition on the sample complexity and signal-to-noise ratio, an over-parameterized ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy. Moreover, we also establish a matching lower bound result showing that when the previous condition is not satisfied, the prediction accuracy of the obtained CNN is an absolute constant away from the Bayes-optimal rate. Our result demonstrates that CNNs have a remarkable capacity to efficiently learn XOR problems, even in the presence of highly correlated features.

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Heterogeneous Random Forest

arXiv

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arXiv 2024年

作者： Kim, Ye-Eun Kim, Seoung Yun Kim, Hyunjoong Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Business Data Division GS Retail Seoul Korea Republic of

Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we introduce a novel approach called heterogeneous RF (HRF), designed to enhance tree diversity in a meaningful way. This diversification is achieved by deliberately introducing heterogeneity during the tree construction. Specifically, features used for splitting near the root node of previous trees are assigned lower weights when constructing the feature sub-space of the subsequent trees. As a result, dominant features in the prior trees are less likely to be employed in the next iteration, leading to a more diverse set of splitting features at the nodes. Through simulation studies, it was confirmed that the HRF method effectively mitigates the selection bias of trees within the ensemble, increases the diversity of the ensemble, and demonstrates superior performance on datasets with fewer noise features. To assess the comparative performance of HRF against other widely adopted ensemble methods, we conducted tests on 52 datasets, comprising both real-world and synthetic data. HRF consistently outperformed other ensemble methods in terms of accuracy across the majority of datasets. © 2024, CC BY-NC-ND.

关键词： Random forests

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