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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kalaiselvi, K. Thilak, K.Deepa Devi, K.Lalitha Kumaresan, K. Malathi, M. Faculty of Engineering and technology Department of Networking and communications SRM Institute of science and technology Kattankulathur Tamilnadu Chennai India Department of Computer science and Engineering Sathyabama Institute of Science and Technology India Department of CSE KSR College of Engineering Tiruchengode Tamilnadu Namakkal India Tamilnadu Namakkal India

ISBN: (纸本)9798331300579

A Wireless Sensor Network is made up of a large number of small, low-power sensor nodes that are randomly or manually distributed across an uncontrolled target ***, the WSNs' main constraint is the sensor nodes' restricted power *** a result, energy saving at sensor nodes is the most difficult barrier for WSNs' long-term *** and low-energy routing are two well-known optimization tasks that have received a lot of attention in the quest to extend the life of wireless sensor networks (WSNs).In a periodic data gathering application, the Particle Swarm Optimization (PSO) technique is employed to overcome this *** monitor the environment on a regular basis and send data to the base station (BS), which analyses the data and draws conclusions about the field activities.A cluster is used to send the data to the base station.A cluster head receives data from sensor nodes and transmits it to the cluster head, which combines the information and sends it to the base *** packet delivery ratios and throughput have both been *** a result, total energy consumption is reduced. © Grenze Scientific Society, 2024.

关键词： Particle swarm optimization (PSO)

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Constrained Secrecy Capacity of Finite-Input Intersymbol Interference Wiretap Channels

Constrained Secrecy Capacity of Finite-Input Intersymbol Int...

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作者： Nouri, Aria Asvadi, Reza Chen, Jun Vontobel, Pascal O. Shahid Beheshti University Faculty of Electrical Engineering Department of Telecommunications Tehran*** Iran McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada Institute of Theoretical Computer Science and Communications The Chinese University of Hong Kong Department of Information Engineering Shatin Hong Kong

We consider reliable and secure communication over intersymbol interference wiretap channels (ISI-WTCs). In particular, we first derive an achievable secure rate for ISI-WTCs without imposing any constraints on the input distribution. Afterwards, we focus on the setup where the input distribution of the ISI-WTC is constrained to be a time-invariant finite-order Markov chain. Optimizing the parameters of this Markov chain toward maximizing the achievable secure rates is a computationally intractable problem in general, and so, toward finding a local maximum, we propose an iterative algorithm that at every iteration replaces the secure rate function with a suitable surrogate function whose maximum can be found efficiently. Although the secure rates achieved in the unconstrained setup are potentially larger than the secure rates achieved in the constrained setup, the latter setup has the advantage of leading to efficient algorithms for estimating and optimizing the achievable secure rates, and also has the benefit of being the basis of efficient coding schemes. © 1972-2012 IEEE.

关键词： Markov processes

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An Early Detection of Ovarian Cancer and The Accurate Spreading Range in Human Body by using Deep Medical Learning Model

An Early Detection of Ovarian Cancer and The Accurate Spread...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Saravanan, V. Sankaradass, Veeramalai Shanmathi, M. Bhimavarapu, John Philip Deivakani, M. Ramasamy, S. Dambi Dollo University Department of Computer Science Ethiopia Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Engineering College Electronics and Communications Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Guntur India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul India Hindusthan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350323887

Current methods of ovarian cancer screening include physical examination, ultrasound, and blood tests to detect the cancer antagonist (CA-125). All of these are subject to appropriate restrictions. That is, the correct diagnosis of cancer by biopsy depends on the expertise of the doctor performing the test. In many cases, other disorders are also mistaken for ovarian cancer. Ultrasound also does not help differentiate tumors as malignant or benign. Although antibody tests are accurate, they do not help diagnose the disease in its early stages. Also, CA-125 antagonist levels may also increase due to growth or inflammation of the uterus. Therefore, even if all these methods are combined, it is not possible to detect the disease at an early stage. In this paper an innovation model to identify the ovarian cancer and its spreading range by using the deep medical learning model. Ovarian cancer often goes undiagnosed until it spreads to the pelvis and abdomen. But if these are detected early, these malignant tumors can be surgically removed so that they do not spread to other parts of the body. When this diagnostic approach is combined with chemotherapy for cancer, patients' quality of life and well-being improves © 2023 IEEE.

关键词： Diseases

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Apple and Tomato Leaves Disease Detection using Emperor Penguins Optimizer based CNN 9

Apple and Tomato Leaves Disease Detection using Emperor Peng...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Suguna, R. Vinoth Kumar, C.N.S. Deepa, S. Arunkumar, M.S. Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Tamilnadu Erode638401 India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Technology -PG Kongu Engineering College Tamilnadu Erode Perundurai638052 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350397376

The economy of the country is heavily dependent on agricultural productivity. The largest number of the crop yield trust on growth of the healthy plants. Nearly all the times the crop yields are heavily affected by leaves diseases. The vital role of the agriculture field is to discover the plant's leaves diseases in a timely premature point. An automatic disease detection technique is mandatory in agriculture field to identify and determine the kind of plant leave diseases in an earlier stage and give the right pesticides. The healthy plants are the major effect on quality and productivity of the agriculture products. An image segmentation and classification system is presented in this study to recognize the plant leaf diseases automatically. In this work, YOLO network of version 4 is used for object spotting (detection) and segmentation for detecting the affected leaves such as Apple and Tomato Leaves. Here, Convolutional Neural Network (CNN) acts as both feature extraction and also classifier, i.e. the color features and statistical attributes are bringing out using CNN. Finally, the learning rate of CNN is optimized by using Emperor Penguin Colony Optimizer (EPC). The prominent aspects of EPO is producing and computing the huddle boundary, distance, temperature, and effective mover all at the single point time. The experiments are done on publicly available datasets to analyze the performance of proposed algorithm with existing techniques. © 2023 IEEE.

关键词： Fruits

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A Pricing-Based Cooperative Relay Selection Scheme for Reliable communications

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China communications 2024年第8期21卷 30-44页

作者： Xiao Yulong Wu Yu Amr Tolba Chen Ziqiang Li Tengfei School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and ***,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission *** enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction *** propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration *** results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

关键词： cooperative communication edge net-work energy harvesting relay selection

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TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

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IEEE Transactions on Image Processing 2024年 33卷 738-752页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui He, Jiang Lin, Chia-Wen Zhang, Liangpei Wuhan University School of Geodesy and Geomatics Wuhan430079 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300 Taiwan Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens;(2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top-k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top-k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://***/XY-boy/TTST for validation. © 1992-2012 IEEE.

关键词： Remote sensing

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Local-Global Temporal Difference Learning for Satellite Video Super-Resolution

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第4期34卷 2789-2802页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui Jin, Xianyu He, Jiang Zhang, Liangpei Lin, Chia-Wen The School of Geodesy and Geomatics Wuhan University Wuhan430000 China The School of Computer Science and Technology Harbin Institute of Technology Harbin150000 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430000 China The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are less generalized in large-scale or complex scenarios, especially in satellite videos. In this paper, we propose to exploit the well-defined temporal difference for efficient and effective temporal compensation. To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies since we observe that these discrepancies offer distinct and mutually complementary properties. Specifically, we devise a Short-term Temporal Difference Module (S-TDM) to extract local motion representations from RGB difference maps between adjacent frames, which yields more clues for accurate texture representation. To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation. Moreover, we further propose a Difference Compensation Unit (DCU) to enrich the interaction between the spatial distribution of the target frame and temporal compensated results, which helps maintain spatial consistency while refining the features to avoid misalignment. Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches. Code will be available at https://***/ XY-boy/LGTD. © 2023 IEEE.

关键词： Three dimensional displays

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Novel Distributed Approach for Next Best Offer using XGboost

Novel Distributed Approach for Next Best Offer using XGboost

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Shatakshi Chaudhari Prerana Malladi Sai Ashwini D Bundele Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Electronics and Communications Engineering Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Product recommendations have become integral to business development and customer engagement strategies. While traditional techniques like market basket analysis are effective in retail, they are insufficient for the financial sector due to the diversity of banking products and the lack of direct dependency among them. This research paper proposes a novel distributed framework for developing an automated recommendation system leveraging XGBoost to predict banking product purchases based on categorized customer profiles. The framework integrates distributed computing technologies such as Hadoop for data storage and PySpark for scalable data processing. The system classifies customers into distinct categories based on their transaction history, demographic attributes, and banking behaviors to deliver personalized product suggestions. Model performance is evaluated using metrics such as the ROC curve, precision, recall, and F1-score. Empirical results demonstrate the system’s effectiveness in enhancing lead generation, targeting high-potential customers, and improving customer satisfaction in real-world financial markets.

关键词： Measurement Technological innovation Memory Banking Lead History Ensemble learning Informatics Recommender systems Business

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E-commerce Product Review Analysis based on Multi-class Support Vector Machine 1

E-commerce Product Review Analysis based on Multi-class Supp...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Alzubaidi, Laith H. Hatem Mohammed, Ibraheem Ranjith Kumar, Gotte Sarada, B. Radha, N. The Islamic University Najaf Iraq Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Malla Reddy Engineering College for Women Department of CSE-Data Science Hyderabad India Department of Cse Annapoorana Engineering Salem India

ISBN: (纸本)9798350316469

Recently, an extensive number of consumers can choose their best options from automated retailers and compare products in online stores. Sentiment analysis is frequently employed in applications meant to demonstrate and provide client service as the voice of the user. On E-commerce websites, user reviews provide helpful information about the product. Sentiment analysis of the text reviews helps forecast product sales by assessing user sentiment regarding the product. The Word Embedding Attention approach assigns additional weight to words that have a strong association with a specific class. Sentiment analysis machine learning models provide a thorough depiction of capabilities and far surpass conventional feature-based methods. The obj ective of this research is to improve sentiment analysis performance by creating a weighted ensemble with a Multi-class Support Vector Machine (MSVM) model utilising novel word embedding techniques. Because of the weighted ensemble's higher generalisation skills, MSVM produces superior results. When comparing existing methods for sentiment analysis, the Weighted ensemble with MSVM has produced higher results in terms of accuracy, precision, f-score, and recall. The Weighted ensemble with an MSVM has achieved 99.87 % accuracy, 99.39% precision, 99.03% f-score, and 99.690/0 recall in sentiment analysis when comparing existing methods such as LSTM+FL, APSO-LSTM, ABCDM, SSentiA, and Hybrid CNN+LSTM. © 2023 IEEE.

关键词： E-commerece Multi-class Support Vector Machine Natural language processing Sentiment analysis Weighted ensemble Word embedding attention

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Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance

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IEEE Transactions on Mobile Computing 2025年

作者： Lee, Chao-Yang Tsai, Ang-Hsun Wang, Li-Chun National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Taiwan Feng Chia University Department of Communications Engineering Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Taiwan

This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided unmanned aerial vehicle (UAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided UAV can cause "an inverted pendulum effect" and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided UAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided UAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided UAV under a gusty wind disturbance. 2002-2012 IEEE.

关键词： Buoyancy

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