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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Singh, Divanshu Khandelwal, Avish Kumar, Aniket Bawaskar, Harsh Ugale, Prajakta Mit Academy of Engineering Information Technology Pune Alandi India Mit Academy of Engineering Computer Science Pune Alandi India Mit Academy of Engineering School of Computer Engineering Pune Alandi India

ISBN: (纸本)9798350383287

Efficient timetable creation is paramount for organizational productivity, yet often a labor-intensive task. In this paper, we present 'Timelog', a groundbreaking innovation that harnesses the power of Genetic Algorithms to generate bespoke, conflict-free timetables tailored to user-defined requirements. Timelog empowers users with the ability to customize key parameters such as density, duration, and compatibility, ensuring schedules that perfectly align with their unique *** evaluate the effectiveness of Timelog, we introduce a set of performance metrics, including schedule efficiency and resource utilization, providing a comprehensive assessment of its capabilities. Remarkably, Timelog consistently achieves an astounding accuracy rate of 99% when used in educational institutions compliant with UGC (University Grants Commission) norms, making it an ideal solution for *** seamlessly integrating genetic algorithms, Timelog represents a transformative leap in the scheduling domain, offering organizations a streamlined, efficient, and highly customizable scheduling solution. This innovation promises to revolutionize the scheduling process, significantly enhancing organizational efficiency and productivity. © 2024 IEEE.

关键词： Efficiency

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An Adaptive Attitude Controller for A Quadrotor Transporting the Payload With Unknown Mass 38

An Adaptive Attitude Controller for A Quadrotor Transporting...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Qiu, Shihao Chen, Longlong Li, Shuang Lv, Zongyang Tang, Huayi Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China Henan University of Technology College of Information Science and Engineering Zhengzhou China Dalian University of Technology School of Mechanical Engineering Dalian China

ISBN: (纸本)9798350303636

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor's attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller. © 2023 IEEE.

关键词： Adaptive Control Quadrotor Unmanned Aerial Vehicle Unknown Mass Payload

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Revolutionizing Malware Detection: Feature-Based Approach for Targeting Diverse Malware Categories 56

Revolutionizing Malware Detection: Feature-Based Approach fo...

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Jain, Sanyam Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350315875

In today's digital landscape, the task of identifying various types of malicious files has become progressively challenging. Modern malware exhibits increasing sophistication, often evading conventional anti-malware solutions. The scarcity of data on distinct and novel malware strains further complicates effective detection. In response, this research presents an innovative approach to malware detection, specifically targeting multiple distinct categories of malicious software. In the initial stage, Principal Component Analysis (PCA) is performed and achieved a remarkable accuracy rate of 95.39%. Our methodology revolves around leveraging features commonly accessible from user-uploaded files, aligning with the contextual behavior of typical users seeking to identify malignancy. This underscores the efficacy of the unique feature-based detection strategy and its potential to enhance contemporary malware identification methodologies. The outcomes achieved attest to the significance of addressing emerging malware threats through inventive analytical paradigms. © 2023 IEEE.

关键词： Malware

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Design of artificial biomimetic channels with Na^(+)permeation rate and selectivity potentially outperforming the natural sodium channel

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Nano Research 2024年第9期17卷 8638-8646页

作者： Zhi Zhu Yan Zhao Chao Chang Shaojian Yan Tingyu Sun Shiyu Gu Yangmei Li Dengsong Zhang Chunlei Wang Xiao Cheng Zeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Innovation Laboratory of Terahertz Biophysics National Innovation Institute of Defense TechnologyBeijing 100071China School of Physics Peking UniversityBeijing 100871China International Joint Laboratory of Catalytic Chemistry College of SciencesShanghai UniversityShanghai 200444China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China

Artificial ion channels that enable high-efficiency ion transport have important implications in nanofluidics and biomedical applications such as drug ***,we show a simulation-based chemical design of a biomimetic sodium channel that possesses permeation rate and selectivity potentially higher than those of the state-of-the-art natural vertebrate voltage-gated sodium ***,our theoretical findings have undergone empirical testing,aligning well with the Arrhenius law as derived from a diverse range of experimental *** high-efficiency ion transport is achieved by anchoring the carboxylate functional groups within the channel filter.A key chemical guiding principle underlying the ion channel design is that the free-energy barrier for the Na^(+)passage across the channel should be comparable to typical thermal energy at room *** the implementation of the chemical design,we found that the relatively low free-energy barrier can be attributed to the compensation effect of the carboxylate groups to the partially lost oxygen shell of the ion within the ion channel,as well as to the consonant vibration of the ions inside and outside the *** mechanistic understanding brings new insight,at the molecular level,into the high-efficiency ion transport across the designed membrane *** proof of principle achieved from the simulations will stimulate future experimental confirmation and potential applications of the high-performance artificial channels in nanofluidics and in bioinspired iontronics.

关键词： sodium channels anchoring carboxylate functional groups molecular dynamics simulations low free-energy barrier for ion passage thermal kinetic energy

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Stroke Disease Prediction Using Adaboost Ensemble Learning Technique 4th

Stroke Disease Prediction Using Adaboost Ensemble Learning T...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ganachari, Sreenidhi Battula, Srinivasa Rao School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh Amaravati India

ISBN: (纸本)9789819920990

This paper presents an approach to predict stroke utilizing a better model with Adaboost algorithm. Today the utilization of opportunely trained machine learning algorithms can be significantly utilized in fields such as medicine and data science to identify and provide solutions to quandaries that do not have a solution or if the current solutions are not efficacious. A stroke is a condition where blood arteries in the brain burst, harming the brain. The rigor of a stroke can be abated by early apperception of numerous stroke warning symptoms. Previous works have utilized sundry machine learning algorithms to determine stroke. Despite many research works in the literature indicating successful outcomes in preclinical stroke models, there has been no significant contribution to the medical field. Since one weak classifier cannot reliably predict an object’s categorization by itself, adaptive boosting, also known as AdaBoost, combines numerous weak classifiers in progressive learning to create a strong predictive classifier. It uses ensemble learning techniques to predict the accuracy which makes it better than the other algorithms discussed in the paper. The dataset used emanates from Kaggle with sundry parameters that have been taken into consideration. The machine learning algorithms considered are random forest algorithm, decision tree algorithm, SVM, logistic regression algorithm, KNN, Naïve Bayes, and AdaBoost algorithm to train different models and compare the results for the best model. Amongst all the algorithms AdaBoost algorithm gives us the best precision of 98.8%. Thus, the main aim of this paper is to propose a classifier—AdaBoost classifier that can be utilized by medical practitioners to detect the ailment in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Yuxin Zhang, Lizhong Zhu, Tao Luo, Wenjian School of Computer Science and Information Engineering Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230027 China School of Computer Science and Information Engineering University of South China Hunan Hengyang421200 China School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9798350337020

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to 'track the moving optima', rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://***/DMiC-Lab-HFUT/SMDE-Transfer. © 2023 IEEE.

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Blockchain-Based Revocable Key-Aggregate Searchable Encryption for Group Data Sharing in Cloud-Assisted Industrial IoT

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IEEE Internet of Things Journal 2025年第11期12卷 16899-16911页

作者： Zhang, Kai Hu, Xinyi Zhao, Jian Wei, Lifei Ning, Jianting Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Shanghai University of Electric Power College of Electrical Engineering Shanghai China Shanghai Maritime University College of Information Engineering Shanghai China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products-making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in Industrial IoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: (i) the user revocation needs interaction process between data owner and cloud;(ii) the non-revoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves non-interactive user revocation and trusted revoked user management. Technically, the result is non-trivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with re-designing trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with 13.4×~15× time efficiency accelerating for data retrieval and runs more than 16× faster for user revocation. © 2025 IEEE.

关键词： Industrial Internet of Things Cloud computing Aggregates Encryption Blockchains Permission Cryptography Data retrieval Production facilities Costs

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Study on forest carbon sequestration based on clustering algorithm and fitting analysis

Study on forest carbon sequestration based on clustering alg...

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2023 International Conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Chen, Zhang Peng, Yuxu Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798350313314

With the intensification of global warming, carbon sequestration has received more and more attention, in which the research of forest carbon sequestration has been widely carried out, and forest managers all over the world want to maximize the efficiency of forest management. This paper puts forward two best forest management schemes for national managers from the point of view of carbon sequestration and sustainable development. Twenty variables are comprehensively considered and determined to solve the model, and the Word2vec model is used to reflect the differences between different climatic and geographical environments. Then the cluster analysis method is used to divide the forest carbon sequestration capacity into different regions, which provides a basis for discussing the influencing factors of forest carbon sequestration. We combine correlation analysis and fitting analysis to determine the main influencing factors of carbon absorption and describe the basic evolution trend of the time series of different carbon absorption conditions in different regions from 1990 to 2020. © 2023 IEEE.

关键词： Forestry

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Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification

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IEEE/CAA Journal of Automatica Sinica 2021年第10期8卷 1686-1696页

作者： Chenghao Liu Fei Zhu Quan Liu Yuchen Fu School of Computer Science and Technology Soochow UniversitySuzhou 215006China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China

In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is *** solve the problem,we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision(HADS)which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal *** uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement *** to the fact that not all sub-goal points are reachable,a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the *** involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals,while those that are not will be removed based on prior *** experiments verified the effect of the algorithm.

关键词： Hierarchical control hierarchical reinforcement learning option sparse reward sub-goal

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