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A Step Towards Automated Haematology: DL Models for Blood Cell Detection and Classification

EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Rahat, Irfan Sadiq Ahmed, Mohammed Altaf Rohini, Donepudi Manjula, A. Ghosh, Hritwik Sobur, Abdus VIT-AP University Andhra Pradesh Amaravati India Department of Computer Engineering College of Computer Engineering & Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Dept. of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur Dist Andhra Pradesh Vaddeswaram522502 India Department of Computer Science & Engineering Jyothishmathi Institute of Technology and Science Telangana Karimnagar India Masters of Information technology Westcliff University United States

INTRODUCTION: Deep Learning has significantly impacted various domains, including medical imaging and diagnostics, by enabling accurate classification tasks. This research focuses on leveraging deep learning models to automate the classification of different blood cell types, thus advancing hematology practices. OBJECTIVES: The primary objective of this study is to evaluate the performance of five deep learning models-ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19-in accurately discerning and classifying distinct blood cell categories: Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The study aims to identify the most effective model for automating hematology processes. METHODS: A comprehensive dataset containing approximately 8,500 augmented images of the four blood cell types is utilized for training and evaluation. The deep learning models undergo extensive training using this dataset. Performance assessment is conducted using various metrics including accuracy, precision, recall, and F1-score. RESULTS: The VGG19 model emerges as the top performer, achieving an impressive accuracy of 99% with near-perfect precision and recall across all cell types. This indicates its robustness and effectiveness in automated blood cell classification tasks. Other models, while demonstrating competence, do not match the performance levels attained by VGG19. CONCLUSION: This research underscores the potential of deep learning in automating and enhancing the accuracy of blood cell classification, thereby addressing the labor-intensive and error-prone nature of traditional methods in hematology. The superiority of the VGG19 model highlights its suitability for practical implementation in real-world scenarios. However, further investigation is warranted to comprehend model performance variations and ensure generalization to unseen data. Overall, this study serves as a crucial step towards broader applications of artificial intelligence in medical diagnostics, particularly in t

关键词： Medical imaging

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Advancing Data science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jahir Pasha, M. Gaurav, Kumar Bhanja, Amit Kumar Shamout, Mohamed Dawood Mupparaju, Chinna Babu Manohar Bhanushali, Mahesh G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Lovely Professional University Phagwara India School of Management Sciences Department of Management Varanasi India College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates University of Central Missouri Masters in Computer Science warrensburg United States Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane India

ISBN: (纸本)9798350357769

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal Language Processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (CV), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (CV) is another region in which advances in AI-pushed information science are being visible. CV generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. computerized facts analysis is one of the maximum powerful approaches. © 2023 IEEE.

关键词： Analyzing Records computerized Data Evaluation Semantic Algorithms

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques

Enhancing Medical Image-Based Diagnostics Through the Applic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities 1

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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Security Challenges in Multi-Cloud Environments: Solutions and Best Practices

Security Challenges in Multi-Cloud Environments: Solutions a...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Madanan Pratik Patel Prateek Agrawal Pankaj Mudholkar Megha Mudholkar V. Jaganraja Department of Computer Science Dhofar University Oman National Forensic Sciences University Sr. Solution Architect Masters of Computer Applications Gurukula Kangri (Deemed to be University) Faculty of Computer Applications Marwadi University Rajkot Gujarat India Department of Computer Engineering Marwadi University Rajkot Gujarat India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The aim of this research paper is to investigate how machine learning can be incorporated to improve security around multi-clouds, with specific reference to the challenges. Based on the datasets obtained from Kaggle, this study uses both unsupervised learning, more specifically the Isolation Forest technique and supervised learning, such as regression-based forecast for identifying possible security threats. The evaluation shows that through the use of ML techniques risk that originates from multi-cloud systems may be well managed while, at the same time, the utilization of cloud resources could be enhanced to produce the best results. The results make it possible to conclude that machine learning still holds a huge potential in the sphere of cloud security and provides a range of recommendations and recommendations for organizations that have to manage their multi-cloud environment. Possible future studies are devoted to the investigation of deep learning models and the incorporation of blockchain technology for more security and effectiveness of the system.

关键词： Deep learning Machine learning algorithms Supervised learning Organizations Forestry Real-time systems Blockchains Security Cyberattack Unsupervised learning

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Optimization of Multi-Channel Quartz Crystal Microbalance for Gas Sensing Application 8

Optimization of Multi-Channel Quartz Crystal Microbalance fo...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Zainuddin, Ahmad Anwar Bhattacharjee, Soumik Md Ralib, Aliza Aini Mohd Sukri, Nurain Sufi Sabreena Binti Kamarudin, Saidatul Izyanie Subramaniam, Krishnan Handayani, Dini Reyasudin Basir Khan, M. Muhammad Saifudin, Nik Nor International Islamic University Malaysia Computer Science Kuliyyah of Information Communication Technology Kuala Lumpur Malaysia Universitat Bremen Digital Media Masters Germany International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia Selangor Shah Alam Malaysia Mila University Department of Mechanical Engineering Nilai Negeri Sembilan Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350315516

Mass detection sensor Quartz Crystal Microbalance (QCM) measures mass sensitivity by piezoelectric, high frequency shaving on nanograms these sensors detect with high precise geometry, low cost and ease of production. Conventional QCM sensors are limited to one electrode structure that minimizes the range of sensing. On a single quartz crystal substrate, three new QCM Multi-Channel sensors are developed and optimized that include 2-channel, 3-channel and 4-channel QCM sensors the COMSOL Multiphysics software is used to design and simulate the multi-channel sensors to enhance the device structure for applications with less interference frequency. From the results, it shows that the least interferences exhibit frequency values of 9.58MHz as both QCMs for the 5mm centre-to-centre (c2c). However, due to small c2c distances that decrease accuracy and operating disadvantages, this design was not selected. For 3 channel sensors, however, three resonant peaks show a similar value of 9.58MHz when c2c is maintained at 7mm. With the highest c2c distance this design shows less interference. Finally, for 4-channel sensors, a distance of 6 mm c2c also has a minimum interference with a 9.58 MHz resonance frequency. This shows that 4 channel sensors with the highest c2c can provide greater operational convenience and less interference at frequencies and thus maximize measurement accuracy. Multi-sensor arrays are advantageous when detecting a large number of analytes at the same time or when only selective coatings (coatings that respond to more than one analyte at the same time) are available these sensor arrays, which use only one sensor and combine it with enhanced signal evaluation methods or prior sample separation to achieve the results of multi-sensor arrays, are becoming increasingly popular. © 2023 IEEE.

关键词： Quartz crystal microbalances

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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ARCS-R: Mission Critical Combined Reliability and Cybersecurity Systems Engineering Analysis

ARCS-R: Mission Critical Combined Reliability and Cybersecur...

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Annual Symposium on Reliability and Maintainability (RAMS)

作者： Douglas L. Van Bossuyt Nikolaos Papakonstantinou Britta Hale Ryan Arlitt Srinivasa Rao Palatheerdham Department of Systems Engineering Naval Postgraduate School Docent VTT Technical Research Centre of Finland Department of Computer Science Naval Postgraduate School Masters in Cyber Security University of South Brittany Lorient

This paper explores how reliability analysis and cyber-security analysis can be combined using Artificial Intelligence and Machine Learning (AI/ML), and Large Language Models (LLM) to produce a continuously updated resilience analysis. This is achieved by modeling both the hardware and software of the system, and employing LLMs and AI/ML to continuously search for new software vulnerabilities and feed that information into continuously updating resilience models. A case study of a drone is presented that demonstrates the promise of the proposed method. It is expected that using the proposed method, named Assessment for Risk in Cybersecurity and Safety - Resilience (ARCS-R), will reduce failure rate of mission-critical cyber-physical systems by reducing the likelihood of a potential initiating event causing a prolonged degradation in system performance that impacts system resilience.

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AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Ranita Gangul Sagar Kolekar Advaitha Naidu Chintakunta Sudeep Mungara Computer Science & Engineering (AI& ML) Dayananda Sagar University Bangalore India Department of data Science Stevens Institute of Technology Hoboken NJ U.S.A. Computer Science & Engineering Delaware State University Bangalore India Department of Civil Engineering Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Pune India Department of Computer Science University of North Carolina at Charlotte Charlotte NC U.S.A. Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity.

关键词： Adaptation models Uncertainty Neural networks Medical services Predictive models Diabetes Bayes methods Risk management Indexes Random forests

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