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The problems of underexposure, under-detail-enhancement and residual haze are detected in the previous dehazing techniques. These issues occur due to different reasons and are highly difficult to be resolved using a single algorithm. Therefore, a three-stage dehazing model (TSDM) is proposed in this paper using pre-processing, dehazing and post-processing modules. The improved auto-color transfer (IACT) approach is presented as part of pre-processing to efficiently enhance the hazy image to overcome underexposure. Also, adaptive dehazing (AD) is developed in this work which considers the global characteristics of the hazy image as a parameter, to adaptively enhance the details. Moreover, adaptive contrast enhancement (ACE) is proposed as a post-processing operation that adaptively fuses the dehazed image and its contrast-enhanced image to effectively improve the contrast. However, the IACT operation is performed on the hazy image only when dark regions are detected. Similarly, the ACE is performed only when a dehazed image exhibits residual haze. Based on these prior conditions, the proposed work can be implemented in four distinct ways i.e. using only the AD technique; using IACT and AD approaches, using AD and ACE methods, and using all IACT, AD and ACE algorithms. The proposed TSDM is experimentally analyzed using many databases which shows improved results compared to previous techniques.

Photoacoustic computed tomography (PACT) is an innovative biomedical imaging technique that has gained significant application in the field of biomedicine due to its ability to visualize optical contrast with high resolution and deep tissue penetration. However, the inherent challenges associated with photoacoustic signal excitation, propagation and detection often result in suboptimal image quality. To overcome these limitations, researchers have developed various advanced algorithms that span the entire image reconstruction pipeline. This review paper aims to present a detailed analysis of the latest advancements in PACT algorithms and synthesize these algorithms into a coherent framework. We provide tripartite analysis — from signal processing to reconstruction solution to image processing, covering a spectrum of techniques. The principles and methodologies, as well as their applicability and limitations, are thoroughly discussed. The primary objective of this study is to provide a thorough review of advanced algorithms applicable to PACT, offering both theoretical foundations and practical guidance for enhancing the imaging effect of PACT.

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.

Cursive handwriting recognition (CHWR) is an interesting area of research as it has a wide range of applications but lacks an accurate approach to provide better results due to its character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. A novel CHWR model is proposed to enhance the recognition accuracy with high global stability. The proposed model introduces three major phases: pre-processing, feature extraction and classification. In the pre-processing stage, the noise removal and binarization are adapted with the intrusion of improved adaptive wiener filtering (IAWF) and structural symmetric pixels. A hybrid deep direction distribution feature extraction (HDDDFE) approach is proposed to extract directional Local gradient histogram (LGH), column gradient histogram (CGH) features and a wavelet convolutional neural network with Block Attention Module (WCNN-BAM) is proposed to extract deep global features (GF), profile features (PF) and dynamic features (DF). A novel double hidden layer gated recurrent neural network with a feature attention mechanism (ODHL-GRNN-FAM) is proposed to offer handwritten classification results. The developed model is evaluated with the IAM database and attains an overall recognition accuracy of 98%, precision of 97%, f-measure of 97.99%, character error rate (CER) of 1.23%, word error rate (WER) of 4.8%, respectively.

Video forgery detection and localization is one of the most important issue due to the advanced editing software that provides strengthen to tools for manipulating the videos. Object based video tampering destroys the originality of the video. The main aim of the video forensic is to eradicate the forgeries from the original video that are useful in various applications. However, the research on detecting and localizing the object based video forgery with advanced techniques still remains the open and challenging issue. Many of the existing techniques have focused only on detecting the forged video under static background that cannot be applicable for detecting the forgery in tampered video. In addition to this, conventional techniques fail to extract the essential features in order to investigate the depth of the video forgery. Hence, this paper brings a novel technique for detecting and localizing the forged video with multiple features. The steps involved in this research are keyframe extraction, pre-processing, feature extraction and finally detection and localization of forged video. Initially, keyframe extraction uses the Gaussian mixture model (GMM) to extract frames from the forged videos. Then, the pre-processing stage is manipulated to convert the RGB frame into a grayscale image. Multi-features need to be extracted from the pre-processed frames to study the nature of the forged videos. In our proposed study, speeded up robust features (SURF), principal compound analysis histogram oriented gradients (PCA-HOG), model based fast digit feature (MBFDF), correlation of adjacent frames (CAF), the prediction residual gradient (PRG) and optical flow gradient (OFG) features are extracted. The dataset used for the proposed approach is collected from REWIND of about 40 forged and 40 authenticated videos. With the help of the DL approach, video forgery can be detected and localized. Thus, this research mainly focuses on detecting and localization of forged video based on the ResNet152V2 model hybrid with the bidirectional gated recurrent unit (Bi-GRU) to attain maximum accuracy and efficiency. The performance of this approach is finally compared with existing approaches in terms of accuracy, precision, F-measure, sensitivity, specificity, false-negative rate (FNR), false discovery rate (FDR), false-positive rate (FPR), Mathew’s correlation coefficient (MCC) and negative predictive value (NPV). The proposed approach assures the performance of 96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC and 96% NPV, respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for the GMM model attained about 104 and 27.95, respectively.

Extensive use of digital multimedia has led to the development of advance video processing techniques for development of multimedia applications. Application such as video surveillance requires 247 recording and streaming. So, the bandwidth and storage costs become significant. With introduction of video streaming over internet, where different kinds of end users request same content with different available bandwidth, it requires scalable video coding (SVC). These challenges can be overcome by developing new techniques to reduce redundancy in subsequent frames and to improve the coding efficiency. In this paper, overlapping weighted linear sum (OWLS) pre-processing method and its hardware architecture are proposed. It is implemented using field progrmmable gate array (FPGA) and the application specific integrated circuit (ASIC) is also developed using TSMC180nm technology standard cell library. Results show improvement in terms of power and area as compared to the existing work. In motion compensated temporal filtering (MCTF), wavelet transform is implemented by temporal filters. Architecture for 5/3 Lifting MCTF is also implemented and compared with baseline H.264 video codec. Simulation results show that the average peak signal to noise ratio (PSNR) improvement is 2.36dB. The MCTF design using 5/3 Lifting filter is synthesized for Virtex-5 FPGA and compared with the existing close-loop architecture with better performance.

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

Social platforms have become one of the major sources of unstructured text. Investigating the unstructured text and interpreting the meaning is a complex job. Sentiment Analysis is an emerging approach as the social platforms have lot of opinionated data.¹ It uses language processing, classification of texts and linguistics to retrieve the opinions from the text. Twitter is a micro blogging site which is popular amongst the social users as it is a vast open data-platform and it witnesses lot of sentiments. Twitter Sentiment Analysis is a process of automatic mining of user tweets for opinions, emotions, attitude to derive useful insights into community opinions and classify the opinions as well. Due to the enormous increase in the number of collaborative tweets, it has become complex to identify the terms that carries sentiments. Also, the unstructured tweets may have non-relevant terms and reduce the classification accuracy. To address these issues, we propose a Social-Spider Lex Feature Ensemble Model-Based Syntactic-Senti Rule prediction Recurrent Neural Network Classifier (S²LFEM-S2RRNN) to obtain better classification accuracy. Twitter is used as source of data and we have extracted the tweets using Twitter API. Initially, data pre-processing is done to remove unwanted data, symbols and content terms are extracted to improvise the dataset. Then, the significant lexical content terms are extracted employing the proposed Social Spider Lex Feature Ensemble Model (S²LFEM) based on Syntactic-Senti Rule Prediction. The semantics⁴ of the terms are analysed on the verbs, subjectivity of the tweet patterns to count the overall weightage of tweets. Based on tweet weightage Recurrent Neural Network is trained to classify the tweets int to positive, negative and neutral. The experiment results show that the proposed classifier outperforms the existing models for sentiment classification in terms of accuracy with a performance score 94.1%.

The main objective of Project PalmPrints is to develop and demonstrate a special co-evolutionary genetic algorithm (GA) that optimizes (a clustering fitness function) with respect to three quantities, (a) the dimensions of the clustering space; (b) the number of clusters; and (c) and the locations of the various clusters. This genetic algorithm is applied to the specific practical problem of hand image clustering, with success. In addition to the above, this research effort makes the following contributions: (i) a CD database of (raw and processed) right-hand images; (ii) a number of novel features designed specifically for hand image classification; (iii) an extended fitness function, which is particularly suited to a dynamic (i.e. dimensionality varying) clustering space. Despite the complexity of the multi-optimizational task, the results of this study are clear. The GA succeeded in achieving a maximum fitness value of 99.1%; while reducing the number of dimensions (features) of the space by more than half (from 84 to 41).

A literature review on sarcasm detection has been undergone in this research work. To have a meaningful study about the existing works on sarcasm detection, a total of 65 research papers have been analyzed in diverse aspects like the datasets utilized, language, pre-processing technique, type of features, feature extraction technique, machine learning/deep learning-based sarcasm classification. All these papers belong to diverse international as well as national journals. Moreover, the performance of each work in terms of accuracy, F-score and recall will also be manifested. To show the superiority of the works, a comparative evaluation has been undergone in terms of analyzed performances of each of the works. Finally, the works that hold the superior or improved values are furnished. In addition, the current challenges faced by the sarcasm detection system are portrayed, and this will be a milestone for future researchers.

Our objective in this paper is to introduce the efficacies of texture in the interpretation of color skin images. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly; its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. Using the features of skin tumors, such as color, symmetry, and border regularity, an attempt is made to determinate if the skin tumor is a melanoma or a benign tumor. In this work, we are interested by adding to form parameters such as the asymmetry (A) and the shape irregularities of skin tumors (B), the textural parameters to estimate colors in dermatoscopic images. In this case, the images are analyzed using textural parameters computed in several directions. These parameters and the form parameters are added to obtain a better classification results. A statistical analysis is performed over these ratios to select the most highly discriminating textural parameters. The method has been tested successfully on 144 images and we found significant differences between the lesions (melanoma and benign). Finally, these parameters (form and parameters of texture selected) are only use to classify the benign and malignancy of the skin lesion. A multilayer neural network is employed to differentiate between malignant tumors and benign lesions.

It is very useful in the human computer interface to quickly and accurately recognize human hand movements in real time. In this paper, we aimed to robustly recognize hand gestures in real time using Convolutional Recurrent Neural Network (CRNN) with pre-processing and overlapping window. The CRNN is a deep learning model that combines Long Short-Term Memory (LSTM) for time-series information classification and Convolutional Neural Network (CNN) for feature extraction. The sensor for hand gesture detection uses Myo-armband, and six hand gestures are recognized and classified, including two grips, three hand signs, and one rest. As the essential pre-processing due to the characteristics of EMG data, the existing Short Time Fourier Transform (STFT), Continuous-time Wavelet Transform (CWT), and newly proposed Scale Average Wavelet Transform (SAWT) are used, and thus, the SAWT showed relatively high accuracy in the stationary environmental test. The CRNN with overlapping window has been proposed that can improve the degradation of real-time prediction accuracy, which is caused by inconsistent start time and hand motion speed when acquiring the EMG signal. In the stationary environmental test, the CRNN model with SAWT and overlapping window showed the highest accuracy of 92.5%. In the real-time environmental test, for all subjects learning, 80% accuracy and 0.99 s time delay were obtained on average, and for individual learning, 91.5% accuracy and 0.32 s time delay were obtained on average. As a result, in both stationary and real-time tests, the CRNN with SAWT and overlapping window showed better performance than the other methods.

Analyzing and gathering the people’s reactions on product trading, public services, etc. are crucial. Sentiment analysis (also termed as opinion mining) is a usual dialogue preparing act that plans on discovering the sentiments after opinions in texts on changing subjects. This research work adopts a novel sentiment analysis approach that comprises six phases like (i) Pre-processing, (ii) Keyword extraction and its sentiment categorization, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Accordingly, the Mongodb documented tweets initially underwent pre-processing with stop word removal, stemming, and blank space removal. Regarding the extracted keywords, the existing semantic words are derived after categorizing the sentiment of keywords. Additionally, the semantic similarity score is evaluated along with their keywords. The subsequent step is feature extraction, where the Holoentropy features such as cross Holoentropy and joint Holoentropy are formulated. Along with this, the extraction of weighted holoentropy features is the major work, where weight is multiplied with the holoentropy features. Moreover, in order to enhance the performance of classification results, the constant term utilized in evaluating the weight function is optimized. For this optimal tuning, a new, improved algorithm termed as Self Adaptive Moth Flame Optimization (SA-MFO) is introduced, which is the adaptive version of MFO algorithm. For classification, this paper aims to use the Deep Convolutional Neural network (DCNN), where the batch size is fine-tuned using the same SA-MFO algorithm. Finally, the performance of the proposed work is compared over other conventional models with respect to different performance measures.

Information Retrieval is the most predominant topic in the field of Information Systems as the generation of data over various systems and channels is growing every day. The proposed system which works on the offline basis is designed for the purpose of organizing data in a defined manner and for the purpose of increasing relevancy in the retrieval of the required information from this largely generated data. In the proposed system two novel algorithms, Dynamic Path Selection Clustering (DPSC) algorithm for clustering and the Rearward Binary Window Match (RBWM) algorithm for search process are introduced to overcome the difficulty in data organization and search. The evaluation of the entire system is done and the results are compared along with the results of the existing techniques.

A pre-processing design using neural networks is proposed for multiwavelet filters. Various numerical experiments are presented and a comparison is given between neural network pre-processing and a pre-processing for solving linear systems. Neural network pre-processing produces a good approximation for a large number of terms and converges rapidly.

There are many short-read aligners that can map short reads to a reference genome/sequence, and most of them can directly accept a FASTQ file as the input query file. However, the raw data usually need to be pre-processed. Few software programs specialize in pre-processing raw data generated by a variety of next-generation sequencing (NGS) technologies. Here, we present AUSPP, a Perl script-based pipeline for pre-processing and automatic mapping of NGS short reads. This pipeline encompasses quality control, adaptor trimming, collapsing of reads, structural RNA removal, length selection, read mapping, and normalized wiggle file creation. It facilitates the processing from raw data to genome mapping and is therefore a powerful tool for the steps before meta-analysis. Most importantly, since AUSPP has default processing pipeline settings for many types of NGS data, most of the time, users will simply need to provide the raw data and genome. AUSPP is portable and easy to install, and the source codes are freely available at https://github.com/highlei/AUSPP.

Sentiment analysis through opinion mining is determined through significant and growing interest for many industries including hotel, tourism, educations and so on. Sentiment analysis includes design of the system to search the user opinions in blog posts, comments, reviews or tweets regarding the product, policy or area. Many researchers carried out their research on opinion mining to identify the polarity of the statements. But the main problem during opinion mining is that the words chosen do not solve attribute relevancy and could not classify the positive and negative usage of uncertain terms. In order to address these problems, normal discriminant piecewise regressive (NDPR) sentiment classification technique is introduced. NDPR technique perform three processes, namely, pre-processing, feature extraction and classification to improve the accuracy level through forming classes (i.e., positive, neutral and negative) based on the extracted words from user review comments. Initially, NDPR technique performs the data pre-processing task for stemming and removing the stop words from review statements to reduce the file size that in turn improves the efficiency. After that, normal discriminant feature extraction process is carried out in NDPR technique to extract the opinion word from the review statements sent by reviewers. The related opinion words are systematized for their semantic equivalence of sentiment based on extracted word. This helps to reduce the time consumption to extract the opinions from reviewers. Finally, piecewise regressive sentiment classification (PRSC) process is carried out in NDPR technique to analyse the semantic opinion words for evaluating the sentiment class label. The sentiment class labels are categorized into positive, neutral and negative sentiments with the user review comments. This in turn helps to reduce the time consumption to extract the opinions from reviewers and to improve the review detection accuracy. The performance evaluation of NDPR technique is carried out with standard benchmark datasets of consumer product and services reviews extracted. The parameters used in evaluation are number of customer review words, accuracy, time complexity (TC) and false positive rate. Experimental analysis shows that NDPR technique reduces the time to extract the opinions from reviewers and false positive rate.

This work presents a novel multi-class detection approach for predicting car damage due to accidents or other reasons. The proposed prediction model intends to extract the class information to analyze the objects over the frames. Here, a novel masking-based deep convolutional neural network (MD-CNN) is proposed to capture the car parts’ regions and the classification process. The proposed model works well compared to the various existing approaches. The computation is initiated with the acquisition of damaged images, and pre-processing is performed for data labeling, which is further divided into training and testing samples. The features are internally analyzed by the proposed MD-CNN model, where the internal feature extraction process is a significant advantage with the classification process. The simulation is done in MATLAB 2020a environment where various performance metrics like accuracy, precision, $F$-measure, recall and detection rate are evaluated and compared with conventional learning approaches. The model gives an average precision of 94.5% and $F$1-score of 91% which is higher than other approaches. The proposed model establishes a better trade-off among the prevailing approaches with superior prediction accuracy.

The state of the environment and human behavior today contributes to a wide range of diseases that affect people. Medical professionals often find it challenging to detect disorders by themselves appropriately; it is crucial to recognize and anticipate them early on. This paper aims to detect and predict people with more widespread chronic illnesses. To prevent such diseases from worsening, this research proposes a new deep learning-based technique to predict chronic diseases. Initially, patient data will be collected using Internet of Things (IoT) devices. Then, the missing values from input data are eliminated, and categorical data encoding, outlier detection, and data transformation are performed in the pre-processing stage. After that, the necessary attributes are selected to optimize the performance by eliminating unnecessary features using Binary Grasshopper Whale Optimization Algorithm (BGWOA), which combines the benefits of the Binary Grasshopper Optimization Algorithm (BGOA) and Binary Whale Optimization Algorithm (BWOA) algorithms. Then, the disease can be classified as chronic or not, utilizing a three-layer stacked bidirectional long short-term memory (TLSBLSTM) technique. The performance is evaluated on two chronic disease datasets that are publicly available. It successfully obtained good results by preparing the dataset on heart disease and comparing the findings using the most recent state-of-the-art approaches. According to the experimental findings, the proposed approach performs better in evaluating performance measures than the existing approaches. The observed accuracy of the proposed method is 99.87% and 99.84% for chronic kidney disease dataset and cardiovascular disease dataset, respectively.