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Automated Malaria Detection Using Deep Learning: A Convolutional Neural Network Approach for Microscopic Cell Image Analysis

Automated Malaria Detection Using Deep Learning: A Convoluti...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Mahajan, Manish Kalra, Amandeep Singh Bansal, Ankit Bhattacherjee, Abhishek Vishal Jain, Eshika Amity School of Engineering and Technology Amity University Punjab Mohali India Gulzar Group of Institutions Punjab Ludhiana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara India Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798350369106

This study explores using a Convolutional Neural Network (CNN) for automated malaria detection from microscopic cell images. A dataset of 27,558 images was split into training, validation and testing subsets, all with balanced class representation. The testing dataset was then used to evaluate the model, which achieved an overall accuracy of 94.81% and precision, recall and F1 scores of 0.94 and 0.94 for classes 'Uninfected' and 'Parasitized', respectively. The confusion matrix showed that it was reliable, especially for minimizing the false negatives for infected cases. These results highlight the potential of the model to decrease dependence on manual microscopy, increase diagnostic consistency and enable large-scale malaria screening. Through this research, we show the feasibility of integrating deep learning into malaria detection workflows while expanding access to healthcare in underserved regions. Future works include deployment optimization, integration with mobile applications, and validation on multiple site datasets to increase generalizability. The results of this work pave the way for artificial intelligence-powered diagnostics, a burgeoning field, and present a cost-effective, efficient, and scalable solution to combat malaria in high-burden areas. © 2024 IEEE.

关键词： Automated Diagnosis Convolutional Neural Network (CNN) Deep Learning in Healthcare Malaria detection Microscopic Cell Image Analysis

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Garima Shukla Vanshaj Awasthi Prafull Goswami Sofia Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Maharashtra India Department of Computer Science and Engineering Indian Institute of Information and Technology Sonipat Department of Artificial Intelligence Amity School of Engineering and Technology Amity University Noida Uttar Pradesh

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies

关键词： Wireless communication Surveys Electric potential Accuracy Inductive charging Central nervous system Wireless power transfer Machine learning Electromagnetic fields Biological neural networks

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Interpretation of Sign Language Using Machine Learning

Interpretation of Sign Language Using Machine Learning

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Palak Srivastava Bramah Hazela Shikha Singh Pallavi Asthana Deependra Pandey Kamlesh Kumar Singh Vineet Singh Department of Computer Science Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh India Department of Electronics & Communication Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments.

关键词： Sign language Technological innovation Accuracy Education Machine learning Auditory system Medical services Real-time systems Convolutional neural networks Long short term memory

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Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images

Crafting a Machine Learning Model for Discerning Synthetic a...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Misha Shah Gautam Jaiswal Dolly Sharma Amity School of Engineering and Technology Amity university Noida India Department of Computer Science and Engineering Amity university Noida India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of $\mathbf{2 . 7}$ million images, the model aims for high accuracy in classification tasks.

关键词： Training Attention mechanisms Accuracy Computational modeling Data augmentation Data models Convolutional neural networks Ensemble learning Load modeling Residual neural networks

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Anshul Atre Kamlesh Kumar Singh Brijesh Kumar Chaurasia Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Electronics Communications and Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes.

关键词： Cloud computing Protocols Processor scheduling Quality of service Signal processing Internet of Things Telemetry Security Resource management Optimization

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An Approach to Generate Music using Genetic Algorithm 10

An Approach to Generate Music using Genetic Algorithm

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Gupta, Sumita Majumdar, Rana Gambhir, Sapna Amity School of Engineering & Technology Amity University Department of Computer Science & Engineering Noida India Sister Nivedita University Department of Computer Science and Engineering New town Kolkata India J.C. Bose University of Science and Technology Department of Computer Engineering Faridabad India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Music is a very vast domain with various notes, chords, and octaves. Music theories are very complex in nature and are difficult to understand by a layman, in this research paper the aim was to implement Genetic algorithm for generating fresh and unique music generation which is independent of human intervention. The work will automate the music generation capabilities. The algorithm works on different music parameters like pitch, frequency, beats, and notes in order to generate more relatable and interesting melodies by understanding the relationship between different given parameters. The algorithm can generate music melodies with constant rating analysis of the music generated by the algorithm. Each analysis phase helps in better and synchronized music generation. The algorithm takes inspiration from the theory of Natural selection. © 2022 IEEE.

关键词： Genetic algorithms

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Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure Recognition: A 1D-CNN LSTM Approach

Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Mantra Jain Harshit Bhardwaj Ansh Srivastav Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The intricate neurological condition known as epilepsy, which is common across the world, presents consid-erable difficulties in accurately identifying and differentiating between non-epileptic and epileptic activity using electroen-cephalograms (EEGs). To customize successful therapies, it is essential to accurately identify the kinds of epileptic activity. Since epilepsy affects about 50 million people worldwide according to latest update of WHO and is typified by spontaneous seizures, early identification and prediction are vital in enabling people to minimize possible harm. The current seizure prediction methods lacks, with particular emphasis on their limited performance on small training datasets and their disregard for time-series data. To identify small patterns in EEG data, this study combines cutting edge technologies, like Long Short-Term Memory (LSTM) and 1D-CNN (Convolutional Neural Network) leveraging data from five hundred patients acquired from the UCI Machine Learning Repository. To optimize the 1D-CNN LSTM architec-ture and hyper-parameters, Bayesian optimization is employed, allowing for efficient exploration of the parameter space. It's effectiveness is not only limited to enhancing the performance metrics of a particular model but also minimizing the computing power required for fine tuning. The research evaluates the effectiveness of the 1D-CNN LSTM-based model, showcasing its potential as a reliable tool for automated epilepsy detection with accuracy of 99.47% (~ ~ 100%), average sensitivity of 99.45%, and average specificity of 99.57%. This approach, emphasizes the significance of anticipating seizures in advance, attempts to provide epileptics the tools they need to control and avoid seizures in advance, so ultimately enhancing their quality of life for patients.

关键词： Training Computational modeling Epilepsy Brain modeling Electroencephalography Data models Reliability

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Review of Crowdfunding: Historical Evolution, Societal Implications, and Architectural Perspectives

Review of Crowdfunding: Historical Evolution, Societal Impli...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Kajal Joseph Deepa Parasar Computer Science and Engineering Department Amity School of Engineering and Technology Amity University Maharashtra Mumbai Maharashtra India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study performs an analysis on crowdfunding. It explores how crowdfunding began, how it's impacting society, the challenges it faces, and how these platforms are designed. Following a detailed historical review of crowdfunding platforms' emergence, the paper explores their significant impacts on investors, enterprises, and society at large. By reviewing relevant literature, the study identifies research gaps, thus establishing a foundation for future investigation. The analysis delves into architectural designs and frameworks, while simultaneously investigating the difficulties faced by businesses during crowdsourcing campaigns. Finally, the study synthesizes key findings and their implications for academics, practitioners, and policymakers within the domain of crowdsourcing. Decentralized crowdfunding has fundamentally transformed the investment landscape, revolutionizing traditional models and unlocking novel funding avenues for early-stage ventures. Examining this evolution in detail, with a specific focus on the AngelicCrowd platform and its impact on angel investors, this study aims to provide a holistic understanding of the dynamics within the decentralized crowdfunding ecosystem. Through analysis of key trends, obstacles, and opportunities, the research investigates factors influencing adoption, the role of blockchain technology and smart contracts, and impediments to widespread implementation. The analysis is informed by a diverse dataset, incorporating responses from angel investors utilizing AngelicCrowd and similar platforms. Additionally, the paper explores the repercussions of these developments on conventional funding strategies and the democratization of investment.

关键词： Surveys Technological innovation Crowdfunding Uncertainty Reviews Social networking (online) Smart contracts

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Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics

Predictive Analytics in Sports: Using Machine Learning to Fo...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Jhankar Moolchandani Vikrant Chole Santosh Sahu Rahul Kumar Anshita Shukla Ashok Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models; thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034.

关键词： Analytical models Adaptation models Accuracy Linear regression Predictive models Market research Data models Predictive analytics Random forests Sports

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Early Detection of Cognitive Skill Impairments using Machine Learning

Early Detection of Cognitive Skill Impairments using Machine...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sunita Patil Swetta Kukreja Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Maharashtra Mumbai Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Cognitive skills are essential for everyday tasks, encompassing mental abilities such as memory, problem-solving, and logical thinking. Conditions such as depression, anxiety, and schizophrenia can impair these skills, affecting memory, attention, and problem-solving abilities. The COVID pandemic and other stress-inducing events have a great impact on individuals, often leading to stress-related diseases. Depression often stems from heightened stress levels, yet individuals may struggle to recognize or seek help due to social stigma. Delayed treatment impairs outcomes. Surveys indicate low success rates in treating cognitive skill impairment. Early identification through routine cognitive testing is crucial, especially for vulnerable individuals due to age, family history, or health conditions. Regular screenings facilitate proactive healthcare management, leading to improved outcomes in patients with cognitive skill impairments. This study explores the application of Support Vector Machine (SVM) algorithms for the early detection of cognitive skill impairments, aiming to develop accurate prediction models for identifying individuals at risk of cognitive decline. By employing feature selection methods, the SVM achieved an 83% accuracy rate. This study aimed to advance the development of efficient cognitive assessment tools, enabling early intervention and personalized care for individuals vulnerable to cognitive impairment.

关键词： Support vector machines COVID-19 Accuracy Pandemics Predictive models Prediction algorithms Feature extraction Depression Problem-solving Testing

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