Reviewerly/exHarmony huggingface dataset:
A Novel Approach Using Extractive and Abstractive Summarization for the Genre Classification of Short Text

Genre classification for text documents is useful in media monitoring and detection of misinformation. Recent work in text classification for genre has shown that advanced algorithms such as neural networks and transformers are well-suited for the purpose. However, for shorter text documents, such as those obtained from social media or news articles, training of deep learning models becomes challenging since they require a large amount of input. Furthermore, genre classification of text summaries, such as headlines of news, is an important direction which has not been explored at large. In this work, the effect of Extractive and Abstractive Summarization on classification for genre of text documents was evaluated. Gensim summarizer was used to obtain extractive summaries and the Pegasus summarizer to obtain abstractive summaries. For classification, two classes of genres, Fiction and Non-fiction, were considered while the gold standard Brown Corpus was used for experimentation. The features used for genre classification were frequencies of various Part-of-Speech (PoS) tags derived from five Penn TreeBank annotated tags. Logistic Regression (LR) and Support Vector Machines (SVM) were used for classification purposes. The results of classification were better for summaries obtained using the extractive technique, indicating that the features of extractive summaries remain in agreement with the documents from which the summary is constructed as compared to abstractive summaries. Further, the SVM classifier performed better than the LR classifier. For exhaustive coverage of the research goal, further experimentation with the number of words of the output summaries of the extractive technique was performed to arrive at a threshold value of the length of summaries. The value indicated that summaries as short as 80 words can be successfully classified using this method.
Design and Development of a People Counting System

Counting persons entering or leaving a space is known as people counting. Typically, this is done at an entrance or exit. The people counter is an electronic device that automatically collects count data. In this work material selection and fabrication of a people counting system is presented. The system uses VL53L1X and HC-SR501 sensors for detecting entry and exit of people based on infrared radiation within a range of 2 m. A counting algorithm is implemented to count the number of people passing. The passing count is transmitted to the display unit through a bluetooth connection. An ESP32 microcontroller receives the count data from the sensor. Polylactic acid material is used for 3D printing of the display case and cover. Entry, exit and total count of people is displayed continuously. For the proposed system three design concepts are presented and an optimal design was selected for fabrication. Experimental results show that the developed system is fast and counts only people with an accuracy level of 98%.
Automatic Scikit-learn based detection and classification of Breast Cancer using Machine./ Learning techniques

The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against breast cancer.
Vehicle Detection and Classification based on C-DSO Dataset using YOLO v3 with SRBD Method for Intelligent Transportation Applications

The tremendous evolution of Computer vision and technology, the most challenging application of vehicle category detection is an important role in our Indian Traffic system. The most of the vehicle detection algorithms facing some struggles with vehicle object detection and recognition rate. Using Deep Learning with Conventional Neural networks to undercover the problem of vehicle detection and to understand the right way of vehicle classification, detection, and vehicle recognition. This paper proposes our model of designing a dataset (C-DSO) and combines the method YOLO(You Only Look Once) V3 algorithm with SRBD to introduce the SRBD-C-DSO algorithm, it detects the smart way of integrating the pre-training of the image, labeling of vehicle image, image smoothing, and Annotation. Furthermore, renovating the dataset, adding the various categories of vehicle images, and the experimental results show a better detection rate and reduction of loss of validation in the dataset.
Intelligent Recognition of Ancient Brahmi Characters using Transfer Learning

Character recognition is an unresolved challenge when it comes to regional languages. Researchers across the world are working on automatic document reading, Translation, indexing and ancient text collection and recognition. Similar challenges are addressed in this paper to decipher ancient brahmi characters found on various inputs. Inputs considered are handwritten brahmi characters, Printed brahmi characters and Epigraphical script images. Manual collection of dataset includes, 287 characters having 210 samples per class making the dataset big enough to train and test deep convolutional neural networks for recognition. Alexnet, Googlenet and Inception-V3networks were transfer learned for the brahmi input and Inception V3 performed well. The traditional machine learning models are trained and tested on Hu-Zernike moments and found, Transfer learning yields better results by recording huge features from the image samples.
Textual Analytics on ‘Azadi Ka Amrit Mahotsav’: Exploring Indian citizens' ideas for achieving Aatmanirbhar Bharat

Recent years have witnessed an increase in the number of online forums established by governments to encourage public participation in policymaking. This has resulted in the availability of enormous amounts of digital text. It is not practical to manually analyze such large amounts of text data. Machine learning algorithms are especially useful in this context for exploring the hidden patterns in the data. The purpose of this work is to explore the ideas and suggestions proposed by the general public of India as a part of “Azadi Ka Amrit Mahotsav”, a high-profile public-engagement campaign, initiated by honourable prime minister of India, Sri. Narendra Modi. To accomplish the study, ‘Topic modelling based on LDA’, a well-known textual data analytics technique, was employed on a corpus of ideas and suggestions posted by the citizens in the ‘Ideas Box’, launched by the government of India between January 26, 2021 and September 15, 2021 and the results were extracted.
Face Recognition and Identification Using Deep Learning

Face recognition is an unexpectedly growing and extensively carried out component of biometric technologies. Its programs are broad, starting from regulation enforcement to customer programs, and enterprise performance and tracking answers. The latest creation of affordable, effective GPUs and the introduction of massive face databases has drawn studies consciousness usually at the improvement of an increasing number of deep neural networks designed for all components identification tasks, starting from detection and pre-processing to characteristic illustration and category in identity verification. Convolutional neural networks can be used to extract various features from images. A fairly straightforward method for facial recognition is employed here. Here, it's crucial to take a deep neural network and generate a set of digits representing a face (called a face code). The network should produce similar results for both photographs if you pass two separate images of the same person. But it should produce different results if you give it two different photographs of two different persons. The algorithm need to return very different output for one image. This implies that a neural network must be trained to automatically recognize various facial features and determine figures based on them. It is possible to imagine that a neural network's output identifies a specific person's face. Passing different images of the same person, the neural network's output will resemble one another/near, but passing images of different people, the output will be very different. This paper presents a technique for recognition of face using the concept of deep learning.
Traffic sign detection using Deep learning techniques

In this fast-growing world, the trend of self-driving cars and advanced driving systems is increasing rapidly. For humans, identifying a traffic sign is an easy task. However, for self-driving cars, accurate and fast road sign location and classification remains an incredible challenge. So, detection of traffic sign in autonomous vehicles has been catching the attention of the computer vision community incessantly for several decades. With the intensive development of artificial intelligence and computer vision technologies, Deep Learning appears to be one of the most effective solutions for complex detection tasks such as traffic sign detection, which has high demands on accuracy and response speed in multi-object detection. Our proposed work involves in developing a model based on Convolution Neural Network (CNN) in Deep Learning that can be utilized to characterize the traffic signs with great exactness. The performance of our model will be measured by using different metrics like accuracy, F1 score.
Phishing Website Detection using Hyper-parameter Optimization and Comparison of Cross-validation in Machine Learning Based Solution

The development of the internet and web technologies has led to their use in a variety of services. It has led to an increase in cyber security problems over time, the most well-known of which is the phishing assault, in which malicious websites appear as legitimate websites to steal the details of unsuspecting users needed for unauthorised access. Anti-phishing software and machine learning (ML) techniques are now used as mitigation measures, and they have been successful in detecting phishing activities. On the other hand, hackers are developing new methods to get through existing defences. Despite this, there is a continuing need for innovative and effective website phishing detection solutions due to the vitality of phishing operations. In this study, phishing website detection is proposed using machine learning classifiers and incorporates several cross-validation techniques to obtain the highest level of accuracy (97.3%). The random forest's hyper parameter tuning has finally been used to get successful results. The Phishtank repository, a collection of authentic and phishing websites, is used to assess the efficiency of the proposed system. The results of the experiment showed that hyper parameters outperformed a few chosen baseline classifiers. High detection accuracy was reported by Hyper parameter (97.6%). Therefore, the proposed approach is suggested for addressing complex phishing assaults.
Deformation Analysis of Aluminium

The paper uses digital image correlation open-source software Ncorr to analyse deformation in aluminium. An aluminium plate was subjected to deformation and its image was obtained. This image was compared with the original undeformed image to analyse strains. It is concluded that the strains are uniform over the entire plate except for near cracks, where they attain high values.
Efficient Feature Selection Approach For Breast Cancer Classification Using Machine Learning

Breast cancer detection and classification is very important area and it is playing vital role in saving human life if we detect in early stage. In this research, we have used only 4 features for detecting cancer or non cancer image identification. By using 6 features with other classifying methods they got only 98% accuracy. By using this feature analysis, method and feed forward backpropagation classification method given 100% accuracy, sensitivity and specificity. Compared with the existing methods it has given better accuracy.
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification

The use of deep learning in computer vision tasks such as image classification has led to a rapid increase in the performance of such systems. Due to this substantial increment in the utility of these systems, the use of artificial intelligence in many critical tasks has exploded. In the medical domain, medical image classification systems are being adopted due to their high accuracy and near parity with human physicians in many tasks. However, these artificial intelligence systems are extremely complex and are considered ‘black boxes’ by scientists, due to the difficulty in interpreting what exactly led to the predictions made by these models. When these systems are being used to assist high-stakes decision-making, it is extremely important to be able to understand, verify and justify the conclusions reached by the model. The research techniques being used to gain insight into the black-box models are in the field of explainable artificial intelligence (XAI). In this paper, we evaluated three different XAI methods across two convolutional neural network models trained to classify lung cancer from histopathological images. We visualized the outputs and analyzed the performance of these methods, in order to better understand how to apply explainable artificial intelligence in the medical domain.
Fine - Grained Sentiment Analysis on Online Reviews

Sentiment analysis is an effective text categorization technique that is often used. It analyses any given text or comment and categorizes it as positive or negative depending on the viewpoints expressed. Previous sentiment categorization research has relied on either a traditional-based approach or machine learning approaches. Similarly, a fundamental flaw in earlier systems was they focused solely on only two-way classification or tri-classification of reviews, i.e., classifying the review as positive, negative, or neutral. Denial of the highly positive and highly negative reviews will lead to a misconception of the user's opinion of a product or movie, causing a trend or business to suffer. When solely a lexicon-based method is used, the system is heavily reliant on the lexicon resource and dictionary chosen. The performance of a machine learning system is determined by the algorithms used. Beyond the binary or tri classification of the customer's review, the suggested work integrates a traditional based approach (SentiWordNet) with statistical methods such as Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), and SVM are used to solve the aforesaid challenges. These machine learning methods were compared to a lexicon-based strategy in terms of performance. The results showed that the Logistic Regression and SVM methods outperformed the rest two algorithms.
Design and Implementation Secure Hash Algorithm 3 (SHA-3) using FPGA

In this moment the world an instant communication technology, security has become extremely significance factor. It is crucial in system with communication fields that the cryptograph mechanism is intricated. The procedures intricate the creation of a hash function, that is a one-way encrypted code used in data security. The most common examples the Digital signatures, the MAC (message authentication codes), with the smart cards. Secure Hash Algorithm (SHA-3) is a new typical cryptograph Keccak hash function that is not vulnerable for the attacks. In this paper, we presented software simulation for optimization techniques on Keccak SHA-3 algorithm using the (VHDL) programming language with the output length for the Keccak hash function is (256). And the design as simulation was implemented on FPGA. implementation procedure is quick with the efficient ratio. algorithm's goals are to increase throughput and decrease area.
Hospital Appointment Management System for Telemedicine System Reinforced with Diagnostic Data Transfer

The acceptance and attraction of telemedicine, a clinical component of the telehealth system, have increased due to the introduction of digital technology. Rapid advancements in communication, imaging technologies, and healthcare systems enable virtual consultation, diagnosis, and medication prescriptions. This project aims to strengthen the telehealth system by creating a website that makes it easier for patients and doctors to interact virtually. The website has tools for making appointments with doctors, writing prescriptions, and keeping track of patients' progress. The system's admin module facilitates the overall management of the website and the coordination of communication between doctors and patients. The website enables users to log in, register, schedule appointments with doctors, and get prescription drug information. Doctors have access to patient meeting times, medical reports, and the provision to write prescriptions. It supports the transfer of medical reports in a fast and reliable mode. Since the direct transfer of medical reports needs more time and bandwidth, a hybrid compression technique reduces the report data size in the proposed system. The system compresses and transfers the medical reports with an estimated means of compression ratio, PSNR, and MSE of 26.95,43.52dB, and 10.5, respectively.
Shaping Postquantum Cryptography: The Hidden Subgroup and Shift Problems

The security of popular public key-cryptographic protocols, such as RSA, Diffie-Hellman key exchange and the Digital Signature Algorithm (DSA), is endangered by the advent of quantum computers. Shor brought a big breakthrough with his quantum algorithm that can be used to factor an arbitrarily large integer into the product of its prime factors, hence jeopardizing the security of RSA, and that at the same time also solves the Discrete Logarithm Problem, which raises issues for certain Diffie-Hellman based cryptosystems and digital signatures. It is hence crucial to upgrade our current tools for post-quantum cryptography: it should be infeasible, even using quantum algorithms, to break the new cryptosystems. Popular candidates include for example elliptic curve or lattice-based cryptography, but they share something in common: they are specific cases of the more general Hidden Subgroup and connected Hidden Shift Problem.
A 28-nm 8-bit Floating-Point Tensor Core-Based Programmable CNN Training Processor With Dynamic Structured Sparsity

Training deep/convolutional neural networks (DNNs/CNNs) requires a large amount of memory and iterative computation, which necessitates speedup and energy reduction, especially for edge devices with resource/energy constraints. In this work, we present an 8-bit floating-point (FP8) training the processor which implements: 1) highly parallel tensor cores (fused multiply–add trees) that maintain high utilization throughout forward propagation (FP), backward propagation (BP), and weight update (WU) phases of the training process; 2) hardware-efficient channel gating for dynamic output activation sparsity; 3) dynamic weight sparsity (WS) based on group Lasso; and 4) gradient skipping based on the FP prediction error. We develop a custom instruction set architecture (ISA) to flexibly support different CNN topologies and training parameters. The 28-nm prototype chip demonstrates large improvements in floating-point operations (FLOPs) reduction (7.3×), energy efficiency (16.4 TFLOPS/W), and overall training latency speedup (4.7×), for both supervised and self-supervised training tasks.
Game Analysis and Optimization for Evolutionary Dynamic Heterogeneous Redundancy

Dynamic Heterogeneous Redundancy (DHR) has been considered as a proactive defense architecture against unknown vulnerabilities and backdoors. It is a heterogeneous, fault-tolerant, and redundant system, which ensures the security by constantly switching heterogeneous executors. However, a limited number of heterogeneous executors can be merely switched and combined statically. It is difficult to cope with persistent threats. In this paper, we propose an evolutionary DHR system, add evolutionary sub-strategies of executors to solve this problem, optimizing defense mechanisms of DHR. To demonstrate theoretical validity, a game-theoretic analysis of the DHR security mechanism is performed under dynamic incomplete information, and the dilemma of defense is discussed. We construct a DHR game model based on evolutionary, and the results are extended to the general case to analyze the Bayesian equilibrium when each service strategy has a different number of evolved sub-strategies. In addition, the correlation factor is added to the game to investigate the impact of the correlation between different heterogeneous executors on defense results. Finally, Gambit and NS2 simulation experiments for the proposed method are shown. To verify the applicability of the physical system, a prototype DHR system based on dynamic evolution was constructed. We collect key configuration information and modified them randomly. It ensures that the information scanned by the attacker is different each time. Finally, the effectiveness of the physical system is verified by nmap and metasploit tools.