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Innovation in technology (INOCON), IEEE International Conference for

作者： Zahid Ahmed N. Beemkumar Debanjan Ghosh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications.

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch

Stage Wise Classification and prediction of Alzheimer diseas...

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Intelligent Technologies (CONIT), International Conference on

作者： A Prabagaran Vinay Dwivedi Neelam Jyoti Rani J Angelin Blessy P Padmanabhan Gupshup Technology India Pvt Ltd Bengaluru India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Dept of Computer Application School of Computing Science and Engineering Galgotias Univeristy India Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Alzheimer’s, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals’ independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer’s disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer’s disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD.

关键词： Surveys Accuracy Magnetic resonance imaging Predictive models Convolutional neural networks Alzheimer's disease Kernel

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A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

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computer Systems science & Engineering 2023年第8期46卷 1999-2020页

作者： Qianlei Wang Zaijun Wang Renhe Xiong Xingbin Liao Xiaojun Tan Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu610041SichuanChina School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of ChinaGuanghan618307SichuanChina School of Air Traffic Management Civil Aviation Flight University of ChinaGuanghan618307SichuanChina Neurology Department Deyang Second People’s HospitalGuanghan618307SichuanChina

How to accurately recognize the mental state of pilots is a focus in civil aviation *** mental state of pilots is closely related to their cognitive ability in *** the cognitive ability meets the standard is related to flight ***,the pilot’s working state is unique,which increases the difficulty of analyzing the pilot’s mental *** this work,we proposed a Convolutional Neural Network(CNN)that merges attention to classify the mental state of pilots through electroencephalography(EEG).Considering the individual differences in EEG,semi-supervised learning based on improvedK-Means is used in themodel training to improve the generalization ability of the *** collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this *** method in this paper achieved an accuracy of 86.29%,which is better than 4D-aNN and HCNN *** emotion will increase the probability of fatigue appearing,and emotion recognition is also meaningful during the *** we conducted experiments on the public dataset SEED,and our method achieved an accuracy of 93.68%.In addition,we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.

关键词： Pilot mental state EEG attention CNN semi-supervised learning

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Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

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Space(science & technology) 2023年第1期3卷 389-401页

作者： Bin Jiang Yingchun Yan Jingjing Zhao Xiang Xiao Li You Di Zhang Jizhao Lei Kezhi Wang Wenjin Wang Xiqi Gao National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China Department of Electronic Information Engineering Beihang UniversityBeijing 100191China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China China Aerospace Science and Technology Corporation Beijing 100094China Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUK.

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance *** satellite communication systems,channel acquisition and energy consumption are two critical *** this end,we investigate the tradeoff between the total energy efficiency(TEE)and minimum EE(MEE)for robust multigroup multicast satellite communication systems in this ***,under the total power constraint,we investigate the robust beamforming aimed at balancing the TEE-MEE,so as to achieve the balance between the fairness and total performance on the system *** this optimization problem,we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user ***,the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex *** addition,depending on the solution rank value,Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible ***,simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE,thus realizing a tradeoff between fairness and system EE *** is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

关键词： convex eigenvalue sequentially

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor Systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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P4AI application Based on Expert System to Evaluate Appropriate Profession

P4AI Application Based on Expert System to Evaluate Appropri...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Lukas Tanto Kurniawan Adrian Sasmoko Sonya Rapinta Manalu Yasinta Indrianti Mobile Application and Technology School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty of Hunamities Bina Nusantara University Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia

P4AI is a platform that supports quality improvement in the field of education which functions to provide special references for students based on industry 4.0. This platform is a form of transforming choices based on big data and local wisdom in the 4.0 era. This study aims to build a Profile of Measurement of Educational and Professional Interest in Era 4.0 in the Indonesian context through an Artificial Intelligence based mobile application abbreviated as P4AI. The Artificial Intelligence used in the development of this mobile application is an expert system where the system uses instruments obtained through gathering information from various experts so that through these instruments, the system can determine the type of user intelligence that is the most powerful based on the answers from user test work.

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

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Procedia computer science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework

Oddity Based Botnet Recognition Utilizing AI Model in the In...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Rupa Rani Sudeshna Chakraborty Pooja Singh Neha Varshney Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Banasthali Vidyapith Rajasthan School of Computer Application and Technology Galgotias University UP India Department of Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with <0.01% actual regrettable.

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