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VGG16-PCA-PB3C: A hybrid PB3C and deep neural network based approach for leukemia detection

International Journal of Information Technology (Singapore) 2024年第6期16卷 3605-3615页

作者： Kaur, Navpreet Singh, Amar School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India School of Computer Application Lovely Professional University Punjab Phagwara India

In recent decades, the leukemia detection using deep learning and convolutional neural networks have shown to be more effective as the main and the important features are retrieved by these methods. Early detection of leukemia is crucial for timely treatment and improved outcomes. Although leukemia detection using convolutional neural networks has been demonstrated to be effective, there is still potential for improvement in terms of accuracy and efficiency of the model. The main objective of this paper is to present a novel Hybrid Deep Neural Network, termed Visual Geometry Group16-Principal Component Analysis-Parallel Big Bang Big Crunch (VGG16-PCA-PB3C), for the accurate and timely detection of leukemia. This approach integrates Visual Geometry Group (VGG16) for robust feature extraction, Principal Component Analysis (PCA) for effective dimensionality reduction by minimizing the amount of training time and enhancing the accuracy, and the innovative Parallel Big-Bang-Big-Crunch (PB3C) optimization algorithm to address computational complexities during training. The integration of these techniques aims to improve the accuracy and effectiveness of leukemia detection. We evaluated our proposed framework on Classification of Normal versus Malignant Cells (CNMC) dataset which has 15,384 blood smear images. For the leukemia detection, VGG16-PCA-PB3C achieved an accuracy of 95%, and a precision of 94.0%. It is demonstrated by the experimental results that the classifier's performance has increased, and the suggested model performed better as compared to the other popular image classification approaches. The proposed framework can be applied to create accurate and efficient models for leukemia detection, which can assist medical professionals in the diagnosis of leukemia. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Deep learning Leukemia detection PB3C PCA VGG16

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Unveiling factuality and injecting knowledge for LLMs via reinforcement learning and data proportion

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science China(Information sciences) 2024年第10期67卷 389-390页

作者： Wenjun KE Ziyu SHANG Zhizhao LUO Peng WANG Yikai GUO Qi LIU Yuxuan CHEN School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Beijing Institute of Technology Zhuhai Beijing Institute of Computer Technology and Application

Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].

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Applying Bayesian belief in LoRa: smart parking case study

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7857-7870页

作者： Gupta, Sakshi Snigdh, Itu School of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

With the popularity of LoRa, many IoT applications are now being realized with this Low Power Wide Area Network (LPWAN) technology. Apart from ensuring low power and long-range communications, LoRa is also interference-free and hence an obvious choice for applications in urban scenarios. We dedicate our study to analyze and infer the impact of LoRa parameters like SF (Spreading Factor), BW (Bandwidth), CR (Coding Rate) on network performance metrics like delay, throughput for a smart parking scenario. Suited to the smart parking case study, we also present network performance observations for varied LOS settings (Line-of-Sight), NON-LOS, and obstruction. We try to identify the most significant parameters under different network conditions and determine the relationship among them through canonical correlation through our article. We also propose a Bayesian belief network to infer the best possible combination of LoRa parameters for achieving high-quality network efficiency under the environment scenarios. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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A Novel Advanced Transfer Learning Based Approach for Leukemia Detection

A Novel Advanced Transfer Learning Based Approach for Leukem...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kaur, Navpreet Singh, Amar School of Computer Science and Engineering Lovely Professional University Phagwara India School of Computer Application Lovely Professional University Phagwara India

ISBN: (纸本)9798331542344

Transfer learning has been adopted more widely to automate detection in recent years. It enables trained models to work well on new data with great transfer learning benefits. In this study, novel advanced transfer learning technique is proposed to identify leukemia. An integration of ResNet50, Principal Component Analysis (PCA) and Big Bang Big Crunch (BBBC) optimization algorithm is proposed. The early, accurate detection of leukemia is no small feat in medical imaging-especially when it comes to huge amounts of complex image data. A transfer learning model called ResNet50 which extracts important features of microscopic images of leukemia cells is used. As there are too many features which made it complex to understand and interpret, PCA is used to reduce some dimensionality and selected only the best few parameters for the further analysis. In addition, the BBBC algorithm inspired by theories concerning the expansion and contraction of the universe is finally utilized for the optimization to take place efficiently and acutely. Experiments on a CNMC_2019 dataset of leukemia demonstrated the superiority and accuracy of the proposed method when compared with other classification methods, especially in terms of reliability. The findings indicate that such an approach may enhance the early diagnosis of leukemia, enabling more reliable diagnosis and individualized treatment planning. © 2024 IEEE.

关键词： Federated learning

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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An Integrated Security VANET Algorithm for Threat Mitigation and Performance Improvement Using Machine Learning

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SN computer science 2024年第8期5卷 1089页

作者： Khanna, Heena Kumar, Mukesh Bhardwaj, Vivek School of Computer Application Lovely Professional University Punjab Phagwara 144411 India Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India School of Computer Science and Engineering Manipal University Jaipur Jaipur 303007 India

Vehicular Ad-Hoc Networks (VANETs) have emerged as a captivating field of research due to the escalating number of vehicles on the road in recent years. Ensuring a secure Intelligent Transportation System (ITS) is imperative for passenger and driver safety. However, the dynamic nature of VANETs presents challenges for real-time implementation. This study proposes an enhanced security algorithm tailored for VANETs, adept at mitigating threats such as Denial of Service Attacks (DoS), Sybil, and Replay attacks. The proposed method employs an Enhanced K-Means approach to form clusters for diverse attacks, coupled with a hybrid technique utilizing Support Vector Machine (SVM) and Feed-forward backpropagation for classifier accuracy assessment which incorporates firefly algorithm for optimization. The findings demonstrate notable enhancements in terms of Throughput, Jitter, True Detection Rate, and Packet Delivery Ratio (PDR). Lastly, we delineate future directions and identify open issues for further investigation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DoS Machine learning Security Support vector machine VANET

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Predicting Bank Loan Approval with Artificial Intelligence 1

Predicting Bank Loan Approval with Artificial Intelligence

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Ratan, Raj Yadav, Sudeept Singh Malhotra, Ritika Kumar, Avneesh Galgotias University School of Computer Application and Technology Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

In the contemporary financial landscape, banks play a crucial role in facilitating individuals and businesses with their monetary needs. Central to their operations is the assessment of borrower creditworthiness through loan approval processes, ensuring the security of loans extended. However, traditional manual assessment is not only time-consuming but also prone to human error. Therefore, there is an urgent need to develop an automated and reliable credit approval system. In this study, diverse machine learning calculations are compared to decide the ideal show for anticipating whether a bank ought to endrose a advance for a person. Employing a dataset gotten from Kaggle, diverse machine learning methods to a degree logistic reversion, decision trees, K-nearest neighbors (KNN), haphazard woodlands and neural networks are exploited. The animation of these models is evaluated handling measurements to a degree veracity, accuracy, review and F1 score. In particular, the influence of the model is improved by taking into account a number of factors that include the borrower's personal characteristics, such as tax liabilities, purpose of the loan, credit history, loan amount and home ownership, in addition to traditional checking account information. This holistic approach allows for a more accurate estimation of the likelihood of loan default. Furthermore, the logistic regression model proves to be the top performer with an impressive accuracy of 81.89%. Additionally, a trait importance analysis is performed to identify the variables that have the greatest impact on loan approval decisions. The results underline the superiority of ML models over conventional statistical approaches and illustrate their potential to significantly improve the accuracy of loan forecasting. © 2024 IEEE.

关键词： Logistic regression

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Transforming Agriculture with Cloud Computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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Responsible AI Framework For Large Language Models (LLMs) 11

Responsible AI Framework For Large Language Models (LLMs)

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Kandhari, Naman Tripathi, Bharat Kumar, Sheetesh Singh, Kulvinder Singh, Narendra Pal Chandigarh University Department of Computer Science & Engineering Punjab India Allenhouse Business School Department of Computer Application UP India Invertis University Bareilly Department of Computer Science & Engineering UP India

ISBN: (纸本)9798350379136

Large language models (LLMs) have become quite popular in recent years due to their remarkable capacity to produce language that is human-like. The creation and use of LLMs, however, also bring up serious ethical questions on topics like prejudice, discrimination, privacy, security, transparency, and unexpected consequences. Responsible AI frameworks for LLMs are becoming more and more necessary in order to solve these issues. A responsible AI framework can offer direction on how to create and use LLMs in a way that is morally right, just, and advantageous for everyone. This paper covers important factors for creating responsible AI frameworks for LLMs and emphasizes the need for such frameworks. Transparency, justice, privacy, responsibility, safety, and social duty are some of these factors. We can ensure that these models are created and used in a way that is consistent with societal values and goals and that they help to create a better and more just world by adopting a responsible AI framework for LLMs. © 2024 IEEE.

关键词： Differential privacy

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare 1

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Indrakumari, R. Pandey, Vishal Sinha, Ashish Kumar Kumar, Ankesh Galgotias University School of Computer Science and Engineering India Galgotias University School of Computing Application and Technology India

ISBN: (纸本)9798350356816

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations. © 2024 IEEE.

关键词： Electronic health record

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