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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Begum, S. Hameetha Balasubramanyam, C. Thirukrishna, J.T. Manoj, G. Department of Computing Muscat College Muscat Oman Department of Computer Science and Engineering Guru Nanak Institute of Technology Telangana Hyderabad India Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore Bangalore India

ISBN: (纸本)9789811974540

Feature selection, often known as FS, is often considered to be a component of the larger issue of global optimization. FS is being used to optimize and improve the quality of huge datasets. This is accomplished by selecting prominent features and minimizing duplicate data in order to provide satisfactory classification performance. Feature selection is an approach that is taken with the goal of reducing the complexity of the classification process while simultaneously improving its level of precision. This approach is significant in a variety of fields, including data mining, data processing, and pattern classification. The primary objective here is to devise a more accurate subset of all of the data that takes into account the relevant sample. In order to solve this issue, the BWO-V method, which is short for the binary form of the whale optimization (WO) technique, has been introduced. In order to transform the findings into binary, the BWO-V makes use of a function called the hyperbolic tan function. Validation of the BWO-V algorithm’s performance is carried out on five datasets taken from the repository at UCI. The quantitative and qualitative results both show that using BWO-V helps limit the number of features picked while also optimizing the classification accuracy with significantly less effort. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

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computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks 3

Optimizing Drug Dosage and Medication Management for Geriatr...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 IEEE.

关键词： Optimization

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Proposed Framework for Modifications to Artificial Intelligence/Machine Learning (Ai/Ml)-Based Software as A Medical Device (SAMD) 7

Proposed Framework for Modifications to Artificial Intellige...

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

作者： Shrivastava, Anurag Jain, Upma Al-Farouni, Mohammed Kumar, Yogendra Anandhi, R.J. Bhavana, Munugapati Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The rapid adoption of software as a medical device (SAMD) driven by artificial intelligence and machine learning has brought about a fundamental shift in the medical industry. This shift has the potential to greatly improve clinical outcomes and the quality of care provided to patients. This shift has been responsible for a number of key achievements made in recent times. When seen in this context, the proposed legal framework for revisions to the AI/ML-SAMD appears as an essential response to the malleability of these technologies. To successfully navigate the tough process of modifying AI/ML-SAMD with the assistance of this framework. It does this by taking into consideration the need for rapid regulatory scrutiny and making an attempt to combine the promotion of innovation with the simultaneous preservation of patient safety. In other words, it ensures that patient safety is protected while also encouraging innovation. This abstract provides a summary of the fundamental components of the framework, as well as a discussion of the significance of those components with regard to fostering the development of moral AI/ML-SAMD within the context of the healthcare ecosystem. The healthcare sector is undergoing a change as a direct result of artificial intelligence and machine learning, which are improving patient outcomes, diagnostic accuracy, and treatment options. The research emphasizes the significance of specific AI and ML applications as well as the sector's embrace of this paradigm-shifting technology. In addition, the regulatory framework that has been presented is an important step towards guaranteeing the safe use of AI and ML in the medical field. © 2024 IEEE.

关键词： Artificial Intelligence Compliance Guidelines Healthcare Innovation Machine Learning Modifications Patient Safety Regulatory Framework Software as a Medical Device

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The NEMO co-pilot

The NEMO co-pilot

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Costantini, Stefania Dell’Acqua, Pierangelo De Gasperis, Giovanni Gullo, Francesco Rafanelli, Andrea Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Gruppo Nazionale per il Calcolo Scientifico - INdAM Roma Italy Department of Science and Technology Linköping University Linköping Sweden Department of Computer Science University of Pisa Italy

In this work, we describe an agent to be employed in Human-AI Teaming in various, even critical, domains, based upon affective computing, empathy, and Theory of Mind, and a description of the user profile and of the operational, professional, and ethical requirements of the domain in which the agent operates. The architecture of the proposed agent encompasses a Knowledge Graph, a Neural component and a Behaviour Tree. We briefly discuss a case study. © 2024 Copyright for this paper by its authors.

关键词： Chatbots

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement 5

A Recurrent Neural Network for Image Deblocking Detection an...

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

作者： Venkatesan, R. Pandiaraj, A. Selvakumar, M. Bannari Amman Institute of Technology-Sathyamangalam Department of Information Technology India School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur Department of Computer Science and Engineering India

ISBN: (纸本)9781665474672

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation. © 2023 IEEE.

关键词： Image segmentation

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Quantum Key Distribution and Blockchain Based Secure Authentication in Medical Cyber-Physical Systems 8th

Quantum Key Distribution and Blockchain Based Secure Authent...

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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Gawali, Piyush P. Mahalle, Parikshit N. Shinde, Gitanjali R. Sable, Nilesh P. Takale, Dattatray G. Barot, Janki Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Information Technology Vishwakarma Institute of Information Technology SPPU Pune Pune India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819965670

Medical cyber-physical systems (MCPSs) enable flexible patient–medical system interaction as the foundation of application in the field of smart health care, realizing all-encompassing three-dimensional medical service. The security barrier must be built using a secure and trustworthy device identity authentication mechanism in order to meet the controlled and believable requirements of MCPS. This work, therefore, develops an efficient authentication and encryption system (EAES) to improve the security and authentication of MCPSs. This research suggests a secure authentication method to safeguard sensitive healthcare data. The suggested modified "elliptic-curve Diffie–Hellman" (ECDH) encryption is first applied to the medical data that is acquired from the patients and stored in the cloud in encrypted form. This is done in order to guarantee secure access to the stored data for use in medical data analysis. The key in the ECFH is controlled via a quantum key distribution system. Additionally, blockchain is considered for the mutual authentication of users in the MCPSs. The proposed methodology is implemented and evaluated by using performance measures. Additionally, it is compared with the conventional techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations

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Human-Centric Intelligent Systems 2024年第4期4卷 497-510页

作者： Prity, Farida Siddiqi Hasan, MD. Mehadi Saif, Shakhawat Hossain Hossain, Md. Maruf Bhuiyan, Sazzad Hossain Islam, Md. Ariful Lavlu, Md Tousif Hasan Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka Bangladesh Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

Agriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural produ

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An Efficient Selective Features Approach to Detect Hypothyroid Using Machine Learning 5th

An Efficient Selective Features Approach to Detect Hypothyro...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Chandra, N. Subhash Dhanikonda, Srinivasa Rao Jagli, Dhanamma Siddhartha, Nalla Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Information Technology BVRIT HYDERABAD College of Engineering for Women Hyderabad India V.E.S.Institute of Technology University of Mumbai Mumbai India Indian Institute of Technology Tirupati Tirupati India

ISBN: (纸本)9789819997039

The thyroid gland is a hormone-producing gland located in the upper part of the neck. It helps to regulate the metabolism of the body. Its malfunction might lead to either inadequate or excessive thyroid hormone production. Thyroid issues are classified as hyperthyroidism or hypothyroidism. Predicting thyroid illness is now more crucial than ever. Machine learning algorithms and data mining techniques are critical for dealing with today’s massive amounts of data and information, particularly in the health-care system. A specific dataset is created that contains patient-related medical data. Based on the attributes considered, it is possible to categorize thyroid disease into three categories: primary hypothyroid, compensated hypothyroid, and routine. To identify earlier, various conventional machine learning methods are utilized, including Random Forest, Decision Tree, and Artificial Neural Network (ANN). These algorithms carry out the classification and determine the status but fall short of the desired results, particularly in the medical sector. In order to accomplish the goal, this research developed an effective classification strategy to improve the performance of hypothyroid prediction and it is divided into two stages: data preprocessing and classification methods. During the preparation phase, the dataset was preprocessed using two methods: PCA and an Extra Tree classifier, and the data was enriched with the Synthetic Minority Oversampling Technique (SMOTE). The proposed approach's outcomes were compared to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Innovative Deep Learning Solutions for Image Forgery Detection 1

Innovative Deep Learning Solutions for Image Forgery Detecti...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Mutar, Mohammed Hasan Al-Fatlawy, Ramy Rasool, Hussein Ali Ghafour, Rizgar Rahman Abbas, Fatima Hashim Hashim, Dulfikar Jawad Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq

ISBN: (纸本)9798350367003

Digital image forgery detection is crucial in addressing the rapid spread of fake information through manipulated images, especially on social media platforms. Traditional techniques often focus on specific types of forgery, limiting their effectiveness in real-world scenarios. Traditional methods heavily depend on manual feature engineering, which often results in overlooked manipulations, decreased accuracy, adaptability, and scalability issues when handling large datasets or high-resolution images. Deep learning has emerged as a powerful tool for addressing the challenges associated with image forgery detection. The proposed work introduces an innovative method for detecting image forgeries using deep learning techniques, employing convolutional neural networks (CNNs) and specifically evaluating the performance of the EfficientNetb7 model. This method leverages transfer learning to detect copy-move image forgery. It involves generating featured images by calculating the difference between the input image and compressed versions, which are then fed into pre-trained CNN model. The model undergo fine-tuning to adapt to forgery detection. Additionally, the output of the forgery detection process includes both text and audio. This combination enhances the accessibility and interpretability of the detection results, making them more understandable for users with different sensory preferences or impairments. This added feature ensures that the detection outcomes are easily comprehensible and usable across a broader range of users and applications. © 2024 IEEE.

关键词： CNN deep learning digital image EfficientNetb7 forged detection Rapid spread

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