Reviewerly/exHarmony huggingface dataset:
Efficiency Evaluation Parameters of Digital Image Watermarking Techniques

There are a good number of photo editing tools. Hence it is very easy to tamper a watermarked image. Image watermarking techniques are used in various applications like copy right protection, image authentication, image tamper correction etc. This paper narrates the various efficacy parameters of watermarking techniques used in images. The performance of a digital image watermarking technique is mainly measured by the parameters like hiding capacity (HC), imperceptibility, robustness, computational complexity, and security. The HC is the magnitude of bits that can be hidden in a image in terms of watermark bits. Peak signal-to-noise ratio (PSNR) estimates the distortion in the watermarked image. Structural similarity (SSIM) estimates the similarity between the original image and the watermarked image. The watermark should not be disturbed after some attacks are applied on the watermarked image. If the watermarked bits are altered by various attacks, then the purpose of watermarking will not be served. So, this article also describes the various attacks and counter measures on watermarked images.
Data Analytics System for Digital Currency Price Prediction Using Regression Algorithm

Digital currencies like dollars, euros, and yen are analogous to cryptocurrencies. One can purchase and sell bitcoins using exchanges. For several months in 2017, the market capitalization of cryptocurrencies increased dramatically, which significantly increased their appeal. As a result, interest in virtual currencies has increased. Unlike traditional paper money, which may be created as needed by the market, the supply of cryptocurrency is finite. This prevents currency depreciation and printing inflation from occurring. The remaining 21 million cryptocurrencies are expected to take a long time to mine, and related infrastructure will be required to handle such large computations, possibly with a low return in the future, given the limited supply of cryptocurrencies and the fact that 80% of them had already been mined by mid-July 2018. Utilizing machine learning, this programme assists with cryptocurrency price forecasting. To quantitatively to tell beforehand bitcoin prices with greater precisely. The proposed system uses the LSTM algorithm, a machine learning algorithm, as demonstrated by experimental results, to predict cryptocurrency prices with a high level of accuracy.
Logistics and Supply Chain Management in Leather Industry Using Blockchain

Blockchain is one of the famous technologies nowadays. so, the paper can integrate into many research departments and industries applications like (Logistics/Healthcare/Energy Industry/etc.,). Here the paper talk about Leather Logistics, it's one of the familiar logistics around the globe. moreover, high revenue industrial application. coordination of the executive's application to give a bunch of capacities to design, plan, arrange, screen, and approve the performed exercises. Such capacities can be effectively and safely upheld by blockchain. Utilizing the common appropriated records in blockchain to check, store and review coordination exchanges will assist with diminishing time delays, board expenses, and human blunders. Also, applying brilliant agreements will work with arrangements between the organizations in question, what's more, make authoritative agreements quicker and with lower costs. With these advantages, blockchain is expected to generously affect the coordination business. There are numerous new businesses in this space that offer blockchain-based coordination of the executive's stages and applications. One model is Provenance, which gives a detectability framework that joins customers and providers together for various coordination exercises. the paper can easily manage to track/trace the logistics goods with the help of RFID Technology. Safely recording furthermore, sharing conditional information, setting up mechanized and effective production network measures, and upgrading straightforwardness across the entire worth chain are a few instances of these issues. Blockchain offers a powerful approach to handling these issues by utilizing disseminated, shared, secure, and permission conditional records. The work of blockchain advancements and the chance of applying them in various circumstances empowers the cowhide coordinations industry through expanded proficiency and security; upgraded recognizability and straightforwardness; furthermore, and diminished expenses.
Software Testing Based on Software Product Quality Metrics (SPQM)

Producinghigh-quality products has emerged-as a crucial component of corporate success in today's marketplace. In this regard, establishing and holding to software product to assess the present quality standardto environment and promote system to improvethe quality of the productit significance. Testing software is an essential step in the software development process. This technique is more accurate and effective when it is automated. Emerged new testing tools for automated testing. The selection of the proper tools has become a challenging and confusing activity as a result of the abundance and variety of testing instruments. The current study attempts to assess the current research areas and trends on this subject that have appeared in the literature over the previous twelve years. Based on their titles and abstracts, 50 conference and article papers on SPQM published in the interval 2010 and 2022 were subjected to Systematic Mapping (SM) research. The findings are presented using a combination of diagrams, written explanations, and mind-mapping techniques, which include a trend map for the period from 2010 to 2022, details about the field and measurement techniques, identified areas for improvement, and consistency across sources such as conference papers, journal articles, and internationally recognized quality models. Future research that aims to advance this important topic may build on the outcome of this study. This research may provide a starting point for further investigations meant to advance this vital area of study
An IoT Based Gesture Recognition System for Vocally and Hearing Impaired Individuals

The sign language recognition system is one of the advanced technologies for impaired to communicate with others and it can be utilized by deaf and dumb people by interacting and sharing information with others. At present, the sign language recognition system consists of two types of methodssuch as glove-based and vision-based techniques. The former usesthe gloves in which the sensors are fixed in it to detect the hand gestures while the later uses the camera to detect the hand characteristics of the user for recognizing and translating the sign languages. The paper mainly focuses on implementation of embedded system for communicating with disabled people and normal people where communication is the major challenge faced by deaf and dumb people. Most of the normal people does not understand the signs given by the disabled people and hence this will be more useful for deaf and dumb community. The main objective of the system is to develop user friendly device and to fulfill the basic need of the impaired people and to overcome their difficulty in communication. The system is designed with the flex sensors to detect the hand movements which is attached with the gloves. The gestures are detected by the sensors and the output is givenas text display, so that the user can easily convey their message with others.
Symmetric And Asymmetric Algorithms for Susceptible Harmful in Wireless Sensor Network on FPGA Platform

WSNs are made up of a network of sensor nodes that are used to track environmental characteristics like pressure, temperature, etc. and communicate the data to the intended location. Networks must overcome obstacles including security, routing, problems with energy conservation, scalability, dependability, secrecy, integrity, authentication, and others when transmitting information. Different routing systems, including data-centric, location- and mobility-based, Quality of Service (QoS)-based, and multipath routing protocols, have been employed to address these issues. Even while these routing methods guarantee a successful transaction, the network still has to manage intermediary assaults that can completely alter the content of messages being carried. Therefore, a number of cryptographic approaches have been used to minimise the network. The newly developed symmetric and asymmetric algorithms are susceptible to problems with route latency, area overhead, and on-chip power consumption. Thus, to reduce these harmful actions in WSNs, an efficient reduced key size and high security based cryptographic method must be used. Utilizing the limited network resources, the main goal of the study is to increase network security while reducing network overhead and path delay-related problems.
Securing the Confidential Data Using Blockchain and DT Framework

In this Digital era, 84% of people in the world are using the internet and creating login credentials for banks, social media accounts, and so on... most people are storing their login credentials in auto save mode or store them in the local system. by this type of storing, 66% of login credentials have been leaked every day. in this digital world, major people are facing issues remembering login credentials and securely storing confidential data due to the increase of login credentials and also there was a problem in storing data in centralized data because the server has been hacked or affected under a malicious attack by hackers. By that confidential documents have been leaked and caused major issues leading to corporate war. In Government, they faced these types of issues in storing secure data. for these types of issues, we invent a password manager to store data in a secure format. it works under blockchain technology. which data has been encrypted by high-level encryption namely SHA 256 algorithm and connected into chain format. encrypted data has been stored in a decentralized server or peer-to-peer network. the application is known as "SecBlock". With this feature, users cannot be afraid about their login credentials. it was 97% securable.
A Study of Small Corpus-based NMT for Image-based Text Recognition

This paper presents an applied study that aims to build a Chinese and foreign language translation system for real-time applications, using image recognition and Neural Machine Translation (NMT) techniques. The image and text recognition engine development focus on possible technologies for recognition tasks of commonly used text items (Chinese, Portuguese, English) in Macau. The paper gives an overview of Artificial Intelligence (AI), Big Data Mining, and other cutting-edge technologies. The paper also develops an NMT for a limited corpus, with short training times for some translation models. It also explores deep learning problems, such as unsupervised learning, black-box models, and online learning, and shows how these challenges translate into fruitful avenues for future research.
Forecasting Diseases Through Microscopic Image Analysis by Utilizing BNN

Relying on the physical acuity of a haematologist, the current approach to diagnosing blood disorders is both time-consuming and prone to errors. To aid in clinical decision-making, there is a need for an automated visual image analysis system Early, accurate, and secure diagnosis of leukemia, a cancer that impacts the development of immature and abnormal white blood cells called blasts, is crucial for successful treatment and the patient's overall survival. While a blood smear test is typically used to diagnose by observing the white blood cells, machine learning techniques have also been identified as useful for diagnosing diseases like leukaemia. However, these techniques can have a significant misclassification error rate. To address this, a deep learning system can be used to categorize microscope pictures for the examination of white blood count. The WBC difference count system consists of two modules: the detection method and the classification model. The first module, called the detection module, is responsible for processing the raw bone marrow smear images. It identifies various components such as WBCs, red blood cells, platelet counts, and coloring impurities through image segmentation. The detection module then classifies the cells based on their features, such as size and shape. Once the cells are identified, they are passed on to the classification module. The classification process comprises of two stages, where the first stage involves filtering out non-relevant cells, such as crushed or degenerated cells, which are of no use for leukemia diagnosis. In the second stage, the quantifiable WBCs are differentiated using the back propagation network technique.
A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security

In recent years, blockchain has become an extremely trending technology, capable of solving a variety of problems. One of these domains is cybersecurity, where blockchain technology has a huge scope. To dive deeper into this topic, we first need to understand the cybersecurity domain, the need for this field, and how it has become crucial to the current Information-Technology industry. Once we have a good understanding of the field of cybersecurity, we next focus on blockchain technology, its basic working process, and what makes it a trending infrastructural technology in today’s world. The basic idea about the field of cybersecurity and blockchain technology can help us understand how the two different fields can be integrated to solve several problems in the cybersecurity domain. Eventually, we discuss the pros and cons of blockchain technology in cybersecurity and how the integration of the two different fields can make a difference. This study aims to explore various possibilities where blockchain technology can be utilized in several applications to solve a variety of problems in the field of cybersecurity.
Cyber-Ego: The Digital Self

Cyber Ego expands to Cyber and Alter-ego. In this proposed paper, I have integrated four deep learning models for performing the tasks - natural language generation, text-to-speech, speech-driven facial animation and image animation as a combined framework to generates a video of the character speaking the generated content with the character’s voice. Text data and audio voice lines have been collected and pre-processed to train the text-to-speech model. While pre-trained models are fine-tuned and used for the speech-driven facial animation model and image animation model. To put everything together in an interactive way, a Flask-based Python web application is created to provide as an interface that handles user inputs and generates the result.
A Novel Steganographic Approach for Image Encryption Using Watermarking

Steganography is a technique for obfuscating secret information by enclosing it in a regular, non-secret file or communication; the information is subsequently extracted at the intended location. Steganography can be used in addition to encryption to further conceal or safeguard data. Watermarking is one such technique practiced in the area of steganography. Watermarking can be practiced via multiple algorithmic techniques like Discrete Wavelength Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), Discrete Fourier Transform (DFT). In this study, a combination of such approaches along with AES encrypted watermarked images has been implemented. Validation of these techniques has been achieved by evaluating the Peak Signal to Noise Ratio (PSNR).
Empirical Analysis of Quantum Compution using Qiskit Framework

Quantum computing is an emerging field of physics which solves complex problems in n Polynomial time. Availability of open-source support and development tools makes it easy for developers and researcher to explore concepts in the is area. Conceptual development illustrates that it is an efficient way of implementation and testing has certain difficulties due to the noisy environment of today’s quantum computers. Quantum Algorithms implemented are solvable in polynomial time and can be used effectively for application implementation in AI, finance and drug production etc.
Phishing Attack Detection Using Convolutional Neural Networks

Phishing attacks are a prevalent form of social engineering that target individuals through emails to obtain confidential and sensitive information. These attacks can lead to larger security breaches in both corporate and government networks. There have been several attempts to counter phishing assaults, but so far none have proven successful. For this reason, improved strategies for identifying phishing attempts are desperately needed. The proposed fix is a deep learning-based strategy for identifying malicious phishing attempts. By analyzing more than 5,000 phishing emails sent at the University of Malaysia’s Department of Computer Science and Information Technology, the authors hoped to create a model that reliably detects phishing assaults to achieve this, they selected relevant features through feature engineering and used the Random Forest models to extract feature importance at different levels. Finally, the model was trained using Convolutional Neural Networks (CNN), leading to improved detection and accuracy.
Intelligent Door Lock System using Ensemble Machine Learning Algorithms

The Internet of Things (IoT) is now a critical component of home automation and security, particularly for door lock systems. Face recognition powered by IoT is becoming more popular because it is dependable and simple to use. To prevent algorithmic errors and produce enhanced accuracy than existing systems, our system employs a facial recognition door lock with the integration of three algorithms on a single camera. The system detects unauthorized people near the entrance and notifies the user via a mobile app. For hardware communication, we used a camera module, a Raspberry PI, passive infrared motion sensors, and wireless internet access. The Python and OpenCV libraries are used in the algorithm's development. When the motion sensor detects activity in front of the door, the camera is activated, and the system gathers camera feeds and sends the images to three algorithms for analysis. If any two or all of the algorithm's results match the image in the database, the door unlocks; otherwise, it remains locked. The development of this system aims to enhance accuracy by combining three algorithms and to provide consumers with data about what is happening in front of their home via a smart door lock system.
A Novel Approach to Fall Detection System using Computer Vision

The fall causes minor to major injuries in human beings due to accidents, weakness in muscles, loss of balance etc. Therefore, the researchers have given the solutions for various fall detection systems using sensors, actuators, accelerometers along with integration of software modules. These proposed solutions are effective within the specific scope of their usage, but they require tight integration between hardware and software modules. Considering this, in this paper, an attempt has been made to provide a software solution by using computer vision approaches. The proposed system determines the fall by detecting and measuring the human body’s change in the center of gravity, inclination angle and velocity of fall. New features have been highlighted for the development of further fall detection models.
IoT - Based ATM Pin Entry by Random Word Generator Using Design Thinking Framework

The biggest problem society is currently facing is ATM theft. Nowadays, all keypad-based authentication systems are insecure, opening up several opportunities for shoulder-surfing password cracking. Today's thieves use a variety of techniques for account hacking and password stealing. We have to protect our accounts through the verification procedure, and we should exercise extreme caution when conducting transactions and money withdrawals. The paper propose a new approach to the problem of generating random words while entering pins into ATMs. This strategy is quite secure because the thieves might not know how the system functions. In this concept, the random word may be further defined as the addition of an alphabetic letter together with the password and words that require the specific alphabetic letter to be changed regularly for each transaction. Moreover, GSM applications are attached to Automated Teller Machines to facilitate wireless communication. A warning will be issued to the account holder if someone tries to enter an outdated password that was obtained through hacking techniques.
Video Based 3D Convolution Network for Air Quality Assessment

There is an important problem in measuring and detecting air pollution. Monitoring air pollution requires a quick and effectual clarification of the air quality. In order to measure air quality, currently available devices rely heavily on a multitude of sensors, not only are they incredibly expensive, but they are also troublesome to transport. An advance in deep learning has led to great improvements in vision-based computing tasks analysing and interpreting videos, for example. Several new strategies for air quality measurement have been proposed based on images and have been proven accurate in unique situations. Due to noise in photos and the absence of temporal relations between photos, the performance of these techniques is not solid. Due to the rising popularity of short video platforms, it has become easier to acquire and distribute video content. Video-based double channel 3D convolution community for accurate air pollution measurements was proposed. Air contamination is defined as the presence of gasses and particles in the air. Vehicle spreads, fabricated materials from creation lines, build-up, residue and shape spores may be suspended as particles. Gases such as ozone are a significant source of air pollution in urban areas. It is additionally known as exhaust cloud when ozone forms air pollution. A semantic channel is uploaded in addition to the primary visible channel to assist the community with analysing place degree features. Channels are mixed for accurate prediction. We gather outdoor video datasets for air quality measurements. Our approach outperforms the image-based approach in the air fine dimension and achieves top results.
Information Security Using Counter-Anti-Forensics

Digital forensics is a methodology in which the investigators of the cybercrime will be assessing the machines and will be getting some key insights about the crime that has actually happened and also they will gain some information about the attacker. The main theme of this project is to develop a methodology to be followed in order to secure the system once a threat is detected. All this was executed in a real-time simulator and the observations are made. Keeping these observations further changes are made to the improvement of the functionality of the code.