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Fast face detection using a unified architecture for unconstrained and infrared face images

Cognitive Systems Research 2022年 74卷 18-38页

作者： Dash, Priyabrata Ranjan Kisku, Dakshina Gupta, Phalguni Kanta Sing, Jamuna Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur713209 India Department of Computer Science and Engineering Institute of Engineering & Technology GLA University Uttar Pradesh Mathura281406 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random backgrounds and infrared (IR) environment. This paper proposes a novel face detection method which can address some of these issues and challenges quite successfully during face detection in unconstrained as well as infrared environments. It makes use of Fast Successive Mean Quantization Transform (FastSMQT) features for image enhancement and feature representation to deal with illumination and sensor insensitiveness. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Finally, the features and classifiers are combined together with skin detection algorithm for face detection in crowd image and head orientation correction of near infrared faces. The proposed face detector is robust in handling pose, occlusion, illumination, blur and low image resolution. The experiment is performed on six challenging and publicly available databases, viz. BIOID, LFW, FDDB, UFI, WIDER FACE and IIT Delhi near infrared. The experimental results depict that the proposed method outperforms traditional as well as some advanced methods in detecting unconstrained and infrared faces under challenging situations. © 2022 Elsevier B.V.

关键词： Face recognition

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Smart Farming: An Integrated Platform for Crop and Fertilizer Prediction using Machine Learning and Orange Data Mining

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SN computer science 2025年第5期6卷 1-16页

作者： Abrol, Vipasha Gupta, Gaurav Pradhan, Soumya Ranjan GL Bajaj Institute of Technology and Management Noida India Department of Computer Science and Engineering Chandigarh University Chandigarh India

The purpose of this study is to examine the possible advantages of using machine learning algorithms into contemporary agriculture. These algorithms’ primary goal is to minimise waste and maximise agricultural yield by assisting in the making of well-informed decisions about crop planting, irrigation, and harvesting. The paper highlights the use of various supervised learning algorithms to address key challenges in agricultural management. Random Forest is employed for crop recommendation, while a combination of SVM and Random Forest tackles fertilizer recommendation. Our supervised learning models achieved high accuracy: Random Forest for crop recommendation (98.90%), SVM and Random Forest for fertilizer recommendation (97.89%).Top performers were SVM and Gradient Boosting, both achieving a result of 0.995 across all metrics through Orange Data Mining Tool. This paper contributes to the growing body of research exploring the applications of machine learning in agriculture. Specifically, it examines the effectiveness of different algorithms in tackling pest management challenges for various crops, offering a pathway towards cost-effective modernization of farming practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Crop management Gradient boosting Machine learning Orange SVM

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Coevolution with Bee Colony: Paradigm Shift in Many Objective Optimization

Coevolution with Bee Colony: Paradigm Shift in Many Objectiv...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khoirom, Selina Pramanik, Shreya Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in parallel using artificial bee colony (ABC) algorithm. After achieving convergence, a group of good-quality solutions is carefully chosen from each ABC population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership-induced rank measure is developed to discover the top-ranked equally good members of the union set, showing the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy-bee colony (FBC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FBC outperforms its contenders with respect to the performance metrics. © 2024 IEEE.

关键词： Optimization algorithms

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Multi-Convolutional Neural Network Based Crowd Counting Using VGG-19 Architecture

Multi-Convolutional Neural Network Based Crowd Counting Usin...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： John Pradeep, J. Raja Arumugham, M. Santhi, S. Kalaiselvi, S. Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9789819746491

Crowd counting has raised a major issue in computer vision with its extensive range of applications, like crowd management, public protection, and city development. The proposed model involves developing a method that can precisely determine the total number of crowds from a raw photograph captured from a random viewpoint and crowd density. A density map-based estimation that uses Multi-convolution Neural Network that is built on the VGG-19 architecture is developed that are trained to categorize the visual inputs with varying densities. The deeper architecture allows VGG-19 to learn more complex features from the input image, which is especially beneficial in crowd counting tasks where the input image contains a huge number of objects. The proposed crowd counting technique combines the VGG-19 architecture with the MCNN model to achieve accurate results. By using filters of different sizes, the MCNN model can handle input images of varying sizes or resolutions, while also accounting for variations in individuals or head sizes caused by viewpoint effects. This allows to accurately estimate crowd density without needing to identify the perspective map of the input image, making it highly versatile and operative for crowd counting tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Performance Impact of Architectural Parameters on Chiplet-Based IMC Accelerators 20

Performance Impact of Architectural Parameters on Chiplet-Ba...

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20th IEEE India Council International Conference, INDICON 2023

作者： Banerjee, Abir Puri, Amit Varshney, Kushagra Jose, John Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Z H College of Engineering and Technology Amu Department of Computer Engineering India

ISBN: (纸本)9798350305593

The various forms of Deep Neural Networks, such as CNN, RNN, and GNN find applications in many modern devices. The two primary operations, multiply and accumulate, dominate calculations in Neural Network applications. Existing computation engines like CPUs and GPUs fail to yield the desired performance and energy efficiency for these AI workloads. The opportunity arises for domain-specific architectures, and a new computation class called Neural Network Accelerators emerges. Processing-In-Memory is an in-situ analog computation architecture that accelerates MAC operations by evading the memory wall. However, the fabrication process limits the size of the PIM circuits. Therefore, large monolithic ICs become commercially impractical. The solution comes in the form of 2.5 D integration or Chiplet-based *** this paper, we study a Chiplet-based PIM accelerator and identify several of its architectural parameters that impact the accelerator's area, energy consumption, and running time of different networks on the device. We varied the parameters and compared the effects on the accelerator deploying various DNN models. We used a simulator that models Chiplet-based in-memory-computing accelerators for conducting the study. © 2023 IEEE.

关键词： Chiplet Architecture Domain Specific Architecutre Neural Network Accelerator Processing-In-Memory

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Contents-Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTM

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SN computer science 2023年第4期4卷 380页

作者： Ghanem, Razan Erbay, Hasan Bakour, Khaled Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering University of Turkish Aeronautical Association Ankara Turkey

The use of social networks has become an integral part of our daily lives. Even though social networking sites offer many advantages, they also pose a number of problems for their users. One of the most famous problems is unwanted messages. It is not desirable for social network users to be bothered by annoying and time-wasting messages. These unwanted messages, which include ads, malicious content, and any low-quality content, are called spam. It is challenging to combat spam on social networks, because messages exchanged through social media are short, sparse, and may contain grammatical and spelling errors in addition to complex characters and special patterns. The main task to solve such a problem depends essentially on an appropriate representation of the text to increase the efficiency of the classifier. Therefore, in this study, we introduce a RoBERTa-based bi-directional Recurrent Neural Network model for spam detection on social networks. The RoBERTa model is used to learn contextualized word representations to improve the performance of the stacked BLSTM network. Moreover, a comparative study, in which we apply the most common transformer-based models, has been conducted as well to solve the spam problem. The experimental results on three benchmark data set state that our RoBERTa–BLSTM model outperforms all common models used to detect spam on social networks with an accuracy of 98.15%, 94.41%, and 99.74% on Twitter, YouTube, and SMS data sets, respectively. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BERT RoBERTa Spam detection Stacked BLSTM Transformer Word embedding

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AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

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Deep Learning Tools for Covid-19 Pneumonia Classification

Deep Learning Tools for Covid-19 Pneumonia Classification

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Kanyangarara, Ngonidzashe Mathew Soumya, D.R. Sahana, Subrata Das, Sanjoy Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science Indira Gandhi National Tribal University RCM Manipur India

ISBN: (纸本)9789819986606

The outbreak of Covid-19 has triggered a worldwide problem, especially in Asia and America. The World Health Organization (WHO) declared the sickness a pandemic on March 20, 2020. It arrived in waves, and most countries worldwide have now experienced two waves and are on the approach of experiencing the third. The goal of this study is to build up and certify a computer-Aided Diagnosis (CADx) system for distinguishing between COVID-19-positive patients and non-COVID Patients people. Chest X-ray (CXR) images will be used to accomplish this. From public datasets which we got on GitHub 2295 CXR images were obtained which included 712 COVID-19 positive and 1583 normal. The proposed CADx system utilized a Conventional Neural Network (CNN) model for data argumentation and CNN was built, compiled, and trained with the help of TensorFlow and Keras. For the sake of appraisal, our datasets were separated into three categories: Train/Test and Validation. The three sets’ accuracy was evaluated and the results for Training, Validation, and Test were 97.77%, 97.81%, and 97.72% respectively. In the end, this study was able to create a precise computer-Aided Diagnosis system for the two categories of classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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