Gyanendra Kumar Verma

National Institute of Technology Raipur | NIT Raipur · Department of Information Technology

The detection of emotions using automatic electroencephalogram (EEG) analysis is a significant challenge within human-computer interaction. This study aims to present a hybrid model that considers the frequency and time characteristics of multimodal EEG data to facilitate emotion recognition. Most conventional methods for identifying emotions rely...

This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2023, held in Hamirpur, India, during December 21–22, 2023. The 29 full papers included in this book were carefully reviewed and selected from 173 submissions. They were organized in top...

The generation and classification of synthetic images is a challenging and important task in the digital age. Generative Adversarial Networks are powerful tools for creating high-quality synthetic images, but they face limitations in terms of complexity, scalability, and efficiency. Quantum computing offers a promising alternative to enhance the pe...

Despite the developments in deep learning, extracting different features from brain signals remains a crucial challenge in EEG-based emotion recognition. This study introduces a novel methodology to overcome the challenge of capturing temporal and channel-related features. The basic idea of the proposed method is to use scalograms to capture subtle...

Affect analysis has been dominated by two seemingly opposing theories: the categorical approach, which is based on six universal discrete emotions. The dimensional approach is based on 2D/3D valence, arousal, and dominance. This paper proposes a hybrid emotion recognition approach using ResNet152 and eXtreme Gradient Boosting on the DEAP dataset. F...

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science acade...

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science acade...

Recognizing emotions is crucial for the development of artificial intelligence in various fields. This study explores the application of quantum support vector machines (SVMs) on emotion recognition from electroencephalogram (EEG) signals and compares its performance to traditional SVMs. SVMs are a popular machine-learning algorithm for this task d...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimo...

As digitization is increasing, threats to our data are also increasing at a faster pace. Generating fake videos does not require any particular type of knowledge, hardware, memory, or any computational device; however, its detection is challenging. Several methods in the past have solved the issue, but computation costs are still high and a highly...

Emotions are distinct reactions to internal or external events with implications for the organism. Automatic emotion recognition is a demanding task for pattern recognition and a required information retrieval method for diagnosing the condition of emotions in the peripheral nervous system and psychotherapy. In recent years, scientists have extensi...

Object detection has gained remarkable interest in the research area of computer vision applications. This paper presents an efficient method to detect multiple objects and it contains two parts: 1) training phase; 2) testing phase. During the training phase, firstly we have exploited two convolutional neural network models namely Inception-ResNet-...

Emotional Intelligence provides an impetus for simulating human emotions in systems to make emotionally-sensitive machines. Integrating emotion-based theories and principles maturing with research in affective computing, we propose a novel statistical approach that can evaluate the correlation between different emotional states. It provides a way s...

In the last few years, terror activities across the world have been raised drastically. Therefore, we need a framework that can detect these terror and illegal actions automatically. In spite of various state-of-the-art deep learning algorithms, weapon detection is still a serious challenge. This work focuses on the classification and detection of...

A novel method of feature extraction, based on feature fusion, is proposed in this paper. In object classification and detection models, more advantages of transfer learning can be easily extracted from feature fusion. In the proposed scheme, Firstly, features are extracted by using two deep learning models: DenseNet201 and ResNet101 and then a nei...

In the age of information explosion, image recognition and classification is a great methodology for dealing with and coordinating a huge amount of image data. Here, we present a deep learning–based method for the classification of images. Although earlier deep convolutional neural network models like VGG-19, ResNet, and Inception Net can extricate...

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of t...

Recently, researchers focused their attention towards recognition of micro-expression due to real-time application of micro-expression recognition in human behavior understanding, as it indicates whether a person is knowingly or unknowingly manipulating their exact emotion and mental state. Recognition of micro-expression is a challenging task due...

Object recognition is a computer vision technique for identifying objects inan image. Deep neural networks have demonstrated remarkable recognitionresults on the basis of the features extracted from a single image of the object. In this paper, we present a feature fusion-based deep learning method forclassifying and recognizing the multi-class obje...

Image compression play significant role in the data transfer and storage. Recently, deep learning has achieved tremendous success in various domain of image processing. In this paper, we propose a multi-structure Feature map-based Deep Learning approach with K-means Clustering for image compression. We first use a modified CNN to select a multi-str...

The conventional emotion recognition methods are mostly based on the frequency characteristics of electroencephalograph (EEG) signals. However, spatial features are likewise valuable as it contains latent information related to emotional states. In this paper, a wavelet-based Deep Learning framework proposed by considering both frequency and spatia...

This two-volume set (CCIS 1240-1241) constitutes the refereed proceedings of the Second International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2020, held in Silchar, India. Due to the COVID-19 pandemic the conference has been postponed to July 2020. The 79 full papers and 4 short papers were thoro...

This two-volume set (CCIS 1240-1241) constitutes the refereed proceedings of the Second International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2020, held in Silchar, India. Due to the COVID-19 pandemic the conference has been postponed to July 2020. The 79 full papers and 4 short papers were thoro...

Deep learning has developed as an effective machine learning method that takes in numerous layers of features or representation of the data and provides state-of-the-art results. The application of deep learning has shown impressive performance in various application areas, particularly in image classification, segmentation and object detection. Re...

The deep convolutional neural network (CNN) has been successfully used to obtain high-level representation in various applications of computer vision problems. However, in the field of medical imaging there are not sufficient images available to train a deep CNN. Therefore, we have used a pre-trained deep CNN model for classification of cervical ca...

Wild animal detection is a dynamic research field since last decades. The videos acquired from camera-trap comprises of scenes that are cluttered that poses a challenge for detection of the wild animal. In this paper, we proposed a deep learning based system to detect wild animal from highly cluttered natural forest images. We have utilized Deep Re...

Monitoring wild animals became easy due to camera trap network, a technique to explore wildlife using automatically triggered camera on the presence of wild animal and yields a large volume of multimedia data. Wild animal detection is a dynamic research field since the last several decades. In this paper, we propose a wild animal detection system t...

Securing Big Data has become one of the major issues of the exponentially pacing computing world, where data analysis plays an integral role, as it helps data analysts to figure out the interests and detailed information of organizational and industrial assets. Acts like cyber espionage and data theft lead to the inappropriate use of data. In order...

The Department of Computer Engineering, National Institute of Technology (NIT) Kurukshetra is pleased to announce 1st International Conference on Machine Learning, Image Processing, Network Security and Data sciences (MIND-2019) to be organized on 3 - 4 March 2019 at NIT Kurukshetra, India.

The Department of Computer Engineering, National Institute of Technology (NIT) Kurukshetra is pleased to announce 1st International Conference on Machine Learning, Image Processing, Network Security and Data sciences (MIND-2019) to be organized on 3 - 4 March 2019 at NIT Kurukshetra, India.

In this paper, we propose a Network Intrusion Detection System (NIDS) model based on Random Forests (RF) classifier for anomaly detection of the collected network traffic.

In this paper, we propound a Network Intrusion Detection System (NIDS) model based on Gradient Boosted Trees (GBT) classifier for network traffic analysis.

Wildlife monitoring and analysis are an active research field since last many decades. In this paper, we focus on wildlife monitoring and analysis through animal detection from natural scenes acquired by camera-trap networks. The image sequences obtained from camera-trap consist of highly cluttered images that hinder the detection of animal resulti...

Today's, most of the criminal activities are taken place using handheld arms particularly gun, pistol and revolver. Several surveys revealed that hand held gun is the foremost weapon used for diverse crimes like burglary, rape, etc. Therefore, automatic gun detection is a prime requirement in current scenario and this paper presents automatic gun d...

Wild animal detection is an active research area since last many decades among wildlife researchers to study and analyze wild animals and their behavior. This paper presents sparse representation based wild animal detection system using Discriminative Feature-oriented Dictionary Learning (DFDL). DFDL extracts discriminative class-specific features...

Currently, the research on human affect recognition has shifted from six basic emotions to complex affect recognition in continuous two or three dimensional space due to the following challenges: (i) representing and analyzing large number of emotions in one framework, (ii) representing complex emotions in the framework, and (iii) validating the fr...

This paper presents a robust and efficient automatic visual surveillance system to detect presence of human being in restricted zones that are off limit i.e. military installations, secure depository, border areas etc. The system is based on multi-algorithms namely HAAR wavelet (HAAR), Local Binary Pattern (LBP) and Histogram of Oriented Gradients...

Today's automatic visual surveillance is prime need for security and this paper presents first step in the direction of automatic visual gun detection. The objective of our paper is to develop a framework for visual gun detection for automatic surveillance. The proposed framework exploits the color based segmentation to eliminate unrelated object f...

In this modern age the advancement in ubiquitous computing has made the use of natural user interface very much required. The presence of computers and making use of the facilities of human computer interaction in our societies will obviously bring and mark a positive impact on our societies. Either it was the day when the technologies had not been...

Today's automatic visual surveillance is prime need for security and this paper presents first step in the direction of automatic visual gun detection. The objective of our paper is to develop a framework for visual gun detection for automatic surveillance. The proposed framework exploits the color based segmentation to eliminate unrelated object f...

The work being presented here portrays a comparative facial expression analysis using texture and multiresolution analysis for automatic emotion recognition. The expression of the emotion on facial image manifests in variations in spatial arrangement and intensity of the pixels. The emotions can be recognized by taking note of these variations corr...

This paper proposes a novel approach for feature extraction based on the segmentation and morphological alteration of handwritten multi-lingual characters. We explore multi-resolution and multi-directional transforms such as wavelet, curvelet and ridgelet transform to extract classifying features of handwritten multi-lingual images. Evaluating the...

The work being presented here portrays a novel approach for emotion recognition based on the texture analysis of facial images. The expression of the emotion on facial image manifests in variations in spatial arrangement and intensity of the pixels. The emotions can be recognized by taking note of these variations corresponding to the features bein...

In this paper, we have proposed speech emotion recognition system based on multi-algorithm fusion. Mel Frequency Cepstral Coefficients (MFCC) and Discrete Wavelet Transform (DWT), the two prominent algorithms for speech analysis, have been used to extract emotion information from speech signal. MFCC, a representation of the short-term power spectru...

In this paper, we have investigated the performance of different multi-resolution transforms in the application of emotion recognition from facial images. Multi-resolution analysis of image provides frequency information along with time information in different scale, orientation and locations. The emotion information from facial images was being c...

An intra-modal fusion, a fusion of different features of the same modal is proposed for speaker identification system. Two fusion methods at feature level and at decision level for multiple features are proposed in this study. We used multiple features from MFCC and wavelet transform of speech signal. Wavelet transform based features capture freque...

In this paper, we perform the face recognition using curvelet transform. In literature, multi resolution analyses of image through wavelet and Gabor transform have been quite exploited successfully for pattern recognition and so far, for face recognition. In contrast to wavelet transform, curvelet transform very efficiently approximate the curved e...

In this paper, we proposed a new approach for Hindi character recognition using digital curvelet transform. Curvelet transform well approximate the curved singularities of images therefore very useful for feature extraction to character images. A Devanagari script contains more than 49 characters (13 vowels and 33 consonants) and all the characters...

The work being presented here describes a novel approach for human emotion recognition based on curvelet transform. Our approach of the emotion recognition is motivated by the fact that emotions expressed more obviously by the facial curves, hence the technique proficient in capturing the edge singularity as well as the curve singularities i.e. cur...

This paper present an overview of multimodal information fusion strategies such as early, intermediate and late fusion as reported in literature. We also made an experimental evaluation for one of them for multimodal emotion recognition system. Further we propose a fusion scheme based on speech and facial expression for multimodal emotion recogniti...

Emotion detection from speech has been realized to provide benefits for more natural human-machine interaction. To detect the emotion from speech signal, an abundantly long continuous speech segment is needed. This paper proposed a navel approach for emotion detection based on relative amplitude of speech signal. Relative amplitude reduces bias of...

The purpose of this paper is to evolve a robust text independent speaker identification system based on the wavelet transform, which is able to analyze signal at multiple resolutions. The proposed system identifies speakers by their acoustic characteristics embedded in speech signal of speakers. Features are obtained from approximation and detail c...

RFID, Radio Frequency Identification is an inexpensivetechnology, can be implemented for several applications such assecurity, asset tracking, people tracking, inventory detection,access control applications. The main objective of this paper is todesign and implement a digital security system which can deployin secured zone where only authentic per...