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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sharma, Suvarna Gupta, Rashmi Kumar, Jeetendra Dangi, Dharmendra Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Atal Bihari Vajpayee University Department of Computer Science and Application Chhattisgarh Bilaspur495009 India Iit Department of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798350387490

This article gives a thorough investigation of the data processing method used to determine mental effort levels in electroencephalography (EEG). The Egg data was analyzed using a bidirectional Long Short-Term Memory (LSTM) neural network. With the help of this neural network, precise classification was made possible by its ability to capture the intricate temporal correlations seen in the brainwave patterns. By employing a meticulous to-fold cross-validation process, the model exhibited a noteworthy accuracy rate of 80.02% in accurately differentiating between various degrees of mental effort. The use of EEG data to access mental workload is very pertinent to a number of industries, including human-computer interface, aviation, and healthcare. Bidirectional LSTM achieves an 80.02% accuracy rate, demonstrating the strategy's robustness in precisely and consistently recognizing mental strain situations. In addition to making a significant contribution to the quickly developing field of brain signal processing, the current study offers important new understandings into the real-world applications of electroencephalography (EEG)-based mental workload detection systems. There is a great deal of potential to develop human capacities in a variety of sectors and advance general welfare through the investigation of further advancements and useful applications in real-life situations. © 2024 IEEE.

关键词： Data handling

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An Efficient Antenna Optimization Method Based on a Parameter Dimensionality Reduction Approach

An Efficient Antenna Optimization Method Based on a Paramete...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Zhao, Chenyang Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

As antenna structure grow in complexity, the problem of time-consuming in electromagnetic (EM) simulation have become more common. This problem undoubtedly brings a series of the challenges in raising the effectiveness of antenna optimization. In order to overcome the existing difficulties, an antenna optimization method based on RF-GWO method is proposed. This approach consists of three major components. Firstly, an artificial neural network (ANN) is used to build the dataset. Compared with the traditional sampling method, the data filtered by the artificial neural network (ANN) are closer to the optimization objective. Consequently, it can better responsive to the relationship between the antenna parameters and the optimization objective. Then, the Random Forest method (RF) is utilized to analyze the impact of the ten selected parameters based on the electromagnetic (EM) simulation results on the antenna performance. Random Forest Algorithm (RF) has strong interpretability, allowing for the assessment of the contribution of different parameters to the optimization objective, which can effectively reduce the number of design variables. Finally, the antenna optimization is performed utilizing the Grey Wolf Optimization (GWO) algorithm. Combined with the sensitivity analysis results, the Gray Wolf Algorithm (GWO) can effectively search for improved results in a faster manner. In order to evaluate the algorithm's performance, the proposed method is employed to optimize a multiband miniaturized microstrip antenna. The result demonstrates that substantial improvement in impedance bandwidth is achieved by the proposed method, with an approximate 29% increase compared to conventional manual optimization methods. This outcome has proved potential and effectiveness of the proposed method in practical implications of antenna optimization. © 2024 IEEE.

关键词： Sensitivity analysis

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Designing A Secure Large Scale E -Voting System Leveraging Sharding Blockchain with Interoperability Protocol and Consensus Mechanism

Designing A Secure Large Scale E -Voting System Leveraging S...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Alka Maurya Rajendra Kumar Dwivedi Dept. of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

The advent of electronic voting (e-voting) systems has promised greater accessibility, efficiency, and transparency in the process of electoral. However, by adopting e-voting systems, they bring forth significant challenges, primarily related to security and scalability. Old e-voting systems encounter threats such as vote manipulation and denial-of-service attacks, making it imperative to design robust and secure solutions. This research focuses on addressing these challenges through a collaborative approach utilizing sharding technology with interoperability enabling it to handle a vast number of votes simultaneously without compromising performance or security. Recognizing the importance of seamless data exchange, our e-voting system places a strong emphasis on interoperability. The consensus mechanism providing rapid transaction finality, enhanced security, and resilience against malicious actors. It ensures the integrity in the process of voting and safeguards against alterations. We present a pioneering approach to reduce all kind of challenges in existing model by giving a secure e-voting that harnesses the power of sharding blockchain, interoperability, and a novel consensus mechanism. The analysis of blockchain structure that provide blockchain as a service initiates the article. Then, based on blockchain technology, we suggest a unique electronic voting method. That resolves all the issues we found. This reduces the cost of holding a national poll and enhances security.

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A Multi-objective Antenna Optimization Method Based on Preferred Parameter Spaces

A Multi-objective Antenna Optimization Method Based on Prefe...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Lin, Siyu Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

Machine learning has been successfully used in the field of multi-objective antenna design, enabling the enhancement of antenna performance and optimizing efficiency. However, the prediction within an exceptionally vast parameter space imposes significant demands on machine learning algorithms. Moreover, minor adjustments in dimensions can bring about significant alterations in antenna performance, which renders simultaneous optimization for all objectives exceedingly challenging. To solve the multi-objective antenna optimization problem, a multi-objective antenna optimization method based on a preferred parameter space is proposed. Specifically, this method utilizes the KNN algorithm to preliminarily optimize the antenna and acquire a high-quality dataset. Subsequently, the sensitivity of ten selected parameters based on the electromagnetic (EM) simulation results is calculated by the XGBoost algorithm. The value of the parameter sensitivity is acquired by averaging ten calculations. The top four ranked parameters are designated as variables while the rest are fixed as constants. This approach reduces the number of parameters for subsequent optimization, thus greatly improving the subsequent prediction accuracy while ensuring the antenna performance. Finally, the variables derived from XGBoost are input into the fruit-fly optimization algorithm (FOA) to find the near-optimal solution. Hence, the task of parameter optimization under the multi-objectives of the impedance bandwidth and circular polarization characteristics can be effectively accomplished. To further validate its performance, the proposed algorithm is applied to optimize the S-band miniaturized broadband circularly polarized antenna. Compared to the results obtained by electromagnetic simulation only, the optimized results exhibit a 17.95% improvement of impedance bandwidth and a 42.86% enhancement of axial ratio bandwidth. Numerical outcomes validate that the proposed method achieves multi-objective o

关键词： Circular polarization

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Multi-filter Wrapper Enhanced Machine Learning Model for Cancer Diagnosis 1st

Multi-filter Wrapper Enhanced Machine Learning Model for Can...

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Sahu, Bibhuprasad Dash, Sujata Department of Computer Science and Information Technology Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India Department of Computer Application Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India

ISBN: (纸本)9783031350771

The classification accuracy of the high dimensional dataset degrades due to the redundant and irrelevant features. Feature selection (FS) is used to reduce the dimensionality of the dataset by removing the noisy features. Each filter has its statistical approach. So the feature selected by a single filter may ignore the important one. We have presented a multifilter (MF) wrapper hybrid model. The advantage of using the MF method is to select the important feature by one filter which one may ignore by the other. Here, we have used an aggregator approach to combine the most efficacious features among the four individual filter methods (information gain (IG), chi-square (Chi-sq), minimum redundancy maximum relevance (mRMR), and relief). The accuracy assessment is carried out in a multiple filter wrapper (Jaya-SVM, GA-SVM, PSO-SVM, and FA-SVM). The evaluation and prediction of the subset of features are carried out with four classifiers with excellent performance, such as the support vector machine (SVM), Naive Bayes (NB), decision tree (DT), and linear discriminant analysis (LDA) were tested respectively. Four (breast cancer, leukemia, ovarian, and central nervous system (CNS)) cancer datasets are used to implement the model. The performance of the MF wrapper is excellent in comparison to a single filter. According to the findings of this study, the proposed hybrid approach is a more efficient and trustworthy feature selection technique for selecting highly discriminative features. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Support vector machines

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Intelligent Food Identification System Using Improved YOLOv8 13

Intelligent Food Identification System Using Improved YOLOv8

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13th International Symposium on computer Science and Educational technology, ISCSET 2024

作者： Nitiavintokana, Francia Rouf, Md Abdur Yu, Xiaoyu Wu, Haibin Wang, Aili Iwahori, Yuji Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin China College of Electron and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Chubu University Department of Computer Science Aichi Japan

ISBN: (纸本)9798350390803

The field of computer vision has witnessed significant advances in object detection with real-world applications ranging from autonomous driving to surveillance systems. Due to its importance in a number of areas, such as dietary evaluation, food industry automation, and health monitoring, food identification has attracted a lot of interest in this context. The suggested approach makes use of the capabilities of two cutting-edge object recognition models, YOLOv8. Because of its speed and accuracy, YOLOv8 is a great option for real-Time food detection in a variety of settings. Our most recent YOLOv8 model is called BiTrains-YOLOv8. Supervision techniques are applied to improve the combined model's performance even more. This increases overall detection accuracy and helps the algorithm to more effectively generalize to novel food items. Based on the detection results, 96.7% of an average of detection accuracy, 96.4% for precision, 95.7% for recall, and 96.04 % for F1-Score were obtained. The results show that the combination of YOLOv8 and BiTrains-YOLOv8 improved model techniques, significantly improved the performance of food detection, outperforming the previous methods in terms of accuracy, speed and robustness. Furthermore, the paper analyzes potential real-world applications of the system, such as calorie counting, dietary assessment and smart kitchen automation. Implications of advanced intelligent food detection in healthcare, the culinary industry and personalized nutrition are also discussed. In conclusion, the results provide valuable insights for pushing the boundaries of food recognition technology, with promising applications in various fields that promote healthy lifestyles and improve efficiency in food-related processes. © 2024 IEEE.

关键词： Food industry

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Security Enhancement in Tea Supply Chain using Blockchain with Hyperledger

Security Enhancement in Tea Supply Chain using Blockchain wi...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Amritesh Kumar Gupta Rajendra Kumar Dwivedi Dept. of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

With the advancement of technology and emergence of blockchain, it has become quite a popular tool which is often integrated with systems and processes that require decentralization, traceability, consistency etc. One such area is of supply chain management. Often the raw materials used to make a final product do not come from a single source and hence there has been always a risk of making counterfeit, cloned item of the same. Same goes in the case of tea powder production too. Seeds used and leaves planted come from different sources which collectively determine the quality of the end tea powder. The proposed work tackles issues not only by providing traceability but also contributes in the overall security of the system by making use of Hyperledger fabric and Elliptic Curve Digital Signature Algorithm (ECDSA). In addition to these features, cross platform payment is also made possible by integrating the system with bridges. By making use of Hyperledger fabric and Interplanetary File System (IPFS), latency is decreased as well as throughput is increased. Additionally, making use of ECDSA provides same level of security with smaller key size and generates signature faster as compared to other cryptographic algorithms like Rivest-Shamir-Adleman (RSA).

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Security Enabled Supply-Chain System Implementation Using Blockchain Integration with Hyperledger and IPFS Techniques

Security Enabled Supply-Chain System Implementation Using Bl...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Amritesh Kumar Gupta Rajendra Kumar Dwivedi Dept. of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

One of the important areas that needs attention is supply chain in this fast expansion of globalization. There are numerous tiny parts that are summed up to obtain an end and furnished product be it agricultural goods in food supply chain or even other goods such as electronics. Now the quality of the final product depends upon the quality of the raw materials used. Since raw materials are manufactured, distributed, and sold globally, ensuring the integrity and security proposes a challenging task due to widespread counterfeit, cloned items, and other infiltration of untrusted items. The proposed work will counter these issues by not only providing traceability but also empowering transparency through Hyperledger. Along with these features, bridges are added to make the transfer of assets such as payments across different blockchains.

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Unsupervised anomaly detection algorithm for sensor data streams based on GAT-GAN 6

Unsupervised anomaly detection algorithm for sensor data str...

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6th International Conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Su, Yuhang Ma, Jun Fan, Jinyu Chen, Bohang Qinghai Normal University School of Physics and Electronic Information Engineering Qinghai Xining810016 China The College of Computer Qinghai Normal University The Computer Technology and Application College Qinghai University Qinghai Xining810016 China Qinghai Normal University The College of Computer Qinghai Xining810016 China

ISBN: (纸本)9781510682962

Detecting data flow anomalies within Wireless Sensor Networks (WSNs) and reaching the server can promptly identify faults and issue maintenance alerts, thereby ensuring safe and reliable operation of the system. However existing methods can utilize single variables in data streams to perform most anomaly detection tasks, they often ignore the correlation between variables, resulting in reduced detection performance. Therefore, our approach constructs a generative adversarial network model based on Graph Attention Network (GAT) and employs a dual autoencoding structure in both the generator and discriminator to learn the latent representation of the entire time series. The results of ablation experiments and comparison experiments on two real datasets with baseline models show that GAT-GAN outperforms the former in detecting data anomalies. © 2024 SPIE.

关键词： Gallium nitride

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