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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Guru Prasad M S Praveen Gujjar J Arnav Kotiyal Prasanna Kumar H R Raghavendra M Devadas Afsar Jahan Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Dept. of Information Science & Engg. Visvesvaraya Technological University PESITM Shivamogga India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Computer Application Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and dept. of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models.

关键词： Analytical models Technological innovation Data analysis Navigation Data visualization Machine learning Feature extraction

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A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-Enabled Real-Time Human Detection System for Disaster Management

A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Govind Murari Upadhyay Mukesh Joshi Preeti Rathi Prashant Vats Meenakshi Narula Shashi Kant Gupta Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India School of Computing Graphic Era Hill University Uttarakhand India Dept. of CSE School of Engg. & Techn. K.R. Mangalam University Gurugram Haryana India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Jagannath International Management School New Delhi India Department of CSE ITM University Gwalior Madhya Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Disasters pose significant challenges to emergency response efforts, often hindering timely and effective interventions. In recent years, integrating Unmanned Aerial Vehicles (UAVs) has emerged as a promising approach to enhance disaster management capabilities. This paper presents a novel real-time framework leveraging UAV technology for disaster response. The framework encompasses UAV deployment strategies, real-time data acquisition through aerial imaging and remote sensing, robust data transmission using advanced communication infrastructure, automated AI and machine learning analysis, and dynamic resource coordination based on actionable insights. By integrating these elements, the proposed framework aims to improve situational awareness, optimize response efforts, and facilitate informed decision-making during disaster scenarios. This paper discusses the key components of the framework, highlighting its potential impact on enhancing disaster response capabilities in real-time scenarios.

关键词： Disasters Imaging Disaster management Machine learning Autonomous aerial vehicles Emergency services Real-time systems Sensors Vehicle dynamics Remote sensing

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Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection

Securing IoT-Edge Networks: Federated Deep Learning for Botn...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S. Nagasundaram R. Sindhuja B. Rajesh Kanna S. Rajalakshmi Shobana G Aviral Srivastava Department of Computer Application Vels Institute of Science Technology and advanced Studies Pallavaram Chennai Department of Medical Electronics Saveetha Engineering college Dept. of Electronics and Communication Engineering Alagappa Chettiar College of Engineering and Technology Karaikudi Department of Electronics and Communication Engineering RMK College of Engineering and Technology Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur India Department of CSE Amity University Jaipur Rajasthan

Governments throughout the world are encouraging the use of “smart city” technologies to improve urban residents' day-to-day experiences. In order to improve services like healthcare, electricity distribution, water purification, traffic management, etc., smart cities implement internet-connected technologies. The proliferation of connected gadgets has led to an increase in botnet assaults based on the IoT. The term IoT is used to describe a system of computers that are linked together that may be used for a wide variety of tasks, from environmental monitoring to on-demand power switching and beyond. Many Internets of Things gadgets are inherently disparate, update at irregular intervals, and hide in plain sight on a private or company network. The safety and confidentiality issues surrounding the Internet of Things that need to be addressed in both academic and practical settings. This research study proposes a federated-based solution to botnet attack detection utilizing on-device decentralized traffic data and a deep learning (DL) model. The proposed federated method addresses privacy concerns by preventing data from leaving the network's edge on the device. Instead, the edge layer is used to do the DL calculation, which has the extra benefit of being closer to the source of the data. Many tests are run on newly made public test data sets for deep learning models. Additionally, the sets of data are presented for examination and understanding. The recommended DL model achieved better results than the ML models. Finally, this research shows that the suggested model can detect anomalies with a precision of up to 98%.

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Full-atom protein pocket design via iterative refinement 23

Full-atom protein pocket design via iterative refinement

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zaixi Zhang Zepu Lu Zhongkai Hao Marinka Zitnik Qi Liu Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei Anhui China and Harvard University Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei Anhui China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua-Bosch Joint ML Center Tsinghua Harvard University

The design and optimization of functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context modeling of the ligand molecule, and the inability to generate side-chain atoms. Here, we present the Full-Atom Iterative Refinement (FAIR) method, designed to address these challenges by facilitating the co-design of protein pocket sequences, specifically residue types, and their corresponding 3D structures. FAIR operates in two steps, proceeding in a coarse-to-fine manner (transitioning from protein backbone to atoms, including side chains) for a full-atom generation. In each iteration, all residue types and structures are simultaneously updated, a process termed full-shot refinement. In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions. The subsequent stage delves deeper, modeling the side-chain atoms of the pockets and updating residue types to ensure sequence-structure congruence. Concurrently, the structure of the binding ligand is refined across iterations to accommodate its inherent flexibility. Comprehensive experiments show that FAIR surpasses existing methods in designing superior pocket sequences and structures, producing average improvement exceeding 10% in AAR and RMSD metrics.

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A novel machine learning hybrid model for heart disease risk assessment

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Amit Saxena Rajeev Kumar Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Heart disease claims millions of lives annually in more than a hundred countries. Heart disease has become one of the world’s leading causes of mortality, according to a World Health Organization (WHO) report. Among the conditions that can cause heart attacks include angina pectoris, congestive heart failure, cardiomyopathy, congenital heart disease, arrhythmias, myocarditis, heart attacks, and heart cancer. Heart disease presents a significant global health challenge, prompting extensive research into early detection and preventive measures. This study introduces a predictive model leveraging advanced machine learning (ML) techniques to assess heart disease risk. The model development process encompasses several phases, including data preprocessing, feature selection, and model training employing state-of-the-art algorithms like logistic regression (LR), decision tree (DT), random forests (RF), artificial neural network (ANN), naïve Bayes (NB), K-Nearest Neighbor (KNN), and support vector machine (SVM). Additionally, feature importance analysis and model evaluation techniques are utilized to refine interpretability and performance. The results exhibit the model’s efficacy in accurately predicting heart disease risk, offering valuable insights for early detection and personalized healthcare strategies. Overall, this research emphasizes the potential of data-driven methodologies in constructing predictive models for heart disease, presenting a promising avenue for enhancing patient outcomes and alleviating the burden on global healthcare systems.

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A transfer learning based model for brain tumor detection using magnetic resonance imaging

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Rajeev Kumar Amit Saxena Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Rapid and uncontrolled proliferation of cells is the cause of brain tumors. If treatment is not carried out early, death can occur. A major challenge in detecting a brain tumor is that it originates from variations in size, shape, and location. Since tumor cells are diverse, it might be difficult for radiologists to classify brain tumors, which are malignant diseases. The overall survival of patients, treatment planning, and risk factor identification depend on robust and accurate tumor classification and prediction. In the medical field, deep learning (DL) and machine learning (ML) are often employed to diagnose illnesses. Various tools have been developed that are used to diagnose different diseases such as heart disease, lung cancer, and many more. In this study, a DL-based transfer learning technique has been used to build a model that can easily identify whether a patient is suffering from a brain tumor or not. Brain tumors have been categorized into four types of categories based on the characteristics of the tumor. Patients who are suffering from the first and second categories of tumors can be saved by treatments that include surgery, radiation therapy, and chemotherapy. The third and fourth categories of tumors are more dangerous because these categories of tumors spread very fast, so if it is diagnosed early stage then the patient can be saved or increase the patient’s life span. Glioma, Meningioma, and Pituitary are a dangerous form of tumors, and the survival rate of patients who are suffering from these is very low. The developed system can predict that a patient is suffering from a brain tumor or not using MRI, and also predict the type of tumor such as Glioma, Meningioma, and Pituitary.

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Adaptive Reinforcement Learning Framework for Optimized Energy Management in Hybrid Electric Vehicles Using Synthetic Driving Environments and Open-Source Simulation Models

Adaptive Reinforcement Learning Framework for Optimized Ener...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Phaneendra babu Bobba S P Sreeja Alok Jain Bhasker Boddu Fatimah Dhari Jabbar Ashima Tyagi Kailash Sharma Department of EEE GRIET Bachupally Hyderabad Telangana India Master of Computer Application New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Information Technology Mlr Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Conventional heuristic approaches are sometimes used to model energy management strategies for HEVs, especially for Rule-Based car models, but RL is the solution that presents a shift in strategy. In this research, RL is used to solve the problem of M/M objectives in the field of HEVs by optimizing fuel efficiency and emissions. Based on the realistic synthetic driving cycles that mimic various realistic driving scenarios, and the open-source toolbox OpenPowertrainLib, the research develops a reproducible and computationally saving approach. I have suggested that the powertrain energy distribution should be trained through reinforcement learning (RL), PPO, a policy-gradient-based RL algorithm should be used to propose an agent capable for adaptive control. When it comes to implement the maneuverer the dynamics of the hybrid vehicle as well as the constraints of the powertrain are modelled within the OpenPowertrainLib’, which provides credible and reconstructive looks at the maneuverer under simulation. The simulations show a remarkable increase in energy efficiency and emissions decrease in comparison with conventional heuristic methods. This work helps towards sustainable transportation by demonstrating the promise of RL-driven intelligent energy management systems of HEVs.

关键词： Adaptation models Vehicle-to-grid Scalability Transportation Reinforcement learning Mechanical power transmission Hybrid electric vehicles State of charge Energy management systems Vehicle dynamics

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Pinching Antennas: Principles, applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Full-Atom Protein Pocket Design via Iterative Refinement

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Lu, Zepu Hao, Zhongkai Zitnik, Marinka Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua-Bosch Joint ML Center Tsinghua China Harvard University United States

The design of de novo functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context modeling of the ligand molecule, and the inability to generate side-chain atoms. Here, we present the Full-Atom Iterative Refinement (FAIR) method, designed to address these challenges by facilitating the co-design of protein pocket sequences, specifically residue types, and their corresponding 3D structures. FAIR operates in two steps, proceeding in a coarse-to-fine manner (transitioning from protein backbone to atoms, including side chains) for a full-atom generation. In each iteration, all residue types and structures are simultaneously updated, a process termed full-shot refinement. In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions. The subsequent stage delves deeper, modeling the side-chain atoms of the pockets and updating residue types to ensure sequence-structure congruence. Concurrently, the structure of the binding ligand is refined across iterations to accommodate its inherent flexibility. Comprehensive experiments show that FAIR surpasses existing methods in designing superior pocket sequences and structures, producing average improvement exceeding 10% in AAR and RMSD metrics. FAIR is available at https://***/zaixizhang/FAIR. © 2023, CC BY.

关键词： Atoms

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Classification of Symptoms for Malaria using Soft Computing Method

Classification of Symptoms for Malaria using Soft Computing ...

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

作者： Subrata Kumar Nayak Sateesh Kumar Pradhan Sujogya Mishra Narayan Patra Sipali Pradhan Department of Computer Science GVHSS Sishu Ananda Mahavidyalaya Balipatna Odisha India Department of Computer Science and Application Utkal University Bhubaneswar India Department of Mathematics Odisha University of Technology and Research Bhubaneswar Odisha Department of CSIT Institute of Technical Education and Research SoA Deemed to be University Bhubaneswar India Dept. of Computer Science RBVRR Women's College Hyderabad India

Detection of Malaria Fever is a challenge for the doctors as symptoms of Malaria fever vary concerning places; for example, the types of malaria fever found in our country are not the same in South Africa, England, and other western countries. Malaria is not fatal if detected early, but due to its complex nature, it is difficult to side-line this fever with other fevers like Typhoid, Dengue, and other class of fevers. Our objective is to find the minimal attributes responsible for malaria by using Rough Set Theory (RST), a Soft Computing technique that is a useful tool to handle imprecise and vague datasets. Our objective is to find a set of attributes (symptoms) of malaria from the vague medical data set, which are helpful for the doctors for concrete diagnosis of malaria. We validate our claim by using statistical techniques.

关键词： Rough sets Medical services Machine learning computer security Viruses (medical) Medical diagnostic imaging Diseases

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