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Improving Medical Image Classification Through Transfer Learning and Meta Learning 5th

Improving Medical Image Classification Through Transfer Lear...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Arora, Sonia Jebakumari, S. Adlin Singh, Vaishali Kharbas, Vikas Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents an observation of improving clinical picture classification via transfer getting to know and meta-studying. Switching getting-to-know and meta-learning methods are usually employed in medical imaging class tasks. Transfer studying objectives to switch information and illustration from a properly-trained version and mildew that expertise to higher healthy the problem at hand, while meta-gaining knowledge is a version of switch gaining knowledge of which targets to seize and adapt the generalizable expertise of a project domain, and to allow a progressed impact for the goal tasks. Inside the clinical imaging scenario, the two procedures can dig deeper into the several layers which might be involved in gaining deep knowledge of algorithms. The authors compare the effectiveness of the proposed strategies in five publicly available medical imaging datasets. Effects have shown that the proposed techniques notably advanced the usual type accuracy for a few datasets. Furthermore, it was found that the 2-step technique showed higher performance than the one-step technique. This study presents a higher understanding of the efficacy of transfer learning and meta-mastering techniques for clinical photo-type tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Identifying Neuronal Damage and Plasticity by Analyzing Changes in Diffusion Tensor Imaging 5th

Identifying Neuronal Damage and Plasticity by Analyzing Chan...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Chandra, Subhash Rengarajan, A. Sahoo, Girija Shankar Sharma, Sunil Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This paper presents a unique method, which investigates the neuronal damage and plasticity, the use of changes in diffusion tensor imaging (DTI). Thru combining the information from DTI, collectively with cognitive and demographic records, this method identifies and assesses the adjustments inside the brain's white count number integrity. The findings suggest that the proposed approach is capable of signify the structural modifications in the white be counted, whilst additionally offering significant data about the diploma of neuronal harm and plasticity. This research consequently gives a significant device for in addition investigation into the diagnosis of certain neurological illnesses, especially in stroke and traumatic brain injury, wherein identifying modifications within the mind is critical for proper direction of remedy. In addition, the challenges related to the DTI statistics analysis is discussed, collectively with capability applications and future extensions. This paper presents a complete survey of ways Diffusion Tensor Imaging (DTI) may be applied to become aware of neural harm and plasticity. It explains how DTI can discover microstructural modifications in frightened tissue, making it a promising device for Neuro-imaging research. The paper compares the maximum popular indices used in analysis and model fitting the use of DTI to measure modifications, including fractional anisotropy and imply diffusivity. Subsequently, the paper examines destiny guidelines and potential packages of DTI within the clinical setting. In the end, the paper shows that DTI gives a valuable supply of perception into the neural machine, allowing researchers to discover, quantify, and better apprehend structural modifications to neurons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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Innovative Deep Learning Model-based Stock Price Prediction using a Hybrid Approach of CNN and Gradient Recurrent Unit 8

Innovative Deep Learning Model-based Stock Price Prediction ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Rao, B. Srinivasa Bhattacharya, Rajib Tiwari, Manoj Kumar Kumari, K. Aruna Devmane, Mahavir A. Singh, Kamlesh Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Globsyn Business School Department of Pgdm Bishnupur JL No. 101 South 24 Paganas West Bengal Chandipur India Jecrc University Department of Computer Application Rajasthan Jaipur India University Mathematics Gss Visakhapatnam India Vppcoe & Va Department of Computer Engineering Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350396638

The fluctuation in stock prices from industry to industry are a major source of concern in the market. As the market attracts more participants and stock prices play a larger role in more transactions, the ability to accurately predict stock price movements becomes more valuable. When making an investment, many people first look at the share price and then try to anticipate whether or not that price will go up or down in the future. The traditional problem of forecasting the stock market using Machine Learning tools and methodologies has been thoroughly studied. Time dependence, volatility, and similar complicated dependencies are interesting aspects that make this modeling non-trivial. To overcome this the proposed method in this work is a deep learning-based hybrid strategy for predicting stock prices. Preprocessing is performed after input is delivered to increase precision. Selecting MPSOA features is the next step. In the end, it's put to use in MC-GRU model training. The proposed method achieves better results than both the CNN and GRU models. © 2023 IEEE.

关键词： Investments

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Applying Transfer Learning to Medical Image Classification Tasks 5th

Applying Transfer Learning to Medical Image Classification T...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Gupta, Manali Haripriya, V. Singh, Akhilendra Pratap Yadav, Surendra Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Transfer studying is a machine learning approach that permits a model to leverage know-how from a previous challenge to boost studying in the following tasks. It's been used increasingly in medical photo classification obligations, allowing area-particular knowledge to be leveraged from current fashions. This method can enhance the overall performance of clinical photograph-type structures and reduce the amount of labeled information required to analyze a brand-new venture. In this paper, we assess the present-day kingdom of switches, getting to know the scientific photo category and the results for clinical picture evaluation. We discuss present switch studying techniques and their influences on the accuracy of medical photo category responsibilities and the challenges related to constructing generalizable fashions. Eventually, we take a look at existing datasets and implementations available for the clinical photo category and factor into promising destiny studies guidelines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Understanding the Role of Data-Driven Communications Software Design in Networks 5th

Understanding the Role of Data-Driven Communications Softwar...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm 1

A Novel Hybrid Approach for Threat Detection in Cyber Securi...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Gupta, Abhishek Kumar Dixit, Neetu Kumar, Sujeet Rawat, Priyanka Madhumita Jagannath International Management School Department of Information Technology New Delhi110070 India RBMI Department of Computer Application Greater Noida201306 India GL Bajaj Institute of Management Greater Noida201306 India Lingaya's Vidyapeeth Department of Computer Science & Engineering Faridabad121002 India

ISBN: (纸本)9798350353648

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks. © 2024 IEEE.

关键词： Cyber attacks

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Examining the Advantages of Dynamic Communications Software Design in Networks 5th

Examining the Advantages of Dynamic Communications Software ...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Raghavendra, R. Das, Neeraj Inzimam Kurmi, Raviraj Singh Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper examines the blessings of dynamic communications software program design in networks. By leveraging advances in generation, dynamic communications provide an ever-evolving method of connecting, communicating, and sharing facts inside networks without relying on static, often outdated, hardware. The principle blessings related to this community layout are scalability, flexibility, and excellent reliability. As networks extend, dynamic communications software can auto-regulate accordingly to ensure the overall capability of the general gadget. Moreover, users can count on operational performance upgrades and extended capability to address protection and privacy worries. Moreover, using dynamic software allows for more green aid usage, which facilitates lessening expenses related to the control and operation of the network. Moreover, dynamic communications have also helped reduce downtime and sell extra resilience in the face of surprising changes or unanticipated threats. Standard, this paper provides an in-depth knowledge of the advantages of dynamic communications software layout in networks. Dynamic Communications software program design and its various benefits in networking have been gaining substantial attention in recent years. Dynamic Communications software program layout additionally allows protocols to be modified on an as-needed foundation. That is useful for networks that might be constantly changing and want to adapt, which will take care of the converting environment. New protocols may be introduced or eliminated as wished, and the gadget will adjust to make the most efficient performance. In addition, using Dynamic Communications software design allows for a more straightforward integration of different systems. It is useful for networks that require a couple of systems to work together in order for them to function well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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HyperCalm Sketch: One-Pass Mining Periodic Batches in Data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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Leveraging Machine Learning to Optimize Real-Time Data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. This technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. Through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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A Novel based Hybrid smart System for detecting the cyber-attacks on IOT's

A Novel based Hybrid smart System for detecting the cyber-at...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Gnanaselvi, K. Juliana Shukla, Surendra Kumar Kumar, Keerthipati Gehlot, Anita Jaiswal, Tarun Geetha, B.T. Rathinam College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India University of Technology and Applied Science Department of Information Technology Al Masnaah Oman Uttaranchal University Uttaranchal Institute of Technology India Department of Computer Application India Saveetha School of Engineering Simats Department of Ece Tamil Nadu India

ISBN: (纸本)9798350322309

The Iot technology (IoT) is rapidly advancing sufficiently that it might have a larger effect on huge industrial infrastructure and everyday routines. However, this became the focus of fraudsters, whom made IoT into such a prey for their misbehaviour, thereby potentially opening the door for an assault on the computer systems. Have used a traditional intrusion detection device to manage the Iot network is problematic because of the enormous variety and quantity of IoT devices. A revolutionary aggregation Interdisciplinary Detection System (HIDS) that merges a C5 predictor with the a One Category Classification Algorithm is built to guard IoT devices. It project's goal is to find threats that can be inhibited with high classification certainty and low completely bogus rates, for both storing and accessing and negligible vulnerabilities. The User database, consisting includes include legal IoT data stream and other varieties of assaults, is employed to assess the specified HIDS. Studies show that when combined with SIDS and Assistance methodologies, the proposed hybrid IDS provides a higher accuracy results and a smaller percentage of false positives. © 2023 IEEE.

关键词： Fault detection

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