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Performance Improvement: Modulation Recognition Scheme Using CVCNN-LSTM Method 1

Performance Improvement: Modulation Recognition Scheme Using...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Krishnamoorthy, Ramkumar Agarwal, Trapty Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The process for automatic modulation recognition (AMR) is important to industrial cognitive radio Models. Although they have made significant strides in modulation detection, state-of-the-art real-valued CNNs compromise model interpretability and signal phase integrity by treating complicated signal components as distinct actual inputs. The CVCNN-LSTM is a novel AMR technique that is presented in this research. In order to capitalize on the advantages associated with both networks while minimizing their drawbacks, this study intends to use CNNs along with a LSTM. This method improves recognition performance by strengthening the learning for the initial in-phase/quadrature information (I/Q) signal characteristics. The suggested network consists of one LSTM layer and many local features learning unit (LFLUs). Convolutional and average pooling layers, which are the main components of an LFLU, are essential for allowing hierarchical correlation extraction and local correlation learning. Researchers recommend employing end-to-end complex-valued CNN & LSTM for modulation recognition rather than dual-channel real-valued networks as in-phase/quadrature information naturally including complex-valued system. Researchers analyse complex-valued networks via experiments and show that this methodology outperforms the state-of-the-art techniques in AMR area. © 2025 IEEE.

关键词： Convolutional neural networks

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Survey on recent advances in IoT application layer protocols and machine learning scope for research directions

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Digital Communications and Networks 2022年第5期8卷 727-744页

作者： Praveen Kumar Donta Satish Narayana Srirama Tarachand Amgoth Chandra Sekhara Rao Annavarapu Department of Computer Science and Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India School of Computer and Information Sciences University of HyderabadHyderabad500046India Mobile&Cloud Lab Institute of Computer ScienceFaculty of Science and TechnologyUniversity of Tartu50090Estonia

The Internet of Things(IoT)has been growing over the past few years due to its flexibility and ease of use in real-time *** IoT's foremost task is ensuring that there is proper communication among different types of applications and devices,and that the application layer protocols fulfill this ***,as the number of applications grows,it is necessary to modify or enhance the application layer protocols according to specific IoT applications,allowing specific issues to be addressed,such as dynamic adaption to network conditions and ***,several IoT application layer protocols have been enhanced and modified according to application ***,no existing survey articles focus on these *** this article,we survey traditional and recent advances in IoT application layer protocols,as well as relevant real-time applications and their adapted application layer protocols for improving *** changing the nature of protocols for each application is unrealistic,machine learning offers means of making protocols intelligent and is able to adapt *** this context,we focus on providing open challenges to drive IoT application layer protocols in such a direction.

关键词： Internet of things Machine learning Application layer Request-response protocols Publish-subscribe protocols

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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Intelligent Intrusion Detection Framework for Anomaly-Based CAN Bus Network Using Bidirectional Long Short-Term Memory

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 541-564页

作者： Kishore, Ch. Ravi Rao, D. Chandrasekhar Nayak, Janmenjoy Behera, H.S. Department of Information Technology Veer Surendra Sai University of Technology Burla768018 India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada757003 India

The modern vehicle depends on controller area network (CAN) bus technology to facilitate communication among electronic control units. Although the CAN bus technology is significant, it lacks the typical authentication and authorization protocols, allowing interactions susceptible to unauthorized access. Attackers can easily target the CAN bus network because of this vulnerability. Attackers have several options when it comes to infiltrating a CAN bus system, and several of them are focused on either denial of service, fuzzing, or spoofing attacks. To ensure current autos are secure, it is critical to developing ways to guard against these threats. In order to identify and eliminate vulnerabilities affecting CAN bus networks, prior research has developed several works, including intrusion detection systems (IDSs) based on (ML) and deep learning (DL) approaches such as the long short-term memory (LSTM)-based IDS. Many previous studies employed ML or DL methodologies to identify and mitigate CAN bus network assaults. Technological advancements increase attack variety. An IDS has been designed for the CAN bus that is more precise and accurate. CAN bus network assaults are being detected and mitigated in this study using a Bidirectional_LSTM-based IDS. Using the suggested framework, non-attack and attack records can be discriminated and precisely characterized. With improved accuracy and an intelligent IDS creating process, the suggested model outperforms other standard ML approaches. The proposed method outperforms other competitive methods with an accuracy of 99.09% and signifies the efficacy of the developed method. © The Institution of Engineers (India) 2024.

关键词： Authentication

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Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

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computer systems science & Engineering 2023年第2期44卷 1295-1307页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Examining and Comparing: Performance of OFDM Utilised in 6G Communication 1

Examining and Comparing: Performance of OFDM Utilised in 6G ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Acharjya, Kalyan Suneetha, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798331518578

It is necessary to have a new and enhanced waveform system for transmission that is compatible with the band in order to accommodate the forthcoming 6G mobile network. Due to the fact that orthogonal frequency-division multiplexing (OFDM) is frequent wave form in 4Generation LTE & 5Generation new radio (NR) for enhanced mobile broadband or eMBB transmission of data on multicarrier, it is very probable that a more advanced version of OFDM will be developed for elsewhere 5G (B5G) along with the 6G mobile message scheme. In orthogonal frequency division multiplexing (OFDM), several orthogonal subcarrier indicators through overlapping frequencies remain sent nearby in order to transport data simultaneously. The methods of FAST Fourier transform are the foundation upon which demodulation is built. For the purpose of altering every subcarrier (the signal) at a low symbolic rate, a conventional modulation approach, such as quadrature amplitude variation or else phase change keying, is used. As long as the bandwidth remains the same, this ensures that the overall data rates remain the same as they would be with conventional single-carrier modulation techniques. By altering the number & indexing of active subcarriers that for every OFDM subblock, a new transmission technique has been devised. This approach is capable of transmitting a greater amount of information. There is a difference between OFDM-HNIM, OFDM-SNM & OFDM-IM when compared to itself. This mixed modulation approach is superior to previous OFDM modulation systems in terms of both the spectrum & energy economy that it offers. When it comes to lower modulation rates, such as BPSK, which may deliver great dependability while still being very simple, this is particularly true. It is possible to employ this modulation approach for systems that need improved energy as well as spectrum economy, strong reliability, and minimal complexity among their requirements. In addition to flow and the greatest feasible rate, it pr

关键词： Orthogonal frequency division multiplexing

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A Content-Based Filtering Approach for Personalized Movie Recommendations 2

A Content-Based Filtering Approach for Personalized Movie Re...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Vashisht, Rohit Sharma, Rahul Kumar Pant, Maneesh Thakral, Gagan Naresh, Prashant Department of Computer Science & Information Technology KIET Group of Institutions Ghaziabad India Department of Computer Science & Engineering KIET Group of Institutions Ghaziabad India Department of Information Technology JSS Academy of Technical Education Noida India

ISBN: (纸本)9798331530389

The recommendation system of today has made it simple to locate the necessities. Movie recommendation systems are designed to assist movie buffs by making recommendations for films to see, saving them the trouble and effort of having to sort through hundreds or even millions of options. The Recommender system has become a major area of research because it helps people find things online by making suggestions that are similar to what they are looking for. In this paper, we introduce a system called MOVIEHOPE for recommending movies. It uses a content-based filtering method that uses the user's preferences and the item's description to make a profile. In CBF, we use keywords to describe items instead of the user's profile to show what they like or do not like. In plain English, the CBF algorithm likes or recommends things that were liked in the past. It looks at how items have been rated before and suggests the best item that fits. © 2025 IEEE.

关键词： Collaborative filtering

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Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

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computer systems science & Engineering 2023年第2期44卷 991-1007页

Establishing a system for measuring plant health and bacterial infection is critical in ***,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been *** inspection can determine which plants reﬂect the quantity of green light and near-infrared using infrared light,both visible and eye using a *** goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classiﬁcation *** neural networks in unmanned aerial vehi-cles image were *** convey this interest,the rice’s health and bacterial infec-tion inside the photo were *** project entailed using pictures to identify bacterial illnesses in *** shape and distinct characteristics of each infection were *** symptoms were deﬁned using machine learning and image processing *** steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by *** proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

关键词： Bacterial infection detection adaptive deep learning unmanned aerial vehicles image retrieval

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Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification

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International Journal of information technology (Singapore) 2024年第2期16卷 817-829页

作者： Admass, Wasyihun Sema Munaye, Yirga Yayeh Bogale, Girmaw Andualem Department of Information Technology Assosa University Assosa Ethiopia Department of Information Technology Injibara University Injibara Ethiopia Department of Computer Science Assosa University Assosa Ethiopia

This study suggests a convolutional neural network (CNN) and histogram oriented gradients (HOG)-based automatic detection and classification system for mango disease. Early detection is essential for efficient disease management since mango disease can have a major influence on fruit quality and yield. The suggested system makes use of the CNN algorithm for extracting features and the HOG technique for capturing shape and texture data. The extracted features are subsequently used to feed a disease classification model for disease detection. The efficiency of the proposed model is demonstrated by experimental findings, which achieve excellent accuracy in both disease detection and classification tasks. The CNN-HOG hybrid model outperforms CNN or HOG alone in terms of performance, demonstrating the complementary nature of these two methods for the detection and classification of mango disease. The system's performance is evaluated using measures for accuracy, precision, and recall and the proposed model accuracy achieved a training accuracy of 98.80% and a testing accuracy of 99.5%. This research helps establish effective and trustworthy tools for managing mango disease by automating the detection and classification process. This enables prompt intervention and reduces crop losses. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Automatic detection Classification Convolutional neural network Histogram oriented gradient Hybrid approach Mango disease

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