Reviewerly/exHarmony huggingface dataset:
Image Captioning with Audio Reinforcement using RNN and CNN

Image captioning involves generating a descriptive text that encapsulates the visual information contained in an image. This ppt proposes a deep learning model for image captioning that utilizes a Vision Transformer (ViT) and a GPT-2 network. Specifically, the ViT is employed to extract image features, which are then used by the GPT-2 network to generate captions for the images. To evaluate the effectiveness of the model, experiments are conducted on a Stanford dataset comprising images and their corresponding captions, and demonstrate that our model achieves performance comparable to other state-of-the-art image captioning models. Proposed results suggest that our model has the potential to generate accurate and semantically meaningful captions for images, and could be useful for various applications such as improving the accessibility of visual media.
Blockchain Oriented Hybrid Architecture for Crowdsource Model

Blockchain has been preserved as one of the strongest innovations to facilitate crowdsourcing as it offers new functionality, such as decentralization and transparency. Alas, certain intrinsic drawbacks of Blockchain, which become the efficiency bottleneck of crowdsourcing schemes, have hardly been tackled by the most current initiatives when implementing Blockchain to clustering. The followings are the few challenges faced by Blockchain: Scalability, the blockchain sector still face difficulties in managing a large number of users at once, Hackers and shadow dealing, Complex to understanding and adopting. In this article, to achieve decentralization and privacy protection, a modern hybrid blockchain crowd sourcing facility is proposed. With its hybrid Blockchain, doubleheaders, and double consensus algorithms, this network integrates stable connectivity between the requestors and the employees. In order to ensure the automated execution of the activities and the security of users’ privacy, smart contracts and zero awareness evidence are both used. Finally, tests are performed to validate the effectiveness of the platform’s consensus algorithm by matching it with the new one. The proposed hybrid framework proved that it is highly confidential and gives an extraordinary performance in regard to privacy. With the development of big data and medical information control systems, there is a growing interest in the sharing of electronic medical records between businesses for better medical care and advancement. On the other hand, the proliferation of large amounts of data, privacy issues, inherent business confidence issues, and complex legal requirements impede the quick development of healthcare intelligence.
Human Action Detection using Transfer Learning Algorithms

In the past few years, Human Action Detection has become more popular due to its applications in various fields. In this scenario, if a model can be created to detect human action then it will be very much useful to the application in Human Action Detection. Training a neural network from scratch may not be a feasible option. The main challenge to develop a neural networking model form scratch is the complexity of the model. The results of each model can be analyzed efficiently with Transfer Learning approach and also the results for the transfer learning models are much more promising than building the model from scratch when there is less amount of data. A comparative study is done to determine the best architecture for a transfer learning model.
Fake Review Detection using Naive Bayesian Classifier

The issue of fake reviews is becoming an increasingly prevalent one on the internet. The purpose of these reviews is to deliberately deceive potential customers and influence their purchasing decisions. Businesses and customers alike are looking for ways to spot and filter out these fake reviews as a result. The Naive Bayes algorithm is one effective method for identifying fake reviews. A well-known machine learning algorithm for classification tasks is Naive Bayes. It is based on the probability theorem of Bayes, which enables us to determine the probability of an event with some evidence. The Naive Bayes algorithm can be trained on a dataset of reviews that are known to be real or fake in the context of fake reviews. The characteristics of genuine and fake reviews are then learned with the algorithm by utilizing this training data. After the algorithm has been trained, it can use the characteristics of new reviews to determine whether they are genuine or fake. The fact that Naive Bayes is a relatively straightforward algorithm that can be trained quickly and easily is one advantage of using it to detect fake reviews. Additionally, it works well with text data, which is the format used by the majority of reviews. Having said that, it’s critical to keep in mind that Naive Bayes isn’t perfect and may not be able to spot all fake reviews. Cleaning and normalizing data, dealing with missing data, and dealing with outliers are all potential obstacles in data pre-processing.
Sign Language Detection and Recognition using CNN

Human motion detection in the film is the focus of this study. In contrast to the current trend of representing activities through the statistics of local video characteristics, a depiction drawn from human posture is more beneficial. To that end, the authors suggest a novel predictor for action detection using Convolutional Neural Networks (P-CNNs) based on the user’s poses. The description collects data on human movement and looks along bodily component lines. The authors utilized PCNN features that were obtained from both automatically estimated and manually labeled human postures. This study also explores different temporal aggregation methods and conducts experiments. The proposed approach consistently outperforms the state-of-the-art dataset.
Blockchain based Smart Healthcare System

The term “electronic health record” (EHR) refers to a patient’s electronic data collection that contains medical history, which can be accessed by authorized personnel from any location at any time. The use of e-healthcare has led to improved social and health outcomes, as well as a reduction in medical errors. However, the security challenges associated with patient data storage in IT frameworks pose a significant challenge to the advancement of e-healthcare. Blockchain technology has emerged as a potential solution to this issue and may revolutionize the healthcare industry. This study lays the foundation for secure patient data management using a blockchain-based approach, which incorporates a public ledger, private ledger, smart contracts, and context-based access control. The suggested architecture ensures reliable access to patient data, safe storage, and interoperability. Additionally, the proposed blockchain-based framework provides an efficient and trustworthy means of managing complex medical procedures. The study also outlines potential uses for blockchain technology in the healthcare industry, such as facilitating secure and anonymous sharing of health data for research purposes. The authors propose a novel, accessible, interoperable, and audible approach for maintaining medical records using smart contracts.
Detecting Malicious URLs using Data Analytics

Phishing is a type of social engineering hack in which attackers trick victims into providing their login information on a form that sends the information to a malicious server. The fast developing subfield of data science is dependent on the application of machine learning as a central component. One vs All Classification is a strong machine learning technique that may be used to detect dangerous URLs by classifying them as belonging to one of several categories, such as malware, phishing, or spam. This algorithm can be used to detect malicious URLs by classifying them as belonging to one of these categories. Training numerous binary classifiers is required for this method. Each of these classifiers is responsible for differentiating one category from the others. The suggested approach makes use of One Vs All Classification in order to obtain a high level of accuracy in the detection of dangerous URLs. It is possible to train the system using a tiny dataset that just includes the pertinent aspects of the URLs, such as the domain and the path. In conclusion, one vs All Classification is a promising strategy for detecting malicious URLs. It can deliver high accuracy even with tiny datasets and low computational resources, which is a significant advantage over existing approaches.
Resume Screening and Ranking using Convolutional Neural Network

Manual filtering becomes a tedious task for the recruiter as there are thousands of candidates applying for a single job posting. In this paper, a Long Short-term Memory (LSTM)-based and Convolutional Neural Network (CNN)-based approach are introduced for categorizing resumes submitted by candidates. Along with proposed CNN and LSTM model, also implemented other baseline models such as Random Forest, K-nearest neighbor, and Support Vector Machine. The resumes of the candidates and the job description are compared using Cosine Similarity. The list of job applicants is ranked based on the percentage obtained by Cosine Similarity.
Smart Learning of Machines using Augmented Reality

This work presents a new approach to teaching machines using Augmented Reality (AR) technology. AR is a technology that allows users to interact with a physical environment in a more immersive way. By using AR, machinery parts and their working can be taught more efficiently, as students can learn by observing and interacting with the environment around them. In addition, AR can be used to provide feedback to the machines in a more intuitive manner, allowing them to learn more quickly. The proposed approach uses a combination of sensors, cameras, and computer vision algorithms to detect objects and learn from them. Using this approach, machines can be taught to recognize objects, identify patterns, and make decisions. A proof-of-concept study that demonstrates the effectiveness of the proposed approach is presented here. The results of the study provide evidence that AR-based learning can be used to teach machines more efficiently and effectively.
Sign Language Recognition using Machine Learning Algorithm

Sign Language Recognition System aims to rephrase sign language into text or speech to ease communication between deaf and hearing individuals. This issue has far-reaching implications but remains exceptionally hard due to the complexity and large variation of hand movements. Existing SLR styles use palm-drafted characteristics to determine the shift of sign language and make classification models based on these features. Still, it’s delicate to design dependable features that accommodate wide variations in gestures. KNN (k-nearest neighbor) algorithm has shown major challenges like curse of dimensionalities, large dataset sizes, class imbalance, noise, and outliers. To address this conclusion, this study suggests a new convolutional neural network (CNN) that can automatically extract discriminative spatial-temporal elements extracted without information known from unprocessed video streams and avoid designing features. To improve performance, multi-channel video streams (such as color data, depth cues, and body joint orientations) are sent into CNN as input to combine color, depth, and movement data. Using a real-world dataset acquired using Microsoft Kinect, this study evaluated the proposed model and showed its effectiveness over traditional methods based on manual labor feature.
Comparative Study of Machine Learning Algorithms for Fake Review Detection with Emphasis on SVM

Online reviews have become an essential factor in consumer decision-making, with the credibility and authenticity of such reviews being a major concern. Fake reviews, including those generated by computers and humans, can significantly influence the opinions and decisions of consumers, resulting in a loss of trust in online platforms. The e-commerce sector has seen a rise in the prevalence of fake reviews, with some sellers engaging in deceptive practices to manipulate the ratings and rankings of their products. One such practice is creating fake positive reviews for their own products or paying individuals to do so. This can mislead customers into believing that the products are of high quality and popular when they are subpar. Another practice involves leaving fake negative reviews for a competitor’s products to damage their reputation and gain a competitive advantage. In addition, some sellers offer discounts or incentives to customers in exchange for positive reviews, leading to biased and inaccurate assessments of the quality of their products. These practices can harm the sales of honest sellers and undermine the trust of consumers in the e-commerce marketplace. This study proposes a supervised machine learning approach to identify fake reviews. The study compares the performance of six classification algorithms, namely Logistic Regression, K Nearest Neighbours, Support Vector Classifier, Decision Tree Classifier, Random Forests Classifier, and Multinomial Naive Bayes. The models are trained on a text dataset of 40433 reviews collected from https://osf.io/. The paper analyses the various features and techniques used in the different algorithms to detect fake reviews. The study concludes that supervised machine learning algorithms can effectively detect fake reviews and can be used to prevent their dissemination, thus enhancing the credibility and reliability of online reviews.
A Performance Analysis of Different Detection Techniques of Human Eye Pupils

In eye hospitals to detect human eye-related diseases, they are using tono-meter-based equipment. The doctors are using the manual joystick for center alignment of the eye, this leads to more time delay and inaccurate pupil identification. To overcome this problem, different human eye pupil detection method is proposed. The Ellipse fitting, Radial Symmetry Transform (RST), Circular Hough Transform, and Daugman's integro-differential operator are discussed in this work. An interesting and computationally efficient RST procedure for detecting the human eye, thereby reducing the complexity of doctors and n number of patients' eyes can be detected with less time delay.
NEWSIFY: - Article Summarization using Natural Language Processing and News Authentication using TF-IDF Vectorizer and Passive Aggressive Classifier

Reading news articles is an integral part of our daily life. It is the responsibility of journalists and editors to ascertain which articles are popular and real in order to effectively devote resources to provide a better reading experience to the users. The goal of this study is to create a model that simultaneously handles summarizing and determining whether or not a given news item is authentic. The system described here will be employed in a real-time setting. In fake news detection model, fake news by utilizing the TF-IDF vectorizer and the Passive Aggressive Classifier is implemented. Experimental results achieved an accuracy of 95.11% by combining these two methods. The system is further extended for obtaining a summary of news articles on a particular topic. This is implemented using Spacy, Genism, and NLTK methods. The time required to generate summary of news articles is found to be decrease as compare to earlier results. Experimental setup shows that the reading time is cut from “1.4 minutes” by NLTK to “0.47 minutes” by SumY, “0.47 minutes” by Genism, and “0.27 minutes” by Genism. However, the Spacy algorithm yields the best results, reducing the reading time from “1.4 minutes” to “0.48 minutes” and providing us with a high level of accuracy in the summary content, which includes all of the significant points.
Identifying the Personality Traits based on Handwriting using ANN and MLP concept

In this digital era, where everything is connected through technology, comes the role of Artificial Intelligence. AI is becoming a source code of every component in this digital world. It is a unification of Machine Learning, Data Analysis and Deep Learning techniques. As already known, humans are very complex in nature and understanding their unique traits or characteristics is solely based on their handwriting as it can give a rich information and insights on where we can work upon. Also, graphology is the study of identifying, evaluating, and understanding human personality through their way of stroking, making different shapes of letters, use of strength while writing, and by creating patterns. This study utilizes various machine learning algorithms and approaches to perform computer aided graphology to understand their emotional, mental and physical state by using their handwriting. This study reviews the novel ways to implement the model by using different algorithms and techniques to better understand the traits of a writer. This study proposes a novel model for identifying the personality/characteristic traits based on individual handwriting using machine learning and compare it with the other techniques. The results are then compared with hybrid model.
Detection of Hotspot Spoofing by Monitoring Network Traffic

An essential step in guaranteeing the security of wireless networks is the detection of Wi-Fi hotspot spoofing. When a hacker establishes a false Wi-Fi hotspot with the goal of intercepting user data or duping users into giving over important information, this is known as wi-fi hotspot spoofing This abstract gives a summary of the methods for spotting fake Wi-Fi hotspots. These methods include studying network metrics, keeping track of signal strength, and analysing network traffic. Wireless signals can be affected by interference from other devices or obstacles, which can impact the accuracy of BSSID, RSSI, and SSI measurements, Signal variation, Signal masking, BSSID cloning. Overreliance on BSSID, RSSI, and SSI for hotspot spoofing detection can lead to a false sense of security, as these measures can be circumvented by determined attackers. Additionally, aberrant patterns in network behaviour that can point to the existence of a faked hotspot can be found using machine learning methods. By employing these methods, the danger of data breach may be reduced and Wi-Fi hotspot spoofing can be detected.
Blockchain based Letter of Recommendation Verification System for Higher Studies

A letter of recommendation (LOR) is one of the important metrics in determining a student’s academic proficiency. It consists of the student’s academic milestones and is validated by the student’s professor which can be a key to pursuing higher studies in any reputed institution. It is required to be highly authentic and its evaluation is very uncompromising. A block-chain based LOR verification system has been proposed in this work. The student details are stored in the blockchain and the generation of a LOR is done with a secure hash key and is stored back in the block chain. External universities can examine the student’s LOR, through a portal to access the block chain. With the secure hash key, the LOR with the student’s professor’s validation is observed. By the proposed method, transparency, security and authenticity in the process of generating the LOR and sending it to desired universities is maintained.
A Novel Blockchain based Decentralised Ballot System

Online voting is getting increasingly popular in modern society. It has the potential to cut administrative costs while increasing participation rates. Citizens can vote online from any place with an Internet connection, eliminating the need to print ballots or build up polling locations. Despite these benefits, remote voting systems have to be approached with extreme caution since it represents new hazards. A single weakness might make it trivial to manipulate votes on a large scale. Electronic voting systems used in elections must be trustworthy, precise, secure, and practical. However, problems with electronic voting machines may prevent their broad use. To overcome these concerns, blockchain technology was developed, which provides decentralised nodes for vote processing and is utilised to build electronic voting systems. Because it provides shared, not repudiating, and security protection features, this technology is an excellent alternative to standard electronic voting systems. The following article provides an overview of electronic voting systems that use block chain technology. The primary purpose of this analysis was to assess the current state of blockchain-based voting studies and balloting systems as a whole as well as any associated issues, in order to forecast future advances. This study offers a conceptual overview of the anticipated block chain-based electronic voting application, as well as an introduction to the blockchain’s core structure and properties in connection to electronic voting. This investigation indicated that blockchain technology may be able to solve some of the problems now plaguing voting methods. However, the most often raised concerns about blockchain applications are transactions timelines and privacy protection. A based on blockchain technology electronic voting system must be scalable in terms of transaction speed as well as distance voting security. Because of these problems, it was decided that current structures needed to be modified in order to be used in voting systems.
AR and VR in Military Combat: Enhancing Soldier Performance and Safety

This study intends to provide an overview of the use of Augmented Reality (AR) and Virtual Reality (VR) technologies in military applications. Military training, situational awareness and decision making can benefit from AR and VR technologies. This study describes various AR and VR applications in the military, including simulators, training systems, and operational support systems and also discusses about the challenges associated with the adoption of AR and VR technologies in the military, such as technical limitations, cost, and security concerns. Finally, this study concludes with a discussion on the future directions of AR and VR technologies in the military field by highlighting potential areas of growth and future research directions.
Video Steganography in IoT: Information Embedding using OpenCV and 2LSB

After a review of many research study it is been concluded that various security techniques are used for data transmission in Internet of Things (IoT) devices. Considerable progress has been made in enhancing data transmission security, but still, some gaps exist. Existing steganography techniques exhibit gaps such as limited concealing capacity, substandard quality of stego files, and reduced resilience when faced with image processing attacks. To overcome some of the existing gaps proposed work utilizes video steganography. In this work, a video steganography technique for securing data transmission in IoT devices has been proposed using OpenCV and Least Significant Bit (LSB). Extraction of video frames for hiding secret data has been done using OpenCV and then concealing the secret message is employed by the 2 LSB technique. The two Least Significant Bits are modified to hide the secret message. OpenCV facilitates the identification of suitable regions of interest for concealing secret data, leading to enhanced robustness in hiding information. Videos offer a wide range of possibilities for embedding secret information, resulting in a significant capacity for concealment and imperceptibility. For measuring the performance of research work Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Entropy, and Histogram analysis are done.