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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Proposed Solution for Currency Classification and Fake Currency Detection Using Machine Learning 4

Proposed Solution for Currency Classification and Fake Curre...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Narale, Dnyaneshwari. B. Pujar, Anita. M. Pardeshi, K.R. Walchand Institute of Technology Computer Science and Engineering Solapur India

ISBN: (纸本)9798331523923

The rapid advancement of digital technologies has greatly impacted financial systems, creating challenges in accurately classifying currencies and detecting counterfeits in various real-world scenarios. Traditional approaches, such as manual inspections and specialized tools like UV light detectors, tend to be time-consuming, expensive, and less effective against sophisticated counterfeiting methods. Recent developments in machine learning and image processing have shown potential, utilizing supervised machine learning models. Nonetheless, issues remain in managing variations due to wear, folds, inconsistent lighting, and different currency designs. This research introduces a robust machine learning-based system for classifying currencies and detecting counterfeit bills. By combining advanced image processing techniques to extract essential features (like texture, edges, and color histograms) with a hybrid model that integrates CNN, Random Forest, and XGBoost, the proposed framework tackles challenges related to real-world variability and subtle signs of counterfeiting. The goal of the system is to achieve high accuracy and reliability in identifying counterfeit notes while ensuring it can scale and adapt to new counterfeiting methods. © 2025 IEEE.

关键词： Adversarial machine learning

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Augmented Array based Efficient Fill Ratio Estimation in a Bloom Filter 2

Augmented Array based Efficient Fill Ratio Estimation in a B...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Singh, Amritpal Singh, Amrinderpreet Batra, Shalini Department of Computer Science and Engineering National Institute of Technology Jalandhar Jalandhar India Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Patiala India

ISBN: (纸本)9798331504106

Analysing patterns/trends and associations from heterogeneous data coming at varied speeds and formats require data structures which can handle large and dynamic data efficiently. Bloom Filter (BF), a probabilistic data structure, can be considered as one of the alternatives as it is space efficient and handles membership query effectively. To accommodate the dynamic data sets in the BF when ever the Fill Ratio (FR) exceeds the defined limits, two major approaches have been discussed in the literature: increasing the number of BFs as the data increases and refreshing the existing BF by deleting the staled data. In this paper, a novel approach motivated from Double probing named (DH-FRP) has been proposed to compute FR using augmented array to sample bit locations from parent BF. The augmented array keeps track of the sampled bit values at any given time and stores at contiguous locations for effcient bit counting and FR calculation. Markov Bound has been used to determine whether to recompute the step size or not. Since the rate of dynamic data coming from various sources may be uniform or non uniform, both cases have been considered and FR has been calculated for standard BF and Counting BF. It has been experimentally proved that the proposed scheme outperforms the given state-of-art techniques in terms of FR calculation time and accuracy. ©2024 IEEE.

关键词： Markov processes

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Deep Learning Strategies for Identifying Machine-Generated Text 19

Deep Learning Strategies for Identifying Machine-Generated T...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yadagiri, Annepaka Pakray, Partha Computer Science & Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331507817

Generative AIs like LLMs are now accessible to the general public. For example, students can utilize these tools to create essays or complete theses. However, how is a teacher supposed to determine if a text was composed by the student or an AI? Using deep learning techniques, we investigate novel and classic approaches for detecting text created by artificial intelligence. We also study the more complex instance when the AI is asked to write the text in a way that a human would not recognize as AI-generated, as we discovered that categorization is more challenging in this scenario. For our studies, we used llm-detect-ai-generated text from the Kaggle competition dataset, which included texts written by students and texts produced using different LLMs. Our top systems achieve an accuracy of 0.98% and F1 scores of more than 0.98% in classifying simple and complex texts produced by humans and AI-Genereted. The systems combine features such as TF-IDF vectorization, word2Vec, and word embedding properties. Our findings demonstrate that these additional characteristics significantly enhance the performance of several classifiers. Compared to deep learning models, our best-performing model for detecting AI-generated text outperforms even the fine-tuned ROBERTA-Open-AI classifier, achieving an accuracy of 0.98%. This underscores the efficacy of our proposed approach in distinguishing between human and AI-generated content. © 2025 IEEE.

关键词： Adversarial machine learning

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Carbon-based interface engineering and architecture design for high-performance lithium metal anodes

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Carbon Energy 2024年第1期6卷 207-235页

作者： Na Zhu Yuxiang Yang Yu Li Ying Bai Junfeng Rong Chuan Wu SINOPEC Research Institute of Petroleum Processing Co. Ltd.BeijingChina Beijing Key Laboratory of Environmental Science and Engineering School of Materials Science and EngineeringBeijing Institute of TechnologyBeijingChina Yangtze Delta Region Academy of Beijing Institute of Technology JiaxingChina

Metallic lithium(Li)is considered the“Holy Grail”anode material for the nextgeneration of Li batteries with high energy density owing to the extraordinary theoretical specific capacity and the lowest negative electrochemical ***,owing to inhomogeneous Li-ion flux,Li anodes undergo uncontrollable Li deposition,leading to limited power output and practical *** materials and their composites with controllable structures and properties have received extensive attention to guide the homogeneous growth of Li to achieve high-performance Li *** this review,the correlation between the behavior of Li anode and the properties of carbon materials is ***,we review emerging strategies for rationally designing high-performance Li anodes with carbon materials,including interface engineering(stabilizing solid electrolyte interphase layer and other functionalized interfacial layer)and architecture design of host carbon(constructing three-dimension structure,preparing hollow structure,introducing lithiophilic sites,optimizing geometric effects,and compositing with Li).Based on the insights,some prospects on critical challenges and possible future research directions in this field are *** is anticipated that further innovative works on the fundamental chemistry and theoretical research of Li anodes are needed.

关键词： carbon materials dendrites hosts interfacial layers Li metal anodes

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Porous nanofibrous dressing enables mesenchymal stem cell spheroid formation and delivery to promote diabetic wound healing

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Chinese Journal of Chemical engineering 2024年第4期68卷 156-164页

作者： Kexin Zhang Wenmin Zhang Heng An Zhe Huang Yanzhen Wen Xiangyu Jiao Yongqiang Wen College of Chemical Engineering and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Beijing Key Laboratory for Bioengineering and Sensing Technology Daxing Research InstituteSchool of Chemistry&Biological EngineeringUniversity of Science&Technology BeijingBeijing 100083China

Delayed and nonhealing of diabetic wounds imposes substantial economic burdens and physical pain on *** stem cells(MSCs)promote diabetic wound *** when MSCs aggregate into multicellular spheroids,their therapeutic effect is ***,traditional culture platforms are inadequate for the efficient preparation and delivery of MSC spheroids,resulting in inefficiencies and inconveniences in MSC spheroid *** this study,a three-dimensional porous nanofibrous dressing(NFD)is prepared using a combination of electrospinning and homogeneous *** thermal crosslinking,the NFD not only achieves satisfactory elasticity but also maintains notable *** the design of its structure and chemical composition,the NFD allows MSCs to spontaneously form MSC spheroids with controllable sizes,serving as MSC spheroid delivery systems for diabetic wound *** importantly,MSC spheroids cultured on the NFD exhibit improved secretion of vascular endothelial growth factor,basic fibroblast growth factor,and hepatocyte growth factor,thereby accelerating diabetic wound *** NFD provides a competitive strategy for MSC spheroid formation and delivery to promote diabetic wound healing.

关键词： Electrospinning Homogenization Biomedical engineering Nanomaterials Stem cell spheroids Diabetic wound healing

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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