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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Prema, S. Solaimalai, Gautam Lalithambikai, S. Priya, S. Gayathri Sowmya, P.D. Thanikodi, Sathish Mahendra Engineering College Department of Information Technology Tamil Nadu Namakkal637503 India U.S. Bank GA30041 United States Knowledge Institute of Technology Department of Information Technology Tamil Nadu Salem637504 India R.M.D. Engineering College Department of Electronics And Communication Engineering Tamil Nadu Kavaraipettai601206 India Reva University Department of Computer Science & Applications Karnataka Bengaluru560064 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

As the need for data transfer in wireless networks grows, cognitive radios are likely to meet a large part of that need. Cognitive radio nodes can sense what's going on around them, figure out what's best for using the radio spectrum by allocating resources in terms of time, frequency, and space, and then act on that information. AI and machine learning are often combined with cognitive radio to create smart and flexible sharing, which makes real-time operations more effective. This research talks about cognitive radio networks and their goals, problems, resources, and limits. There is a research into how learning affects cognitive radios and methods for AI and machine learning are shown. This section gives an outline of the newest research on how machine learning can be used in cognitive radios. A number of AI techniques are used to organize the literature review, such as neural networks, fuzzy logic, case-based reasoning, entropy, Bayesian analysis, Markov models, multi-agent systems, and artificial bee colony algorithms. Cognitive radio uses and possible learning barriers are also talked about. © 2024 IEEE.

关键词： Cognitive radio

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Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers

arXiv

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arXiv 2023年

作者： Liu, Huafeng Dowdell, Benjamin Engelder, Todd Pulmano, Zarah Osa, Nicolas Barman, Arko Department of Computer Science Rice University United States Data to Knowledge Lab Department of Electrical and Computer Engineering Rice University United States

Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for the diagnosis and treatment of brain cancers such as glioblastoma. Accurate tumor segmentation in MRI images is often required for treatment planning and risk assessment of treatment methods. Here, we propose a novel pipeline, Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages MaskFormer, a vision transformer model, and generates robust tumor segmentation maks. We use an ensemble of nine predictions from three models separately trained on each of the three orthogonal 2D slice directions (axial, sagittal, and coronal) of a 3D brain MRI volume. We train and test our models on the publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI (mpMRI) scans from 611 subjects. Using Dice coefficient (DC) and 95% Hausdorff distance (HD) for evaluation, our models achieved state-of-the-art results in segmenting all three different tumor regions – tumor core (DC = 0.894, HD = 2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812, HD = 1.608). © 2023, CC BY-SA.

关键词： Tumors

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computer Vision and Its Algorithmic Evolution in Image Understanding 7

Computer Vision and Its Algorithmic Evolution in Image Under...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Smitha, B.S. Jain, Alok Sreenivasulu, D. Sharma, Saurabh Reddy, B. Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Automobile Engineering Bangalore India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

As computer vision systems have improved, mostly through machine vision progress, people's views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. The manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. This study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. This shows that it has the ability to make computer vision better. © 2024 IEEE.

关键词： Convolutional neural networks

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Algorithmic Intelligence Instantaneously for Complex Real-Time Supply Chain Optimization

Algorithmic Intelligence Instantaneously for Complex Real-Ti...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Lalitha, G. Rajora, Himanshu Kumar Asha, V. Nijhawan, Ginni Praveen Al-Shami, Karrar R. Institute of Aeronautical Engineering Hyderabad Dundigal India GyanGanga Institute of Technology and Sciences Docse M.P Jabalpur India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

The modern world of supply chain management is complex and ever-evolving, thus the ability to make informed decisions and react quickly are of the utmost importance. In response to these issues, we have created a ground-breaking method that we call 'Dynamic Real-Time Supply Chain Optimization with Algorithmic Intelligence (DRT-SCAI).' Better real-time visibility, autonomous decision-making, predictive accuracy, efficiency, customer happiness, risk mitigation, and sustainability are all outcomes of this suggested methodology's use of state-of-the-art algorithmic intelligence, which simultaneously optimizes the supply chain in an instant. By using this approach, several enhancements are realized. To achieve these improvements, it is necessary to use this strategy. © 2024 IEEE.

关键词： Customer satisfaction

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Impact of Hyperparameter tuning on Solar PV Performance using Machine Learning for Moscow, Russia 26

Impact of Hyperparameter tuning on Solar PV Performance usin...

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26th International Conference on Soft Computing and Measurements, SCM 2023

作者： Okoli, Kingsley Uche, Onah Ibukun, Haastrup Adebayo Achirgbenda, Victor Joseph, Innocent Saint Petersburg Electrotechnical University LETI Department of Computer Science and Knowledge Discovery Saint Petersburg Russia Saint Petersburg Electrotechnical University LETI Department of Photonics Saint Petersburg Russia Federal University of Technology Department of Mechanical Engineering Ondo State Akure Nigeria University of Agriculture Department of Mechanical Engineering Benue State Makurdi Nigeria

ISBN: (纸本)9798350322484

The usage of solar photovoltaic (PV) systems has become a viable option for supplying the worlds expanding energy needs while lowering greenhouse gas emissions. The efficiency and cost-effectiveness of the system must be designed and optimized, which requires accurate modeling of solar PV performance. In order to choose the best hyperparameters for a particular situation, it is important to have a deeper grasp of how hyperparameter tuning affects solar PV model performance. In this paper, the impact of hyperparameter tuning on the effectiveness of solar PV modeling for Moscow, Russia, using machine learning approaches is investigated. For the purpose of predicting the performance of the solar PV system, the data and various machine learning models are being analyzed. These models performance is assessed using a variety of criteria while contrasting its results during hyper tuning. The accuracy and dependability of the solar PV performance models can be greatly increased, according to the research, by hyperparameter tuning. We outline the implications and prospective effects of our study on the design and optimization of solar PV systems in Moscow, Russia, and make suggestions for further study. The findings of this study would help Russia and other regions design solar PV systems that are more effective and sustainable. © 2023 IEEE.

关键词： Cost effectiveness

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Evaluation of Modified VGG16 Learning Model for Classifying Skin Cancer Lesions

Evaluation of Modified VGG16 Learning Model for Classifying ...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Mateen Naser Alaubaidy Abdulqadir Ismail Abdullah Fatimah Mousa Alwan Department of Information Technology College of Engineering and Computer Science Lebanese French University Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Kurdistan Region Iraq Department of Computer Science College of Engineering and Computer Science Bayan University Erbil Kurdistan Region Iraq

One of the most serious types of cancer that is prevalent around the world is skin cancer. It is widely known that skin cancer characterized by uncontrolled cell and tissue growth, presenting a challenge in detecting or diagnosis skin cancer in early stage. Accurate segmentation of skin lesions in dermatoscopic images holds great importance in diagnosis and assessment of skin cancer. Thus, Fast, precise, and real-time algorithms are essential in effectively detecting skin cancer to aid physicians in their treatment decision-making process. In this paper, we evaluated the performance of the modified VGG16 with using three common convolutional neural networks(CNN) in detecting the skin cancer with dermoscopic skin lesions images. In this study, the Kaggle dataset is used to classify skin cancer images. To classify skin lesions, the model modified VGG16 is compared to the most common models such as VGG16, mobileNetV2 and Inception ResNet v2. All models are trained on the same dataset and their performance is evaluated using metrics such as: accuracy, precision, F1-score, and recall. Finally, The comparative analysis revealed a promising performance of the modified VGG16 model in detecting multiclass skin cancer, which highlights its promising potential in classifying.

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence

Performance Analysis of Health Care Expense Anticipation usi...

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Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Gopalakrishnan A S. Kumarganesh Divya Bharathi G S. Anthoniraj Suresh Kumar Natarajan Saikat Gochhait Department of Artificial Intelligence and Data Science Knowledge Institute of Technology Salem India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Mahendra Institute of Technology Namakkal India School of Computer Science and Engineering Jain (Deemed-to-be University) Ramanagara India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University Pune Maharashtra

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems.

关键词： Surveys Costs Accuracy Biological system modeling Linear regression Cancer treatment Surgery Medical services Predictive models Strain

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Cryptosystem using Facial Landmark for Authentication Pairing and Key Generation in Bluetooth Security 2

Cryptosystem using Facial Landmark for Authentication Pairin...

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2nd Global Conference for Advancement in Technology, GCAT 2021

作者： Mondal, Asif Ikbal Mandal, Bijoy Kumar Choudhury, Amitava Dumkal Institute of Engineering and Technology Department of Computer Science and Engineering India Nshm Knowledge Campus Department of Computer Science and Engineering West Bengal Durgapur India University of Petroleum and Energy Studies School of Computer Science Dehrdaun India

ISBN: (纸本)9780738132150

In this paper, the whole system starts with a real time picture of the sender and the receiver. The pairing is done utilizing the image of the sender and the receiver at the place of the pin used for pairing. The key generation for the encryption also utilizes the facial characteristics of the parties involved. This method will always generate a key of variable length. As the same person taking the snap of his/her face will change the coordinates every time making it is hard to predict the exact key. The text converted into ciphertext is also padded from both sides with variable bits to make it hard to identify the starting point of the ciphertext to start the actual decryption. The overall aim of this research is to develop a cryptosystem that can be used for achieving good encryption even if the length of the plain text is very small(one or two words). It is an encryption system with associated authentication. An electronic device cannot distinguish between its real users and fake one just by looking at the number which is used for the authentication process but if trained to identify the picture of the used the device up to a certain point is secure. Next thing if someone has requested a pairing merely by looking at the numbers one cannot predict that a legal connection is made but by looking at the real-time image of the person the same can be done with satisfaction. Previously used encryption used key and ciphertext of fixed length but here, a variable ciphertext and key are created in every session of transmission as the image of every sender and the receiver is different. © 2021 IEEE.

关键词： Cryptography

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Smart Blind Stick for Visually Impaired People using IoT 1

Smart Blind Stick for Visually Impaired People using IoT

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Kaushal, Rajanish Kumar Tamilarasi, K. Babu, P. Mohanaprakash, T.A. Murthy, S.E. Jogendra Kumar, M. Department of Electrical Engineering Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering VIT University Tamil Nadu Chennai600127 India Department of Information Technology PSNA College of Engineering and Technology Tamil Nadu Dindigul624622 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Knowledge Institute of Technology Tamil Nadu Salem637504 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9781665460842

The existing assistive device used by the visually impaired, a stick, is insufficient for allowing them to live independently. Getting from one place to another, whether indoors or outside, is a huge issue for persons who are visually impaired. And the traffic conditions make their everyday journeys more onerous. Consequences such as falling over stationary objects, relocating barriers, or missing a traffic light can only be avoided if they maintain a constant state of vigilance. For the visually challenged, technology can solve these problems. The proposed method employs the Internet of Things (IoT) paradigm to serve as a link between the visually handicapped and their surroundings. An ultrasonic sensor may be used to scan the surroundings for possible obstacles, while an ESP32 camera collects the traffic signal image. An Artificial Neural Network (ANN) model is particularly tailored for the task to detects the traffic signal. The speaker is permanently attached to the controller's output and can alert the user about obstacles and traffic signals. The next step is to confirm whether the person is roaming within the nearer area or not. If the person is navigating a long distance away from the house, the system will send a text message to the registered mobile number. The suggested smart blind stick is both simple to use and cost-effective due to the incorporation of several types of sensors and modules with IoT. © 2022 IEEE

关键词： Cameras

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Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services 7

Blockchain and Machine Learning for Optimized Resource Alloc...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Rangaiah, Yagateela Pandu Yaragudri, Hanamant Rani, K.Pushpa Singh, Navdeep Albawi, Ali Kalyani, Susmita Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201315 India

ISBN: (纸本)9798350350067

Resource efficiency becomes increasingly critical as cloud computing services alter. This article uses bitcoin and machine learning to optimize resource utilization. We study PRDA, BSCA, and DRAA algorithms and propose 'Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services.' We employ blockchain, machine learning, and smart contracts to make resource sharing safer, clearer, and adaptable to cloud contexts. A comparative test demonstrates our resource allocation strategy works. PRDA reliably predicts resource usage using real-time data and prior patterns. This greatly reduces expenses and waste. The BSCA ensures safe and transparent delivery via guidelines. In multi-tenant cloud systems, DRAA optimizes resource consumption via reinforcement learning. This fixes underuse, exorbitant pricing, and speed loss. With this strategy, cloud service providers can remain open, keep rates low, and serve customers promptly. © 2024 IEEE.

关键词： Adversarial machine learning

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