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VireNet-SSD: object detection model for resource-constrained applications based on self-organized operational neural networks

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Neural Computing and Applications 2025年第14期37卷 8547-8569页

作者： Kamath, Vidya Renuka, A. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Discovering deep learning-based computer vision solutions for use with constrained devices is exceptionally hard, and the trade-offs are often too undermining. Deep learning models are enormous, which makes it challenging to deploy them on constrained platforms. The convolutional neural network is the fundamental framework for majority of the models that are currently in use. However, operational neural networks have recently shown to be a better option to the convolutional equivalents on a variety of tasks due to their heterogeneous nature and greater resemblance to the functioning of biological neurons. The question of whether heterogeneous models could function on constrained devices and be deployed in real time remains a major concern. To address this problem, an object detection model architecture based on a single-shot multi-box detector with self-organized operational neural networks as its backbone was developed, which can perform efficiently on constrained devices such as Raspberry Pi. The resultant backbone architecture was named as VireNet. In contrast to homogeneous conventional deep learning networks that use convolutions, heterogeneous networks were chosen to develop VireNet, which provides a more productive and effective solution. Furthermore, an in-depth explanation of the design space has been provided to aid any future research that is associated with this architectural search. This new approach might mark the very beginning of the use of heterogeneity to address issues on devices with constrained resources. © The Author(s) 2025.

关键词： Convolutional neural networks

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Challenge Response Protocol using Multivariate Polynomial Cryptography

Challenge Response Protocol using Multivariate Polynomial Cr...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jaiswal, Chandra Keshwar Sharma, Arpit Chaturvedi, Atul Allenhouse Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331529833

The objective of this paper is to define the meaning of a multivariate public key using two or more multivariate encrypted parties A and B which targets to improve the existing multivariate public key encryption. Two polynomials are created by a central authority for the protocol's setup phase. The client generates a public key using a secret key, then uses his secret key to respond to a challenge the server issues and submits it to the server. The response is hashed to ensure its security. Any design, including ones with physically unclonable functions, can use this multivariate polynomial cryptography scheme since it is appropriate for post-quantum computing and quantum resistant encryption. This protocol, which protects the CCA technique of post-quantum communications, validated the promise of multivariate polynomials for information security. The work makes an effort to portray polynomial implementation and polynomial contribution as security-sensitive jobs. © 2025 IEEE.

关键词： Quantum computers

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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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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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Advancements in Isogeny-Based Cryptography: A Mathematical Approach to Post-Quantum Security

Advancements in Isogeny-Based Cryptography: A Mathematical A...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Soni, Abhay Singh, Sunidhi Chaturvedi, Atul Allenhouse Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331529833

In this paper, we have discussed isogeny-based cryptography as a rather appealing option for post-quantum security. We provide analysis of current protocols like SIDH (Supersingular Isogeny Diffie-Hellman) and SIKE (Supersingular Isogeny Key Encapsulation) setting up fundamental issues with their security and performance. In this work we introduce a novel isogeny pathfinding algorithm that employs graph theory as well as probabilistic models in order to lower its computational complexity. The results show significant improvement in both efficiency and security, strengthening the case for the use of isogeny based systems in a quantum-resilient cryptographic space. © 2025 IEEE.

关键词： Encryption algorithms

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer science 2024年第4期5卷 1-9页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges

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Recent Advances in computer science and Communications 2023年第2期16卷 61-80页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Enhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes generate more complex, unstable, and high-dimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of Non-Stationary Time Series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to Non-Stationary Time Series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationary time series into groups and subgroups based on a change in statistical behavior. It also provides a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it is observed that this research sphere still has many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research. © 2023 Bentham science Publishers.

关键词： Time series

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Comprehensive insights into healthcare IoT: the role of machine learning and deep learning approaches

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Multimedia Tools and Applications 2025年 1-42页

作者： Kumar, Atul Dewang, Rupesh Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Conventional healthcare systems have long struggled to address the varied needs of large patient populations, often leading to inefficiencies and less-than-optimal outcomes. Yet, the advent of machine learning (ML) and deep learning (DL) models has heralded a transformative shift toward value-based care, enabling healthcare providers to offer personalized and remarkably effective treatments. Contemporary medical equipment and devices are equipped with internal applications that gather and store extensive patient data, forming a valuable resource for ML-powered predictive *** have conducted a systematic literature review (SLR) which mainly focuses on Theoretical background, challenges, Application framework, Organization of IoT in healthcare and HIoT *** the SLR process some efficient methodologies has been used. In the SLR process we have conducted Quality metadata analysis (QMA) which provides the complete statistical analysis by considering different *** learning algorithms enable the analysis of the extensive data produced by IoT devices, uncovering patterns and irregularities that may escape human detection. Deep learning, leveraging sophisticated neural network models, enhances the accuracy of medical image interpretation, disease prognosis, and forecasting patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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