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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Designing a Pipeline for Predicting Hypothyroidism with Different Machine Learning Classifiers

Designing a Pipeline for Predicting Hypothyroidism with Diff...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Sashikanta Prusty Srikanta Patnaik Sujit Kumar Dash Sushree Gayatri Priyadarsini Prusty Department of Computer Science & Engineering SOA University Bhubaneswar India Department of Electrical & Electronics Engineering SOA University Bhubaneswar India Department of Computer Science & Engineering Sudhananda Group of Institutions Balianta Bhubaneswar India

ISBN: (纸本)9781665478403

Thyroid cancer is caused by abnormal development of thyroid tissue near the thyroid gland's edge. The clinical symptoms of thyroid illness occur when the thyroid gland produces abnormal levels of hormones i.e. hypothyroid (insufficient hormones) and hyperthyroid (excessive hormones). These hormones have a greater impact on the regulation of critical systems including body temperature and heart rate. Thus, the need of thyroid function test (TFT) is required that can examine the blood levels for thyroid-stimulating hormone (TSH) and thyroxine (T4). In this work, the prediction for hypothyroidism disease using different Machine Learning (ML) techniques such as K-NN, XGB, CatBoost, Decision Tree, Naive Bayes, and Neural Network have been implemented and compared to find the best predictive model. Furthermore, a distinguished process has been carried out for finding hypothyroidism, if present in the thyroid gland. After successfully implementing all models, the K-NN achieves the highest accuracy of 98.44%, while detecting hypothyroid disease.

关键词： Heart rate Power engineering Pipelines Neural networks Machine learning Predictive models Biochemistry

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Test Case Generation from UML-Diagrams Using Genetic Algorithm

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computers, Materials & Continua 2021年第5期67卷 2321-2336页

作者： Rajesh Kumar Sahoo Morched Derbali Houssem Jerbi Doan Van Thang P.Pavan Kumar Sipra Sahoo Department of Computer Science&Engineering Ajay Binay Institute of TechnologyCuttack753014India Department of Information Technology Faculty of Computing and ITKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Industrial Engineering University of Ha’ilCollege of EngineeringHail2440Saudi Arabia Faculty of Information Technology Industrial University of Ho chi Minh City700000Vietnam Department of Computer Science and Engineering FSTIFHEHyderabad501203India Department of Computer Science and Engineering ITERSOA Deemed to be UniversityBhubaneswar751030India

Software testing has been attracting a lot of attention for effective software *** model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven *** tools interpret these models into other software artifacts such as code,test data and *** generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the *** paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the *** approach successfully identified defects in various ATM functions such as messaging and operation.

关键词： Genetic algorithm generation of test data and optimization state-chart diagram activity diagram model-driven approach

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Fuzzification-Based Routing Protocol for Wireless Ad Hoc Network 1st

Fuzzification-Based Routing Protocol for Wireless Ad Hoc Net...

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1st International Conference on Intelligent and Cloud Computing, ICICC 2019

作者： Das, Santosh Kumar Patra, Nikhil Das, Biswa Ranjan Das, Shom Prasad Nayak, Soumen Institute Park Pallur Hills Berhampur761008 India Department of Computer Science and Engineering SOA University Bhubaneswar751030 India

ISBN: (纸本)9789811559709

The applications of Wireless Ad hoc Network (WANET) increases rapidly due to its effectiveness. Each node of this network behaves as router to send/receive data packets. In WANET, nodes contain limited capacity of energy, i.e., fail during data transmission, so, due to this issue, network lifetime decreases. In this paper, an intelligent fuzzification-based routing protocol is proposed to solve this issue. Fuzzification is the method of fuzzy logic to reduce the uncertainties of the WANET. At last, the work is evaluated in Java programming language in terms of effectiveness. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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Fuzzy-Based Strategy Management in Wireless Ad Hoc Network 1st

Fuzzy-Based Strategy Management in Wireless Ad Hoc Network

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1st International Conference on Intelligent and Cloud Computing, ICICC 2019

作者： Das, Santosh Kumar Tiwari, Aman Kumar Rath, Somnath Rani, Manisha Das, Shom Prasad Nayak, Soumen Institute Park Pallur Hills Berhampur761008 India Department of Computer Science and Engineering SOA University Bhubaneswar751030 India

ISBN: (纸本)9789811559709

Today’s applications of wireless communication increase rapidly due to its flexibility. Wireless Ad hoc Network (WANET) is one of the variations of the wireless network. The topology of the network is dynamic, so, in this network, parameter variations raise frequently. In this paper, a game theory based on strategy management technique is proposed for WANET. Game theory is a mathematical modeling which is used to manage conflicting strategy of the situation between players. The proposed method is simulated in Python for managing strategy of the nodes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Game theory

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CNN Based Handwritten Odia Character Recognition

CNN Based Handwritten Odia Character Recognition

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Machine Learning, computer Systems and Security (MLCSS), International Conference on

作者： Rabinrayan Panda Sachikanta Dash Sasmita Padhy Mamata Nayak Computer Science and Engineering GIET University Gunupur Odisha School of Computing Science and Engineering VIT Bhopal University Bhopal MP Computer Science and Engineering SOA University Bhubaneswar Odisha

Odia is a 44 million-strong Indo-Aryan language spoken mostly in Odisha, India. Some characters are made up of many symbols that are linked together. Because of its vast variety of applications, Handwritten Character Recognition (HCR) is critical in Optical Character Recognition (OCR) and Pattern Recognition (PR). HCR is employed in fields such as bank checks, medical prescriptions, and tax returns, and contributes greatly to the spread of automation. Handwritten characters are substantially more difficult to discern than printed characters since different people have different writing styles. Character recognition technology may be used to scan a wide range of documents, including handwritten and printed materials. This study investigates and analyses the application of deep learning techniques such as Convolutional Neural Networks for the recognition of Odia characters. CNNs categorise characters by using neurons coupled in several layers to achieve optimal efficiency, which is inspired by the structure of the brain. The goal of this work was to categorise Odia characters using 6 layers of neurons. This method was able to obtain a 95.6 percent accuracy rate. The handwritten Odia characters may be simply translated into English or any other language once they have been identified.

关键词： Deep learning Optical character recognition Neurons Symbols Finance Writing Solids

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Melanocytic Skin Lesion Extraction Using Mean Shift Clustering 1

Melanocytic Skin Lesion Extraction Using Mean Shift Clusteri...

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1st International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021

作者： Rout, Ranjita Parida, Priyadarsan Patnaik, Srikanta GIET University Department of Electronics and Communication Engineering Gunupur Odisha Rayagada765022 India SOA University Department of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665413008

computer vision-based disease diagnosis is the recent trend for skin melanoma detection. This results in early detection resulting in reduced fatalities due to skin melanoma. A number of both supervised and unsupervised computer vision algorithms have been developed. However, a single approach may not apply for all types of dermoscopic images. The algorithm complexity increases while processing color dermoscopic images. Therefore, in this paper, a clustering-based method is developed for melanocytic lesion extraction. The algorithm processes the original dermoscopic images for lesion extraction. Initially, the dermoscopic images are pre-processed for removal of artifacts like hairs, gels, ruler marks etc. Furthermore, it is followed by mean shift clustering technique for lesion feature segregation from that of healthy regions. Moreover, the feature image is post processed using morphological operations for extraction of lesions from dermoscopic images. The proposed algorithm is tested quantitatively and qualitatively via publicly available datasets such as ISIC 2016, ISIC 2017, ISIC 2018 and PH2. The average accuracies for all datasets are 93.76% which can be compared with different approaches developed so far. The results repesent the superiority of the proposed method. © 2021 IEEE.

关键词： Extraction

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DiagPCNN: Enhancing CNN through Pretrained Model for Brain Tumor Diagnosis

DiagPCNN: Enhancing CNN through Pretrained Model for Brain T...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Agrawal, Umang Kumar Panda, Nibedan Ramana, B.V. Singh, Debabrata Dalai, A.K. School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Information Technology AITAM AP Tekkali532201 India Department of CA SOA Deemed to Be University Bhubaneswar751030 India School of Computer Science and Engineering VIT-AP University AP Amaravati India

Analysis of medical images plays a vital role in the early detection and prognosis of diseases. Among various medical imaging, Magnetic Resonance Imaging(MRI) is valuable in providing insights into the brain artefact. Early classification of brain tumors has become indispensable for doctors and medical persons. With the recent advancements in deep learning, Convolution Neural Networks have gained widespread in the domain of disease diagnosis and medical imaging, due to their notable efficacy and high performance. In this study, we have proposed the DiagPCNN model which incorporates a pre-trained model with CNN for an increment in optimal efficacy of the manifest model. We have also analysed the pros and cons of the DiagPCNN model over eighteen different architectures and three DiagPCNN models outperforms the rest of models with superior accuracy of 96.63, 97.28 and 97.79% respectively. Hence, simplifying their skilfulness in detecting brain tumors to induce the manifest model of a productive and virtuous approach for enhanced prognosis. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Neuroimaging

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Hybridization of Sparrow Search Algorithm and Sine Cosine Algorithm for Epileptic Seizure Detection

Hybridization of Sparrow Search Algorithm and Sine Cosine Al...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Sumant Kumar Mohapatra Kaliprasanna Swain Puspanjali Mallik Sabita Mali Farida A. Ali Trident Academy of Technology Bhubaneswar Odisha Dept. of Computer Science Shailabala Women's Autonomous College Cuttack India Faculty of Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

An imbalance in electrical signal flow among neurons causes epilepsy, a complex brain disease that affects other parts of the body and results in seizures. Researchers and neurologists have put their efforts into devising powerful electroencephalogram devices to address the importance of health issues. In this context, machine learning algorithms are used to accurately detect the presence of a seizure signal. This experiment uses the hybridization of two meta-heuristic optimization algorithms: the sparrow search algorithm and the sine cosine algorithm. To conduct the research, this experiment uses the publicly available CHB-MIT EEG dataset, and for feature extraction, it uses the discrete wavelet transform (DWT). ANOVA We use feature selection to identify similar groups. This study uses three classifiers: support vector machines (SVM), Adaptive Neuro Fuzzy Inference System (ANFIS), and Extreme Learning ANFIS (ELANFIS). We got 99.8% accuracy, 98.35% sensitivity, 99.02% specificity, 98% positive predictive value, and 1 for area-under-curve for SSA-SCA-DWT-ELANFIS. This shows that it works better than SSA-SCA-DWT-SVM, SSA-SCA-DWT-ANFIS, and other methods that are already out there. In the future, we can integrate this study with the Internet of Things (IoT).

关键词： Support vector machines Machine learning algorithms Accuracy Transforms Prediction algorithms Feature extraction Electroencephalography Discrete wavelet transforms Classification algorithms Internet of Things

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Enhancing Security: Federated Learning against Man-In-The-Middle Threats with Gradient Boosting Machines and LSTM

Enhancing Security: Federated Learning against Man-In-The-Mi...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Suneeta Satpathy Pratik Kumar Swain Sachi Nandan Mohanty Santi Swarup Basa Centre for AI & ML SoA University Odisha India FET Sri Sri University Odisha India School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Dept. of Computer Science Maharaja Sriram Chandra Bhanjadeo University Baripada Odisha India

ISBN: (数字)9798350374285

ISBN: (纸本)9798350374292

Federated Learning (FL) can be preferred more in the cyber security community because it can enable training without sharing the actual training data when combined with different learning algorithms. Though these types of learning models maintain the privacy of the data by reducing the data centralization, it is vulnerable to cyber security threats like Man-In-The-Middle (MITM) attacks. The present paper makes an attempt to analyse the performance by integrating Gradient Boosting Machines (GBM) and Long Short-Term Memory (LSTM) with Principal Component Analysis (PCA) in a federated learning framework. Both the models are trained on a converted dataset using PCA that reduces the dimensionality of the data while maintaining its important values. The results indicated that the both models enhanced the confidentiality and integrity of the data against MITM attacks while FL-GBM performed better than FL-LSTM model in terms of accuracy and robustness in maintaining performance consistency across rounds of evaluations. This research further extends the current understanding of secure federated learning architectures and proposes a viable model for practical deployments in sensitive applications.

关键词： Training Federated learning Surveillance Training data Boosting Data models Security

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