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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P. Malathi S. Jansi M.V. Prabhakaran K. Senbagam M.P. Sujatha R Sadaieswaran Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer science and Engineering Acharya college of Engineering and Technology Achariyapuram Puducherry India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of arts and science Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns.

关键词： Waste reduction Technological innovation data analysis Fault detection Quality control Big data Real-time systems Manufacturing Predictive analytics Pharmaceuticals

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Surrogate-assisted parallel tempering for Bayesian neural learning

arXiv

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arXiv 2018年

作者： Chandra, Rohitash Jain, Konark Kapoor, Arpit Centre for Translational Data Science School of Geosciences University of Sydney SydneyNSW2006 Australia School of Electrical and Electronics Engineering Indian Institute of Technology Guwahati Assam India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India

Parallel tempering addresses some of the drawbacks of canonical Markov Chain Monte-Carlo methods for Bayesian neural learning with the ability to utilize high performance computing. However, certain challenges remain given the large range of network parameters and big data. Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. We address the inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. In this paper, we present surrogate-assisted parallel tempering for Bayesian neural learning where the surrogates are used to estimate the likelihood. The estimation via the surrogate becomes useful rather than evaluating computationally expensive models that feature large number of parameters and datasets. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making using Bayesian neural learning. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models. Copyright © 2018, The Authors. All rights reserved.

关键词： Large dataset

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal

Maize Plant Disease Prediction of UAV Images for Precision A...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Vinay Mishra Nenavath Srinivas Naik Santosh Kumar Saeed Hamood Alsamhi Abdu Saif Edward Curry Department of Computer Science and Engineering International Institute of Information Technology (IIIT)-Naya Raipur Chhattisgarh India Insight Centre for Data Analytics University of Galway Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

The agriculture sector is vital to a country’s economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images.

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Deep Learning Meets Satellite Images – An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images

arXiv

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arXiv 2024年

作者： Song, Shuang Morelli, Luca Wu, Xinyi Qin, Rongjun Albanwan, Hessah Remondino, Fabio Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States Trento Italy Department of Civil Environmental and Mechanical Engineering University of Trento Italy Civil Engineering Department Kuwait University Kuwait

A critical step in the digital surface models (DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance of feature matching due to spectral distortions between images, long baseline, and wide intersection angles. Feature matching methods have evolved over the years from handcrafted methods (e.g., SIFT) to learning-based methods (e.g., SuperPoint and SuperGlue). In this paper, we compare the performance of different features, also known as feature extraction and matching methods, applied to satellite imagery. A wide range of stereo pairs (~ 500) covering two separate study sites are used. SIFT, as a widely used classic feature extraction and matching algorithm, is compared with seven deep-learning matching methods: SuperGlue, LightGlue, LoFTR, ASpanFormer, DKM, GIM-LightGlue, and GIM-DKM. Results demonstrate that traditional matching methods are still competitive in this age of deep learning, although for particular scenarios learning-based methods are very promising. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite imagery

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SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search

arXiv

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arXiv 2024年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT-e for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. © 2024, CC BY.

关键词： Chatbots

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All in One: Multi-Task Prompting for Graph Neural Networks

arXiv

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arXiv 2023年

作者： Sun, Xiangguo Cheng, Hong Li, Jia Liu, Bo Guan, Jihong Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China School of Computer Science and Engineering Southeast University Purple Mountain Laboratories China Department of Computer Science and Technology Tongji University China

Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a "negative transfer" to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Effective Hybrid Precoder Logic with mm Wave Massive MIMO using Novel Deep Learning Scheme

Effective Hybrid Precoder Logic with mm Wave Massive MIMO us...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Ranjith S P Jesu Jayarin A. Chandra Sekar Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Tamilnadu India Department of Data Analytics Institute of IT Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS) Chennai India Department of Computer Science Engineering St. Joseph’s College of Engineering Chennai Tamilnadu India

For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, however, they are not using all of the spatial details. The combination of analog and digital precoding logics are considered to be a significant methodology to deal with complexities over hardware area and the higher consumption of energy in association with combination of signal elements. But the basic degradation of the traditional hybrid-precoding methodologies has some issues such as processing complexities and has a lacking to explore the spatial data regarding the channels. To address these limitations, this paper suggests a Novel Deep Learning Scheme (NDLS), in which it is enabled by mmWave-MMIMO system for successful Hybrid Precoder Logic (HDL). As well as each Precoder selection is used to obtain the The optimised-decoder is perceived in the Novel Deep Learning Scheme as a mapping relation. To be more precise, a Hybrid Precoder Logic is extracted via Novel Deep Learning Scheme training for optimizing the Millimeter- Wave-Massive-MIMO precoding method. Additionally, a detailed simulation results are presented to demonstrate the proposed scheme's superior efficiency and the resulting section demonstrate that the Novel Deep Learning Scheme based methodology is competent of lowering the Bit-Error- Rate (BER) and increasing the effectiveness of the frequency of Millimeter-Wave Massive-MIMO (mmWave- MMIMO), gaining a competitive advantage in faster convergence as compared to traditional schemes while significantly reducing the necessary computational difficulty.

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Exploring Decentralized Identity Verification Systems Using Blockchain Technology: Opportunities and Challenges

Exploring Decentralized Identity Verification Systems Using ...

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Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Sumalatha A Desidi Narsimha Reddy B. Shamreen Ahamed Udayakumar K Arthi Priyadharshini M Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid advancement of digital technologies has underscored the critical necessity for robust and reliable identity verification systems in today's interconnected world. Traditional centralized identity management systems encounter myriad challenges, from vulnerabilities to data breaches and privacy concerns, to inefficiencies inherent in verification processes. This paper delves into the transformative potential of decentralized identity verification systems using blockchain technology as a robust solution to these pressing issues. By harnessing blockchain's immutable, transparent, and secure characteristics, decentralized identity systems not only promise enhanced privacy, security, and user control over personal data but also foster a paradigm shift towards more efficient and user-centric identity management *** study rigorously examines the foundational principles of blockchain technology and its strategic application in decentralized identity verification. It highlights key opportunities such as strengthened security measures through cryptographic protocols, enhanced user autonomy in managing and sharing personal information, and streamlined verification processes that reduce operational complexities and costs. Additionally, the paper critically evaluates significant challenges that must be addressed, including scalability concerns as blockchain networks grow, complexities in navigating regulatory landscapes designed for centralized systems, and overcoming barriers to widespread user adoption. Through an exhaustive analysis of existing models, real-world case studies, and emerging trends, this paper aims to provide comprehensive insights into the feasibility and future prospects of decentralized identity verification systems. By contributing to the ongoing discourse on innovative identity management solutions in the digital age, this research seeks to pave the way for more secure, efficient, and equitable digital ecosystems.

关键词： data privacy Navigation Scalability Pressing Market research Blockchains Complexity theory Security Reliability Identity management systems

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A Note on the Evaluation of Mutation Prioritization Algorithms

A Note on the Evaluation of Mutation Prioritization Algorith...

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IEEE Symposium Series on Computational Intelligence

作者： Dusan Popovic Jesse Davis Alejandro Sifrim Bart De Moor STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Department of Electrical Engineering (ESAT) KU Leuven Department of Computer Science KU Leuven Wellcome Trust Sanger Institute Wellcome Trust Genome Campus

ISBN: (纸本)9781479975617

Recent developments in the field of gene sequencing technology greatly accelerated discovery of mutations that cause various genetic disorders. At the same time, a typical sequencing experiment generates a large number of candidate mutations, hence detecting single or few causative variants is still a formidable problem. Many computational methods have been proposed to assist this process, from which a large portion employ statistical learning in some form. Consequently, each newly designed algorithm is routinely compared to other competing systems in hope to demonstrate advantageous performance. In this work we review and discuss several issues related to the current practice of evaluation of mutation prioritization algorithms and suggest possible directions for improvements.

关键词： Statistical learning Hereditary Diseases Mutation Hopefulness algorithms Base Sequence Design Algorithm Note Taking computational methods

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Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

Quantum Machine Learning for Software Supply Chain Attacks: ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Mohammad Masum Mohammad Nazim Md Jobair Hossain Faruk Hossain Shahriar Maria Valero Md Abdullah Hafiz Khan Gias Uddin Shabir Barzanjeh Erhan Saglamyurek Akond Rahman Sheikh Iqbal Ahamed Analytics and Data Science Institute Kennesaw State University USA Department of Computer Science Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department Information Technology Kennesaw State University USA Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University USA Department of Computer Science Marquette University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.

关键词： computers Machine learning algorithms Supply chains Neural networks Qubit Software algorithms Support vector machine classification

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