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

作者： Rathi, Snehal Khedkar, Vijayshri Jadhav, Rutuja Kamate, Mugdha Potdar, Apurva Kamble, Prachi Computer Department Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331522667

Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government initiatives. Because of many farmers are not aware about all these things, to address these concerns, the proposed "Agricultural Financial Inclusion System" provides a comprehensive platform which integrates essential services with aim of improving farmers’ production and their financial awareness. Our system has a crop recommendation feature that helps farmers to choose the best suitable crop for their soil in the farm by taking climate and soil type into account. In order to make sure that farmers are aware about various available services/resources or any government schemes and able to take advantage of them, the platform also offers essential information on a variety of government initiatives, saving plans etc. By combining all these features, the Agricultural Financial Inclusion System encourage farmers to make smart choices which help them to enhance agricultural productivity and financial stability. In order to create more successful and sustainable agriculture sector, this integrated approach hopes to fill the gap between farmers and technology & resources they need. © 2025 IEEE.

关键词： Smart agriculture

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A lossless image encryption technique using chaotic map and DNA encoding

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Multimedia Tools and Applications 2025年 1-23页

作者： Yadav, Anju Jaipuriyar, Ayush Roy, Satyabrata Rawat, Umashankar Department of Information Technology Manipal University Jaipur Rajasthan Jaipur300307 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur300307 India

Image encryption is required to protect digital images from unauthorized access and tampering. With the increasing use of digital images in various fields such as medical imaging, military imaging, and e-commerce, it has become essential to secure the transmission and storage of these images. In this paper, we propose a new image encryption technique that uses a combination of Deoxyribo Nucleic Acid (DNA) encoding and chaotic maps. The proposed technique aims to provide a high level of security for digital images while also maintaining low computational complexity. The encryption process involves encoding the image data using DNA sequences and then performing a chaotic map transformation on the encoded data. The resulting cipher image is highly resistant to attacks such as brute-force and statistical analyses, making it suitable for secure transmission and storage. We evaluate the effectiveness of our proposed technique by comparing it with other popular encryption methods using various performance metrics. The results show that our approach outperforms other methods in terms of security and computational efficiency. For instance, we achieved Number of Pixel Change Rate (NPCR) of 99%, correlation coefficient in order of 10-3 and Unified Average Changing Intensity (UACI) of 33%. Additionally, noise detected was 0%, proving it a lossless scheme. Thus, the proposed image encryption technique has the potential to be used in various applications that require secure image transmission and storage, such as medical imaging, IoT applications etc. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Digital image storage

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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Elevating Image Steganography:A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

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computers, Materials & Continua 2024年第5期79卷 2923-2943页

作者： Muhammad Zaman Ali Omer Riaz Hafiz Muhammad Hasnain Waqas Sharif Tenvir Ali Gyu Sang Choi Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongbuk38541Korea

In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

关键词： Steganography most significant bit(MSB) least significant bit(LSB) peak signal-to-noise ratio(PSNR)

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Big Data Analytics Using Graph Signal Processing

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computers, Materials & Continua 2023年第1期74卷 489-502页

作者： Farhan Amin Omar M.Barukab Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Computing and Information Technology-Rabigh King Abdulaziz UniversityJeddahSaudi Arabia

The networks are fundamental to our modern world and they appear throughout science and *** to a massive amount of data presents a unique opportunity to the researcher’s *** networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep ***,this paper initiates a discussion on graph signal processing for large-scale data *** first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,*** filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of *** then consider a graph filter as a model that helps to extend the application of GSP methods to large *** show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the *** several rounds of simulation *** see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based *** using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

关键词： Big data data science big data processing graph signal processing social networks

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HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13869-13885页

作者： Mathur, Garima Pandey, Anjana Goyal, Sachin Department of Computer Science and Engineering UIT RGPV Bhopal India Department of Information Technology UIT RGPV Bhopal India

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases;these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers. © 2022

关键词： Support vector machines

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Enhancement of Signals Characteristics with Least Effect of Optical communication Losses for Dense Optical communication Systems

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Journal of Optical communications 2024年第1期45卷 113-117页

作者： Kumar, Chakresh Kumar, Ghanendra University School of Information Communication & Technology Guru Gobind Singh Indraprastha University New Delhi110078 India Department of Electronics and Communication Engineering National Institute of Technology Delhi110040 India

In this paper, the major aim is to enhance the bandwidth and therefore increase the capacity of the optical fibre communication system by reducing the dispersion in the fibre. Dispersion compensation is necessary to reduce losses and cost of the system. In this paper, we are basically focusing on reducing or decreasing chromatic dispersion. Chromatic dispersion is a type of dispersion in which pulse broadening occurs due to delay in different spectral components. We here make use of dispersion compensated fibre (DCF) for reducing the dispersion in the WDM system. Here, we basically use three dispersion compensated techniques (pre-compensation, post-compensation and mix compensation). The comparison in the three models of dispersion compensated technique is made in terms of transmission power vs. Q factor and also transmission power vs. Bit Error Rate. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Chromatic dispersion

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Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 168-181页

作者： Vani, B.V. Guruprakash, C.D. Information Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India Department Computer Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India

Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (deep CNN) is used to predict the crop disease for which the dominant statistical and texture features are utilised and LSOA as a training algorithm. The experiments were done on an apple data set and a corn data set, and the results show that the LSOA-deep CNN model attains 98.474% of accuracy, 92.837% of sensitivity and 99.00% of specificity in k-fold training data and 94.683% of accuracy, 95.489% of specificity and 99.00% of specificity with 80% training data for the corn data set. With the apple data set, the developed method achieves 94.587% of accuracy, 99.00% sensitivity and 99.00% specificity under k-fold training, while for the 80% of training, 97.959% accuracy, 96.233% sensitivity and 99.005% specificity are attained. © 2024 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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