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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmia, K.R. Prithi, S. Vijay, K. Berna, I. Eugene Bhuvaneswaran, B. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing Srm Ist Kattankulathur Campus India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665474672

Many factors-including sunlight, water, soil, and pest control-need to be considered when cultivating crops. The farmer has other responsibilities besides keeping an eye on the seedlings. In this way, the productivity of farming is enhanced by a machine that tends to all the plants with minimal human intervention. The amount of water a plant receives should be proportional to the amount of sunlight it gets. This research work employs the idea of IoT to connect the monitored plant to the network and transmit data. The microcontroller is an Arduino UNO, and components include a photoresistor for light detection and a moisture sensor for measuring the amount of moisture in the soil. The information is transmitted over a network, and the result is viewed using a mobile application. The data that has been collected is displayed in the app. It warns the user when the plant is in danger of drying out due to a lack of water or sunlight. If the crop is dry, a motordriven water pump is used to saturate the soil, and if it's too wet, a motor-operated shutter opens to let the water out. This project intends to make farming as mechanized and labor-free as possible. © 2023 IEEE.

关键词： Crops

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis 4

Tackling the Qubit Mapping Problem with Permutation-Aware Sy...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Liu, Ji Younis, Ed Weiden, Mathias Hovland, Paul Kubiatowicz, John Iancu, Costin Argonne National Laboratory Mathematics and Computer Science Division United States Lawrence Berkeley National Laboratory Computational Research Division United States University of California Department of Electrical Engineering and Computer Science Berkeley United States

ISBN: (纸本)9798350343236

We propose a scalable, hierarchical qubit mapping and routing algorithm that harnesses the power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to be directly resynthesized. For each block, we pre-synthesize them for all permutations of its input and output qubits. Following this offline step, we employ a permutation-aware, block-based generalization of the popular SABRE mapping algorithm. This mapping step stitches together blocks by choosing an input-output permutation that minimizes intrablock gate count and required inter-block communication (SWAP and bridge gates). Our approach has a twofold advantage: 1) circuit synthesis may eliminate more two-qubit gates than other optimizing compilers;2) considering all permutations of input and output qubits eliminates communication operations transparently. In contrast, other mapping algorithms can only introduce communication operations. We show that we can produce better-quality circuits than commercial compilers: shorter by up to 68% (18% on average) fewer gates than Qiskit, up to 36% (9% on average) fewer gates than Tket. We outperform BQSkit, a permutation-unaware, synthesis-based compiler, by up to 67% (21% on average) fewer gates. We also exceed experimental optimal mappers such as OLSQ in quality (10.7% shorter circuits) and time to solution. Our scalable, heuristic approach can be seamlessly integrated into any quantum circuit compiler or optimization infrastructure, and it applies well to any qubit technology, such as superconducting and trapped ions. © 2023 IEEE.

关键词： Conformal mapping

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Design of a sequestration-based network with tunable pulsing dynamics 63

Design of a sequestration-based network with tunable pulsing...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Nakamura, Eiji Samaniego, Christian Cuba Blanchini, Franco Giordano, Giulia Franco, Elisa University of California Department of Mechanical and Aerospace Engineering Los AngelesCA United States Carnegie Mellon University Department of Computational Biology PittsburghPA United States University of Udine Department of Mathematics Computer Science and Physics Italy University of Trento Department of Industrial Engineering Italy

ISBN: (纸本)9798350316339

Incoherent feedforward networks exhibit the ability to generate temporal pulse behavior. However, exerting control over specific dynamic properties, such as amplitude and rise time, poses a challenge and is intricately tied to the network's implementation. In this study, we focus on analyzing sequestration-based networks capable of exhibiting pulse behavior. By employing time-scale separation in the fast sequestration regime, we approximate the temporal dynamics of these networks. This approach allows us to establish a mapping that elucidates the impact of varying the kinetic rates and pulse specifications, including amplitude and rise time. Furthermore, we introduce a positive feedback mechanism to regulate the amplitude of the pulsing response. © 2024 IEEE.

关键词： Time varying networks

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Performance Analysis of Various Machine Learning Techniques for Mental Health Tracking 2nd

Performance Analysis of Various Machine Learning Techniques ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Poongothai Kasthuri Mariammal Yeturu, Jahnavi Department of Computational Intelligence S.R.M Institute of Science and Technology TamilNadu Chennai India Department of Computer Science Arulmigu Subramania Swamy Arts and Science College Thoothukudi628 907 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Avadi Chennai India Department of Computer Science Dr. V. S. Krishna Govt. Degree and PG College Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9783031689048

Artificial intelligence technologies called "machine learning" automatically build models from data in order to improve decision-making. An examination of ML that can be utilized for mental illness treatment has been prompted by the upsurge in mental health diseases and the directive for efficient medical sustenance. The quality of life for patients is improved and treatment outcomes are increased when mental health issues are identified early. Therefore, it is critical to address basic mental health issues that, if left unattended, could develop into more serious issues. The analysis and diagnosis of medical data can today be accomplished with the help of machine learning algorithms. The conventional (CCTV) system for tracking patient activity is ineffective and expensive, and employing a sensor-based system is particularly challenging because of depleted battery life. The real goal is to provide a critical review, and literature review, about all the machine learning techniques which are supervised-based. These techniques include Random Forest Classification, KNN Classifier, Logistic Regression, Decision Tree Classifier, Bagging, Boosting, & Stacking. And to determine the most effective machine-learning technique for monitoring someone’s mental health. It has been found that Accuracy and ROC Curve plays an important role for comparing the ML models. The outcomes clearly show that stacking generated more precise outcomes by showing the accuracy of 82%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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An Improved Cuttlefish Optimization Algorithm Based Faster R-CNN for Brain Tumour Detection 4

An Improved Cuttlefish Optimization Algorithm Based Faster R...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Arunarani, A.R. Bhenia, Vedant Abirami, T. Aparna, N. Abul Ala Walid, Md. Sharada, K.A. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Campus Department of Computational and Intelligence Tamilnadu Chennai India Ips Academy Institute of Engineering and Science Department of Computer Science and Engineering Madhya Pradesh Indore India Kongu Engineering College Information Technology Tamilnadu Erode India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering Khulna Bangladesh Hkbk College of Engineering Vtu Department of Cse Karnataka Bengaluru India

ISBN: (纸本)9798350300888

Brain tumor is caused due to the anomalous development of tissues in the brain. Precise identification of the tumor in premature stage is essential to provide effective medication and curtail further impact of the disease in affected individuals. This present research includes four main stages for the diagnosis of brain tumor including segmentation and classification based on deep learning approaches. The preprocessing stage makes use of filters to reduce the noise impact in the brain imagery. The segmentation stage is responsible to segment the tumor portions from the images using Enhanced Region Growth algorithm. Feature selection stage utilizes Improved Cuttlefish Optimization (ICO) algorithm to extricate the optimal features from the segmented images. The final stage is classification in which Faster R-CNN (FRCNN) algorithm is employed to categorize the brain images as tumor affected or not. BRA TS-2020 dataset is used for the experimental investigation of the proposed FRCNN-ICO method. Performance of this method is assessed using parameters such as Accurate Prediction Rate (APR), Imprecise Prediction Rate (IPR) and Imprecise Denial Rate (IDR). The proposed FRCNN-ICO produces the highest APR value of 99.2% compared to the existing researches on brain tumor detection and proves to perform superior than the methods considered for comparison purposes. © 2023 IEEE.

关键词： Tumors

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Deciphering the Cluster-Specific Marker Genes via Integration of Single Cell RNA Sequencing Datasets

Deciphering the Cluster-Specific Marker Genes via Integratio...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Rinch, Wardah Afzal Sogunmez, Nuray Altaf, Aiman Alsan, Huseyin Fuat Arsan, Taner Kadir Has University Computational Applied Science Engineering Istanbul Turkey Kadir Has University Molecular Biology and Genetics Department Istanbul Turkey Kadir Has University Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350306590

Experimental data from brain tissues are critical for tackling the problems in brain development and revealing the underlying mechanisms of disease states. However, obtaining the brain tissue is a major challenge. Human brain organoids hold remarkable promise for this goal, but they suffer from substantial organoid-to-organoid variability. We performed a data-driven analysis on single-cell RNA-sequencing data using 17775 cells isolated from 2 individual organoids. The main goal was to accurately integrate the data coming from unmatched datasets, cluster the cells based on their similarity levels and predict the differentially expressed genes per cell types to reveal novel brain cell types and markers. This research opens a way to map human brain cells and develop novel and precise machine learning algorithms for accurate scRNA-Seq data analysis. © 2023 IEEE.

关键词： Machine learning

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Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

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computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

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XAI approach to drug dosage optimization and review-based prediction of side effects 1

XAI approach to drug dosage optimization and review-based pr...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Oumlaz, Maroua Kamdar, Iram Oumlaz, Yassine Oukaira, Aziz Benelhaouare, Amrou Zyad Lakhssassi, Ahmed University of Québec in Outaouais Department of Engineering and Computer Science GatineauQCJ8X 3-7 Canada SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Faculty of Medicine and Pharmacy of Casablanca Casablanca Morocco

ISBN: (纸本)9798350351736

One of the major advancements in medicine is 'personalized medicine': a new approach with a promising future that takes into account the unique makeup of each individual patient instead of broad populations. One of the challenges of personalized medicine is the complexity: the more variables to account for, the more complex the adaptation of a certain treatment to a certain patient becomes;thus, the need for artificial intelligence: a tool that can be used to account for the various variables, and perform the computational and mathematical analyses required to succeed in the task of personalizing a certain treatment modality. This study explores explainable artificial intelligence (XAI) for drug dosage optimization based on individual patient data, utilizing advanced machine learning techniques: Decision Trees and Random Forests to be specific. Our approach applies XAI using ML models to provide clear and interpretable dosage recommendations, as well as predicting potential side effects. The methodology proves a high adaptability to diverse patient profiles, achieving an accuracy of 70% in dosage optimization. These results showcase the efficacy of XAI and the utilized algorithms in improving personalized treatment modalities, providing clinicians with a clearer vision, and understandable dosage recommendations for tailored treatment plans. © 2024 IEEE.

关键词： Personalized medicine

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