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A Detailed Review for Predicting the Quantity of Sugar from Sugarcane Using Various Models

IEEE Access 2025年 13卷 32122-32146页

作者： Narayanasamy, Kathirvel Venkatachalam, Ilayaraja Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India

The production of jaggery and white sugar is an essential aspect of the sugarcane industry. This review aims to assess a variety of techniques for estimating sugar content in sugarcane, including laboratory methods, formula calculations, device-based measurements, and machine-learning algorithms. This review aims to analyze various aspects of sugar production, including sugar prediction, processing techniques, and sugarcane quality parameters, and focuses on the use of sugarcane juice parameters to construct predictive models. Predicting the sugar content in sugarcane is crucial for optimizing agricultural practices, harvesting schedules, and processing operations in the sugar industry, including jaggery and white sugar production. Researchers have explored several methodologies and techniques for accurately predicting the sugar content of sugarcane prior to harvesting. The present analysis highlights that conventional regression algorithms can predict sugar content;however, multicollinearity restricts their effectiveness. To improve prediction accuracy and overcome existing limitations, future research should concentrate on developing advanced models that mitigate multicollinearity. In the existing research, it is difficult for users to comprehend the output of sugar prediction values. In the sugar industry, buyers and sellers typically measure sugarcane in metric tons, whereas sugar manufacturers typically measure sugar in kilograms. Therefore, it is necessary to analyze sugarcane juice parameters and develop a machine-learning model based on these data. Enhanced machine learning techniques and comprehensive quality parameters have the potential to significantly enhance the precision of sugar content predictions. © 2013 IEEE.

关键词： Prediction models

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Neural Network-Driven Base Station Selection in Next-Generation Cellular Networks 17

Neural Network-Driven Base Station Selection in Next-Generat...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Harrithha, B. Kotagi, Vijeth J. Thakur, Rahul Indian Institute of Technology Dharwad Department of Computer Science and Engineering Karnataka Dharwad India Indian Institute of Technology Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798331531195

With the exponential increase in data consumption from activities like streaming, gaming, and IoT applications, existing network infrastructure is under significant strain. To address the growing demand for higher data rates and better coverage, increasing base station density and exploring data offloading in unlicensed spectrum have become essential. While higher base station density reduces the distance between devices and network nodes, enhancing signal strength and data throughput, it also complicates the process of base station selection for users, particularly for those at the cell edge. Thus, ever-increasing demand for high-speed, low-latency communication services in 5G and 5G advanced New Radio (NR) unlicensed (NR-U) networks necessitates intelligent and dynamic approaches for user admission control and resource *** paper presents a novel solution employing neural network technology to address these critical challenges. We first propose an efficient algorithm for admission control and resource allocation. Upon generation of training data using the above algorithm, we propose a neural network-based framework for cellular networks in unlicensed spectrum such as LTE/LTE-U and 5G/NR-U networks. © 2025 IEEE.

关键词： Base stations

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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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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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An efficient secure channel establishment through lightweight key distribution in e-health communication systems

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Multimedia Tools and Applications 2025年第11期84卷 9111-9131页

作者： Ebrahimi, Iman Nikooghadam, Morteza Department of Computer Engineering and Information Technology Imam Reza International University Mashhad Iran

E-healthcare has significantly improved healthcare services and overall health by utilizing digital technologies such as the internet, computers, and mobile devices. However, secure communication channels play a critical role in ensuring the safety and confidentiality of patients' information. Consequently, several user authentication and key agreement protocols have been developed to address this issue. Although numerous proposed protocols are robust against security attacks, researchers have reported vulnerabilities. In this study, we evaluate one such protocol and identify vulnerabilities to insider attacks, server impersonation attacks, and replay attacks. To address these vulnerabilities, we propose a two-factor mutual authentication and key agreement scheme that offers a secure, efficient, and effective solution with low computation cost, enhanced security, and superior performance against diverse security attacks. Our proposed technique guarantees the integrity, confidentiality, and availability of patient information while ensuring the authenticity of users during communication. We conducted thorough comparisons focusing on the practical implementation of ARM microcontrollers to assess computation efficiency. The total estimated execution time of our proposed scheme for registration and authentication phases is approximately 975.8 µs, which is the shortest among all related works. This finding illustrates that our proposed scheme is well-suited for applications in e-health, telemedicine, and healthcare systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave and Future Networks 17

TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave a...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Prakash, Ujjayant Baswade, Anand M. Indian Institute of Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331531195

The rise in mobile internet usage especially using cellular networks demands efficient performance for web traffic, primarily made up of short TCP flows. For TCP, Cubic is the most widely deployed congestion control algorithm. mmWave and THz offer very high bandwidth and low latency, enabling faster speeds and reduced delay for companies deploying innovative applications, positioning them as the future of wireless technology. As such, we are compelled to thoroughly evaluate the performance of Cubic in various mmWave specific NLoS/LoS scenarios. In the process, we find Cubic really struggling when it comes to static NLoS scenarios and long connections particularly because Cubic keeps on attempting recovery from slow-start but fails due to the NLoS condition. This is where TCP Cubiwood comes to the rescue with an improvement of 189%in a simulated static NLoS scenario with results comparable to Cubic in wired conditions as well, thus becoming a suitable contender for replacing the historical algorithm. Delayed Congestion Response (DCR) tries to make TCP more robust towards channel errors in wireless networks so we also propose an additional flavor of Cubiwood, called Cubiwood-DCR which increases performance slightly in both wired and wireless high frequency scenarios. We extensively evaluate Cubiwood against Cubic, both with and without DCR enabled and make our code available for reproducibility and further research in the same direction. Moreover, the simplicity of this method makes our proposal a much more attractive solution. We believe the proposed solution is easy to implement, can replace Cubic and has the potential to improve performance in THz communication as well which is one of the competing spectrum bands for 6G networks. © 2025 IEEE.

关键词： Congestion control (communication)

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A Knowledge Discovery Framework for Maritime Intelligence on Dark Vessels 17

A Knowledge Discovery Framework for Maritime Intelligence on...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Jayaswal, Bharat Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Maritime Intelligence is essential for safeguarding maritime interests of coastal nations. Traditionally, vessel movements are tracked using self-reported positions via Automatic Identification System (AIS) transponders. However, vessels engaged in illicit activities often disable their AIS transponders to evade detection, thereby going dark. Satellite-based sensors, such as Synthetic Aperture Radar (SAR) and Visible Infrared Imaging Radiometer Suite (VIIRS) can be used to detect Dark Vessels. However, extraction of actionable insights for maritime intelligence on Dark Vessels is posed with several challenges. The spatial estimation of activities of vessels over various time periods is complicated due to spatial and temporal overlaps in satellite scans. This poster presents our ideas on knowledge discovery framework that integrates spatial analytics methods to gain actionable insights on Dark Vessels. Preliminary results are shown on Global Fishing Watch (GFW) Dataset of Dark Vessels. © 2025 IEEE.

关键词： Radar imaging

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Hyperbilirubinemia or jaundice detection using CNN in machine learning 1st

Hyperbilirubinemia or jaundice detection using CNN in machin...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gupta, Priyanka Upasna Rai, Vishal Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9781032874036

Jaundice, also called congenital hyperbilirubinemia, is a common condition caused by alterations in an infant’s red blood cell metabolism within the initial week of life. There are many symptoms associated with this multifactor disorder, and we have systems available both invasive and non-invasive. A practical and precise method for monitoring neonatal jaundice using the CNN algorithm has been described in this paper. The method’s accuracy and viability are predicated on the different detection systems examined. The methodologies and detection techniques discussed offer practical insights. To find a linear regression function, the SVR algorithm was used to find linear relationships within an elevated feature space. An approaching nonlinear function was used to map the input data to a scope. The color card is an additional method of detection that allows for the rapid identification of disease fatalities based on skin color and eyes. The old invasive methods of diagnosing jaundice have several drawbacks, including the inability to monitor simultaneously, the length of time required, the trauma to the patient, and the thousands of dollars involved. To reduce the discomfort associated with these techniques, a non-intrusive approach has been devised to identify jaundice in the early months of life, utilizing the image processing technologies and CNN algorithm. © 2025 the Author(s).

关键词： Neonatal monitoring

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Real-time Air Quality Monitoring and Context-aware Alert System 17

Real-time Air Quality Monitoring and Context-aware Alert Sys...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Pagoti, Yashwant Krishna Nikumbh, Sarthak Karmakar, Prasenjit Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their environment. Accurate and real-time monitoring increases our understanding of the surrounding air we breathe and allows us to improve it. The proposed system helps users minimize their exposure to harmful pollutants by providing real-time data on the concentration of various gases. The system triggers alerts directly to the subscribed user's mobile phones in case of any hazardous situations so that the user can take appropriate actions immediately to resolve the issue. Our extensive data collection and evaluation with off-the-shelf machine learning models indicated an overall F1-score of 99.54 (±0.5)% with a simple random forest model in classifying burning, cooking, occupancy in a closed room, using aerosol products, based on the sensor's readings. © 2025 IEEE.

关键词： Indoor air pollution

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