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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Deyasi, Arpan Techno International New Town Department of Computer Application Kolkata India RCC Institute of information Technology Department of Computer Science and Engineering Kolkata India B. P. Poddar Institute of Management & Technology Department of Electronics and Communication Engineering Kolkata India RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

In this paper, novel cancer detection method using Kernel Based Fuzzy Simulation Model (KBFSM) represented with enhanced accuracy of 12%, considering MRI based breast cancer images. Developed framework, as suggested in this paper, Effectiveness and superiority of the suggested approach are proven by thorough testing on a variety of datasets and thorough assessment metrics, highlighting its capacity to produce accurate and nuanced segmentation results. Fuzzification and defuzzification of images using Fuzzy C means algorithm, speaks in favor of the proposed analysis, as tested over several dataset, owing to minute pixel intensity modification when associated with malignant tissues. Result is supported by histogram analysis, which paves the way of future research in the field of lung cancer using the same technique. © 2024 IEEE.

关键词： Lung cancer

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An Adaptive Frequency Hopping Scheme for Cognitive Radio Networks using Reinforcement Learning 3

An Adaptive Frequency Hopping Scheme for Cognitive Radio Net...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Meena, Y.R. Acharjya, Kalyan Upadhyay, Ramakant JAIN School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350354171

Adaptive Frequency Hopping (AFH) is an essential generation for Cognitive Radio Networks (CRNs). It no longer bests improves spectral efficiency and quality of service of CRNs but also prevents interference amongst customers. However, traditional AFH schemes need help with essential problems. First, they require a large amount of previous expertise approximately the surroundings. 2d, they remain highly static and inflexible, situation to adjustments in the external environment. This paper presents an Adaptive AFH scheme using Reinforcement to gain knowledge of (RL) for CRNs. First off, an environment-conscious AFH approach based on the user-described frequency occupancy possibility is proposed. The method is composed of a probabilistic hopping sequence generator and an intra-frequency hopping detector. Secondly, an adaptive AFH scheme with temporal-spatial getting to know is proposed, which incorporates an actor-critic-based RL controller and recreation-theoretic modeled surroundings. The RL controller generates a dynamic hopping sequence with low hopping frequency collisions at the same time as the sport-theoretic surroundings fashion the temporal-spatial modifications of the wireless medium. Sooner or later, the proposed scheme is proven in a simulated CRN environment. © 2024 IEEE.

关键词： Frequency hopping

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OdCFU-Net for Brain Tumor Segmentation Based on MRI

OdCFU-Net for Brain Tumor Segmentation Based on MRI

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2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2024

作者： Wu, Shang Yang, Sijing Li, Boyue Yin, Shimin Zhu, Jianming Li, Shiyong Chen, Zhencheng Guilin University of Electronic Science and Technology School of Computer and Information Security China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China Key Laboratory of Biomedical Sensors and Intelligent Instruments Guangxi Colleges and Universities Guilin China Guangxi Engineering Technology Research Center of Human-Physiological Information Noninvasive Detection Guilin China School of Computer and Information Security Guilin University of Electronic Science and Technology China

ISBN: (纸本)9798350322996

In the field of medical image analysis, the accurate segmentation of brain tumors plays a crucial role in the diagnosis and treatment of brain-related diseases. This study aims to address key challenges in this domain, with a particular focus on improving feature extraction, information integration, and model generalization capabilities. By concurrently introducing multi-dimensional attention mechanisms in convolutional operations, a method has been developed to dynamically generate convolution kernels in spatial, input channel, and output channel dimensions. This approach optimizes the limitations of traditional static convolutional kernels, maintaining high-performance efficiency while reducing the model's parameter count. To overcome the deficiencies of traditional convolutional networks in capturing global information, channel and spatial attention mechanisms have been integrated between convolutional layers. This enhances adaptability and global information retrieval capabilities, allowing the model to dynamically adjust the weight of feature maps based on different task requirements. Leveraging the advantages of Vision Transformer in handling global information and addressing its limitations in capturing details and training on small datasets, the algorithm aligns and learns feature representations effectively by incorporating channel attention and normalizing attention weights within its multi-head attention. When applied to the BraTs2021 multi-modal brain tumor image database, the proposed algorithm accurately delineates brain tumor regions, demonstrating excellent performance. © 2024 IEEE.

关键词： Semantic Segmentation

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DS-YOLO:A dense small object detection algorithm based on inverted bottleneck and multi-scale fusion network

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Biomimetic Intelligence & Robotics 2024年第4期4卷 64-75页

作者： Hongyu Zhang Guoliang Li Dapeng Wan Ziyue Wang Jinshun Dong Shoujun Lin Lixia Deng Haiying Liua School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Unimation Intelligent Technology Co. Jinan 250101China School of Computer Science University of St AndrewsSt Andrews KY169AJUnited Kingdom

In the field of security,intelligent surveillance tasks often involve a large number of dense and small objects,with severe occlusion between them,making detection particularly *** address this significant challenge,Dense and Small YOLO(DS-YOLO),a dense small object detection algorithm based on YOLOv8s,is proposed in this ***,to enhance the dense small objects'feature extraction capability of backbone network,the paper proposes a lightweight *** improved C2fUIB is employed to create a lightweight model and expand the receptive field,enabling the capture of richer contextual information and reducing the impact of occlusion on detection ***,to enhance the feature fusion capability of model,a multi-scale feature fusion network,Light-weight Full Scale PAFPN(LFS-PAFPN),combined with the DO-C2f module,is *** new module successfully reduces the miss rate of dense small objects while ensuring the accuracy of detecting large ***,to minimize feature loss of dense objects during network transmission,a dynamic upsampling module,DySample,is ***-YOLO was trained and tested on the CrowdHuman and VisDrone2019 datasets,which contain a large number of densely populated pedestrians,vehicles and other *** evaluations demonstrated that Ds-YOLO has advantages in dense small object detection *** with YOLOv8s,the Recall and mAP@0.5 are increased by 4.9%and 4.2%on CrowdHuman dataset,4.6%and 5%on VisDrone2019,***,DS-YOLO does not introduce a substantial amount of computing overhead,maintaining low hardware requirements.

关键词： Denseobjects detection LFS-PAFPN DOConv C2fUIB YOLOv8

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RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models 41

RoboMP2: A Robotic Multimodal Perception-Planning Framework ...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Qi Li, Hao Deng, Xiang Shao, Rui Wang, Michael Yu Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Engineering Great Bay University China School of Computing and Information Technology Great Bay University China

Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for embodied agents. However, these methods exhibit limited generalization capabilities on unseen tasks or scenarios, and overlook the multimodal environment information which is critical for robots to make decisions. In this paper, we introduce a novel Robotic Multimodal Perception-Planning (RoboMP2) framework for robotic manipulation which consists of a Goal-Conditioned Multimodal Preceptor (GCMP) and a Retrieval-Augmented Multimodal Planner (RAMP). Specially, GCMP captures environment states by employing a tailored MLLMs for embodied agents with the abilities of semantic reasoning and localization. RAMP utilizes coarse-to-fine retrieval method to find the k most-relevant policies as in-context demonstrations to enhance the planner. Extensive experiments demonstrate the superiority of RoboMP2 on both VIMA benchmark and real-world tasks, with around 10% improvement over the baselines. Copyright 2024 by the author(s)

关键词： Semantics

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NLP-Enhanced Long Document Summarization: A Comprehensive Approach for information Condensation 2

NLP-Enhanced Long Document Summarization: A Comprehensive Ap...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Maurya, Karan Guru Prasad, M.S. Gupta, Shubham Ameta, Hardik Negi, Abhijeet Singh Amrutha, P.V. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Reva University School of computing & Information Technology Bengaluru India

ISBN: (纸本)9798350371314

The present era is characterized by information overload, making the ability to distil multitude of data into concise and meaningful summaries very important. The response to ever-increasing challenge is this revolutionary NLP based model called text Summarizer that will redefine the interactions with information. Text Summarizer aims at providing users with quick, accurate and coherent summaries of lengthy texts so to promote their understanding. The cutting-edge methods harnessed by the Text Summarizer enable it to assimilate given text context, identify major themes and provide brief summaries that capture the essence of source content. To build this text summarizer advanced NLP algorithms are used. Modern deep learning models are mainly based on transformer architectures like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer);they are used because they have the capacity to capture complicated language patterns and nuances. Transformer is a sequence-to-sequence model which uses attention technique to process text sequentially. It is made up of encoders and decoders interconnection. This transformer takes in the text input and provide the summary as output. Transformer discussed in this paper has an accuracy of around 98% which is trained over cnn-dailymail dataset. © 2024 IEEE.

关键词： Natural language processing systems

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Adaptive Offloading and Enhancement for Low-Light Video Analytics on Mobile Devices

Adaptive Offloading and Enhancement for Low-Light Video Anal...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Yuanyi Yang, Peng Qin, Tian Hou, Jiawei Zhang, Ning Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada

ISBN: (纸本)9798350351255

In this paper, we explore adaptive offloading and enhancement strategies for video analytics tasks on computing-constrained mobile devices in low-light conditions. We observe that the accuracy of low-light video analytics varies from different enhancement algorithms. The root cause could be the disparities in the effectiveness of enhancement algorithms for feature extraction in analytic models. Specifically, the difference in class activation maps (CAMs) between enhanced and low-light frames demonstrates a positive correlation with video analytics accuracy. Motivated by such observations, a novel enhancement quality assessment method is proposed on CAMs to evaluate the effectiveness of different enhancement algorithms for low-light videos. Then, we design a multi-edge system, which adaptively offloads and enhances low-light video analytics tasks from mobile devices. To achieve the trade-off between the enhancement quality and the latency for all system-served mobile devices, we propose a genetic-based scheduling algorithm, which can find a near-optimal solution in a reasonable time to meet the latency requirement. Thereby, the offloading strategies and the enhancement algorithms are properly selected under the condition of limited end-edge bandwidth and edge computation resources. Simulation experiments demonstrate the superiority of the proposed system, improving accuracy up to 20.83% compared to existing benchmarks. © 2024 IEEE.

关键词： Scheduling algorithms

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A Memristive Chaotic 5D System with 3D Offset Boosting

A Memristive Chaotic 5D System with 3D Offset Boosting

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zhang, Xin Li, Chunbiao Cen, Xiaoliang Akgul, Akif Gao, Yikai School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Hitit University Department of Computer Engineering Corum19030 Turkey

ISBN: (纸本)9798350316230

In this article, a 5D memristive chaotic system with three 3D offset boosting is designed and analyzed. By introducing three independent offset constants in the system equation, different regimes of offset boosting are defined by the external constants including various combinations of reverse control and differential control. Furthermore, the offset boosting is also influenced by the coefficient of the linear terms, which is a bifurcation parameter. Finally, the memristive 5D chaotic circuit is designed based on the software of Multisim. Circuit simulation validates the function of offset boosting from the separated three constants. © 2023 IEEE.

关键词： Chaotic systems

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Biometric Web based Vote Cast using Blocks 3

Biometric Web based Vote Cast using Blocks

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Abinaya, M. Priya, P.Kaviya Mirza, Mohsina Anbumani, A. Sathya, R. Sethuram, M. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India P.A College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Global College of Engineering and Technology Department of Electrical and Electronic Engineering India Paavai College of Engineering Department of Computer Science and Engineering Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350343632

Web based vote casting is a form of voting in which the voter is able to cast their votes through any web portal wherever in the world. In the Biometric method, voter can cast the votes using their finger impression. In this method the voter can selects the login portal which is particularly designed for an election commission. Each person needs to register the election commission website, using the registration details substantiate themselves before the machine authorize the user to sight the ballot visible on the screen. The voter is then allowed to pick their selected candidate and then cast the votes which would be passed to the election server for the refining process. In 'E-Voting' method a voter can cast their votes through online without any difficulty. If all the entries provided by the user are precise user can login the system using the user ID and password voter can cast their votes through online wherever in the world. If an information given by the voter is not correct then the entry will be discarded and it shows the error message. This technique is mainly implemented to reduce the difficulty of the election commission and voter. It is a user-friendly approach, also it avoids the unwanted crowds during the election period. Here, this study has used immutable ledger technology that is a blockchain based application, which provides the security and also increase the efficiency of the voting process. © 2023 IEEE.

关键词： Blockchain Electronic Voting (E-Voting) Personnel computer (PC)

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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