Reviewerly/exHarmony huggingface dataset:
Quantum analysis of squiggle data

Abstract Squiggle data is the numerical output of DNA and RNA sequencing by the Nanopore next generation sequencing platform. Nanopore sequencing offers expanded applications compared to previous sequencing techniques but produces a large amount of data in the form of current measurements over time. The analysis of these segments of current measurements require more complex and computationally intensive algorithms than previous sequencing technologies. The purpose of this study is to investigate in principle the potential of using quantum computers to speed up Nanopore data analysis. Quantum circuits are designed to extract major features of squiggle current measurements. The circuits are analyzed theoretically in terms of size and performance. Practical experiments on IBM QX show the limitations of the state of the art quantum computer to tackle real life squiggle data problems. Nevertheless, pre-processing of the squiggle data using the inverse wavelet transform, as experimented and analyzed in this paper as well, reduces the dimensionality of the problem in order to fit a reasonable size quantum computer in the hopefully near future.
Aloha: A decentralised messaging topic-based channel application

In the current technological environment, it is essential that decentralised messaging services take the role of centralised systems to provide consumers with a safe experience. Due to the availability of Blockchain technology, peer-to-peer networks, and decentralised applications, this is possible. This article focuses on the development of a decentralised messaging service-Aloha that operates on a blockchain and makes use of smart contracts in order to provide data security, privacy, scalability, availability, and deployment flexibility. Because of this capability, only those users who have been given permission to do so are allowed to send and receive messages inside the system.
Toplu Taşıma İçin Nesnelerin İnterneti Yazılımlarının Modellenmesi

The Internet of Things (IoT) is a very promising domain and it is one of the leading technologies used in the public transportation in recent years. However, in addition to the heterogeneity and high complexity problems which are usually observed in the development of IoT systems, the specific needs of public transportation domain make the construction of such systems even harder for the public transportation. This paper proposes the use of a domain-specific modelling language (DSML), called DSML4PT, to facilitate the design and implementation of IoT-based public transportation systems. A metamodel is introduced that enables modeling IoT-based applications according to the different viewpoints and leads to the model-driven engineering of such applications for different IoT-based public transportation platforms. Furthermore, originated from this metamodel, design and implementation of the DSML4PT language with including its syntax and semantics definitions are all discussed in this paper. Use of this DSML supports both the design of the IoT-based public transportation software graphically and the automatic generation of the code required for the implementation. Based on the conducted case study, it has been observed that 80% of a public transportation application can be generated only with using DSML4PT.
Developing Web Application For The Student Affairs Department Using The ASP.Net MVC Architecture

The developments in internet technology have led many institutions to renew themselves in various fields, and one of them is educational institutions. Especially for the student affairs departments of universities, having an effective web application has become important with the developing technology. All universities that prioritize development should have a strong and interactive student affairs web application that will not only promote the university but also facilitate the implementation of related student affairs from a design and software perspective. It has been found that the error rate in student affairs significantly decreases with web applications, and the unit performance in terms of access and service to students also significantly increases. In this study, the Student Affairs Department web application was developed based on ASP.Net MVC (Model View Controller) technology. ASP.Net MVC technology enables web applications to be more secure, easier to design, and code. The model-view-controller system has provided convenience in the organization of both the front-end and back-end structures. Microsoft SQL was used as the database, and the Code First approach was preferred as the database method. The Language Integrated Query component was used in the back-end structure for communication with the database. Overall, this design allows for easy updates, optimization, and dynamization of the code, as well as removing repetitions from the code.
Individual and pair representativeness of sampling points selection in interpolation tasks of the heavy metals distribution in the topsoil

The article is devoted to the problem of choosing a representative selection of a subset for an artificial neural network in the tasks of interpolation of the distribution of metals in the topsoil. Environmental data, often used to build artificial neural network models, are datasets at irregular points. The traditional division of the input data into training and test subsets occurs randomly, which transfers to a number of problems. For selection in the training subset, the question of individual and collective representativeness of points is asked, sending them a request for data on the content of the element in the soil in a given area. The most representative in terms of individual representativeness arise with the maximum reference points, their presence in the training subset of the ANN measurement of error and an increase in the correlation between the results of model calculations and natural measurements on the test subset. When assessing the pairwise representativeness of the identified synergy effects, which, when included, achieve high model reliability) and anti-synergy (the parameters of using less information to describe the content of the elements than separately the points of view included in the pair). The various sampling locations have different information and unequal meaning for feature interpolation.
Viable Detection of URL Phishing using Machine Learning Approach

The objective of paper is to detect phishing URLs using machine learning algorithms. Phishing is a fraudulent activity that involves tricking users into giving away sensitive information, such as passwords and credit card numbers, by impersonating legitimate websites. The main objective of this work is to build a model that can accurately detect viable phishing URLs and classify them as either legitimate or fraudulent. This will help to prevent users from falling victim to phishing attacks and protect their personal information. The model will be trained on a large dataset of annotated URLs and will be optimised for high accuracy and low false positive rates. The paper consists of two datasets in which one of the dataset consists of phishing URLs and other datasets consist of features of URLs. The performance of the phishing detection model will be evaluated using various metrics, such as precision, recall, and F1 score. We will also conduct an in-depth analysis of the results and discuss the effectiveness of the approach. This work aims to build a robust model for phishing URL detection using machine learning algorithms. Future enhancements to this work could include incorporating more advanced feature extraction techniques, exploring the use of deep learning models, and expanding the dataset to include more diverse types of URLs.
Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems.
Sensing Users Emotional Intelligence in Social Networks

In this age of constant digital communication and social interaction, people are paying a lot of attention to how users' emotional intelligence affects their interactions and well-being on social networks. This research investigates the application of information systems and telecommunications technologies in the detection and analysis of users' emotional intelligence within the realm of social networks. The concept of emotional intelligence, which involves the capacity to notice, comprehend, regulate, and use emotions in a proficient manner, has significant importance in influencing encounters and relationships in the online domain. This article explores potential models of emotional intelligence based on sentiment analysis of social network data. Self-awareness, self-regulation, intrinsic motivation, and interpersonal connections are based on four principles. These four-dimensional models aggregate four numerical indicators to quantify emotional quotient. This study uses Twitter, a popular social network, to predict emotional intelligence in individuals or groups. This finding assesses users' emotional intelligence using four variables and shows their positive, negative, and neutral sentiments. The program we are developing is based on uploading Twitter datasets and forecasting emotional intelligence using algorithms and tools based on tweets, retweets, and followers, among other scenarios. The four dimensions allow us to feel emotions. Twitter datasets are in text files with JSON data.
Enhancing Impulsive Hatred Detection with Ensemble Techniques and Active Learning

The increasing propagation in recent years of hatred on social media and the dire requirement for counter measures have drawn critical speculation from state run administrations, organizations, and analysts. Despite the fact that specialists have observed that disdain is an issue across different Social media stages, there is an absence of models for online disdain location utilizing this multi-stage information. Different techniques have been produced for robotizing disdain discovery on the web. Here we will begin by giving the current issue that comes the right to speak freely of discourse on the Internet and the abuse of virtual entertainment stages like Twitter, as well as distinguishing the holes present in the current works. At long last, figured out how to tackle these issues. It is a considerably more testing task, as examination of the language in the common datasets shows that disdain needs one of a kind, discriminative highlights and in this manner making it challenging to find. Removing a few exceptional and significant elements and joining them in various sets to look at and dissect the presentation of different machine learning classification calculations as to each list of capabilities. At long last, subsequent to leading a top to bottom investigation, results show that it is feasible to fundamentally expand the classification score acquired.
Smart Resume Analyser: A Case Study using RNN-based Keyword Extraction

The pursuit of a reputable position in a company is a common goal shared by many people. For making this desire into reality their resume is the key to it. Making these resumes is a challenging task for many and modifying it is another time-consuming and burdensome task. Taking these problems into consideration and to make eye-catching resumes we have developed a smart resume analyser that can analyse the user’s resume, it intelligently identifies their skills and qualifications, enabling us to suggest the best-suited job titles for their profile. Furthermore, based on this analysis, our system generates recommendations for optimizing and enhancing the user’s resume, making it more appealing to potential employers. Through the utilization of NLP and ML technologies, our resume analyser provides personalised and effective solutions to job seekers, helping them stand out in today’s competitive job market. The information on the resume can be analysed and understood easily as NLP has the capability to understand and parse the information on the resume and extract the desired information efficiently. With the additional help from Python and its packages like streamlit, pymysql, etc. we can store the extracted data and give a rating based on the analysis of the stored data. After the rating is generated, we recommend some modifications like: adding sentences like my hobbies are, my goal is, add objectives, and add declaration and more. We also suggest a few sources through which the user can enhance their skills.
Feasible Criminal Identification using Image Recognition

Criminal identification using image recognition is a crucial task for law enforcement agencies. It involves identifying and tracking down criminals by analysing their facial features from images and videos. The system aims to improve the efficiency and accuracy of criminal identification while reducing human error. The system will be trained on large datasets of criminal images to ensure high accuracy and reliability. The paper will use publicly available datasets such as the Face Recognition Technology (FERET) and the Labelled Faces in the Wild (LFW) dataset to train and test the model. Metrics including accuracy, precision, recall, and F1-score will be used to assess the system’s efficacy. The results and discussions will focus on the strengths and weaknesses of the model, and potential avenues for improvement. In conclusion, the proposed face recognition system shows promising results in identifying criminals and reducing the burden on law enforcement agencies. The manuscript discusses the objective of the paper, provides an overview of facial recognition technology, and summarizes existing approaches in criminal identification using image recognition.
Motorized Smart Roofing System using IoT for Sustainable development

Climate change caused by human activity has led to abnormal rainfall in India. Agriculture is a crucial source of income for the Indian Government and the abnormal rainfall patterns pose a significant threat to the industry. These fluctuations in rainfall have caused damage to homes, and infrastructure, wash away crops and destroy water sanitation. Unusual rainfalls are also affecting the Rooftop restaurants. The Government, Roof Top restaurant owners, and farmers are all seeking solutions to address the impact of climate change. One potential solution is the development of a Smart Roofing System that can detect rainfall and respond accordingly. The Proposed system would use a Rainfall Sensor to detect rain, which would then send signals to a Wi-Fi-enabled board. The board would then activate dc motors, opening the roof to protect against rainfall. When the Rainfall Sensor detects no rain, it sends a signal to the board to reverse the direction of the motors, closing the roof. The implementation of this Smart Roofing System could help minimize the damage caused by unusual rainfall patterns and help towards the development of a sustainable environment. With a Smart Roofing System in place, roof-top restaurant owners could continue their service during periods of heavy rain, ensuring minimal disruption to their business. Overall, the development of a Smart Roofing System could help protect India’s infrastructure, agriculture, and economy from the adverse effects of climate change with minimal cost.
Traffic Light Detection for Information Systems and Telecommunications using CNN

Information and telecommunications using traffic signals plays major role in computer vision. Distinguishing objects which are in more modest size is a difficult task. We centre around an exceptional case: Detection and the classification of traffic lights in road sees and gives a guidance to the regulator for semi-autonomous and completely self-governing vehicles. We are introducing a profound learning approach for precise traffic signal discovery in adjusting a Single Shot Detection (SSD) approach SSD performs object proposition creation and order utilizing one single CNN. The first SSD battles in recognizing smaller objects, that which is fundamental for Traffic Light Detection (TLD). By our transformations it is feasible to recognize protests a lot more modest than the ten pixels without expanding the input picture size.
DFR-TSD: A Sustainable Deep Learning Based Framework for Sustainable Robust Traffic Sign Detection under Challenging Weather Conditions

The development of reliable and sustainable traffic sign detection under difficult weather conditions, or DFR-TSD, is a key step in creating effective, safe, and sustainable autonomous driving systems. The suggested sustainable framework makes use of deep learning techniques to overcome the drawbacks of the current traffic sign detection systems, especially in difficult weather circumstances like haze and snow. The system uses a sustainable CNN pre-processing step to make traffic signs more visible in photos that have been impacted by the weather, followed by a sustainable pre-trained ResNet-50 model to recognize traffic signs. On the CURE-TSD dataset, which includes difficult weather circumstances such as haze, snow, and fog, the suggested sustainable framework was assessed. The testing findings showed how sustainably well the suggested framework performed in identifying traffic signs in adverse weather. The suggested sustainable framework outperforms previous approaches with a sustainable accuracy rating of 98.83%. The outcomes show that sustainable deep learning methods have the potential to enhance traffic sign identification models' functionality. The proposed sustainable framework’s front end offers a user-friendly interface that enables users to upload test photographs and view the results of the detection. There are four sustainable buttons on the UI for loading the model, uploading test photographs, spotting signs, and seeing the training graph. The Tkinter framework, which offers a user-friendly GUI toolkit that enables developers to quickly design and customize sustainable GUI programs, is used to develop the front end. The suggested sustainable DFR-TSD framework is a potential sustainable option for reliable traffic sign detection in adverse weather due to the sustainable pre-processing step, the sustainable pre-trained ResNet-50 model, and the sustainable user-friendly interface.
Feasible LSTM Model for Detection of Sign and Body Language

The use of sign and body language facilitates communication between hearing individuals and those with hearing loss. They play an important role in facilitating effective human-human and human-computer interactions. It is a visual language composed of hand gestures and facial expressions to identify the meaning conveyed by the signer. Body language detection is the process of analysing the nonverbal cues and gestures used in communication to understand the emotional state and intentions of the speaker. Deep learning methods have recently demonstrated promising results in a range of computer vision tasks, including gesture detection. Using a media pipe holistically, extraction of essential points from the body, hands, and face is possible. TensorFlow and Keras are further utilized to construct Long Short-Term Memory LSTM feasible models that can predict on-screen behaviour presenting an innovative approach for sign and body language detection using LSTM.
Deep Learning based Automated Image Deblurring

Image deblurring is a challenging task that aims to restore a sharp and clear image from a blurred one. This problem is usually caused by camera motion or defocus blur. The objective of this paper is to develop a model that can effectively remove Gaussian blur from an image and improve its quality using deep learning techniques. Automated image deblurring is achieved using deep learning, this approach involves implementing a combination of convolutional neural networks (CNN) and simple auto encoders to train the model on a dataset of blurred and corresponding sharp images. The model is then used to deblur the test images and improve their quality. The paper uses a dataset of blurred and corresponding sharp images to train the model, and the performance of the model is evaluated based on metrics such as PSNR and SSIM. The results and discussions focus on the effectiveness of the model in removing Gaussian blur and improving the quality of the images. In conclusion, the paper demonstrates the effectiveness of using deep learning techniques for image deblurring and provides scope for future enhancements such as incorporating more complex models and exploring other types of blur removal techniques.
Autonomous Driving: Enhancing Mileage, Road Safety with AI

The aim of this paper is to solve the problems in driving by introducing autonomous features into vehicles. An autonomous vehicle is a vehicle that can operate and drive itself without any human interference, with the help of sensors, to sense its surroundings and navigate itself. An autonomous vehicle is equipped with an automated driving system that is designed and trained by humans to allow the vehicle to respond to surroundings in a way that a human would act. In this paper, we develop a program with the help of AI/ML to detect roads, traffic signs, traffic conditions, and Surroundings and act according to them by altering the steering, acceleration, and braking. We plan to achieve this by taking input from a series of sensors and training the model to act accordingly. In this paper, we train neural networks with the help of human behavior. So, the model tries to mimic human behavior and human actions in a given situation.
Age Classification for work sustainability using SVM using Co-occurrence features on Fibonacci Weighted Neighborhood Pattern Matrix

Computer vision systems are increasingly focusing on age recognition from facial images. To solve this problem, In this paper, proposed a method that computes the Fibonacci Weighted Neighborhood Pattern on an image to obtain local neighborhood information, then evaluates Co-occurrence features for work sustainability age classification with SVM classifier. These characteristics show how people’s ages differ. The proposed method has been tested on the FG-Net facial images dataset as well as other scanned images. Experiments showed that the proposed approach outperformed other currently existing methods.