Reviewerly/exHarmony huggingface dataset:
Estimation of Solar Power Generation using Ensemble Learning and Algebraic Combiners

As a result of today's pollution, renewable and ecofriendly energy sources have received increased attention in recent years. Before constructing a new solar power plant, it is necessary to study the breakeven point and power generation. So far., solar power estimation models have been developed to guarantee the viability of a new power plant. This paper presented a technique for estimating solar power based on ensemble learning and algebraic combiners. Three different models, i.e. Nearest Centroid Classifier (NCC), Support Vector Machine (SVM), and Decision Tree Classifier (DTC), are also studied and evaluated in this work. Compared to the other techniques, the suggested method with the Weighted Sum Rule (WSR) delivers the most accurate benchmark result, with an accuracy of 99.915%.
An Investigation on Multi-step Bitcoin Prediction based on LSTM and GRU

This work aims to conduct an investigation on 2-, 5- and 10 –output-step with 5 fixed input-step Bitcoin price prediction, using gated recurrent unit (GRU) and long short-term memory (LSTM). The effects of using 2 and 6 layers with regard to LSTM and GRU (2L- and 6L- LSTM and GRU) on the prediction performance are also examined. Two datasets with statistically distinct features, e.g., rather monotonic and non-monotonic, consecutively referred to Binance and Poloniex, the world's leading crypto trading and cryptocurrency exchange platforms are experimented for intensifying the investigation. Prediction performance evaluations include root mean square error (RMSE) and mean absolute error (MAE) along with Pearson correlation coefficient (Corr) are employed here. The best averaged results of all the measures are generated by 2L-GRU. 0.9873, 0.9777 and 0.9593 Corr means are generated by 2-, 5- and 10- output-step; and 0.9758, 0.9575 and 0.9259 Corr means are resulted by the same numbers of steps, respectively for Binance and Poloniex. Overall prediction performance based on more-simpler, monotonic Binance data is rather better than more – complicate, non-monotonic Poloniex data.
A Study on GCN using Focal Loss on Class-Imbalanced Bitcoin Transaction for Anti-Money Laundering Detection

Due to a complexity of Bitcoin transaction graph and a class-imbalance difficulty as only a few known labels of blockchain illicit activities exist, graph convolutional network (GCN) using focal loss constraints is applied in this paper for anti-money laundering detection. The GCN, composed of spectral graph convolution layers suitably handles graph structured input data; whereas the focal loss (FL) has been applied to resolve the severe problem of class-imbalance by reducing the loss weight of easy-to-classify samples. In addition, the licit and illicit detection performance comparison on Elliptic dataset among 2, 4 and 6 numbers of GCN layer (GCN -2L, -4L and -6L) relying upon focal loss relative to cross-entropy (CE) loss is assessed. Assessment criteria depend on precision, recall, F1 and accuracy scores, averaged through 10-fold cross validation. A Bitcoin transaction graph, so-called Elliptic dataset derived from Bitcoin blockchain is experimented to detect illicit transactions. The results indicate insignificant difference of accuracy rate between GCN -2L, -4L and -6L models using FL and those employing CE. However, apparently and interestingly, illicit transactions are 4.78%, 5% and 6.20% more correctly predicted by using FL than CE, consecutively relying on GCN -2L, 4L and 6L. Illicit transactions are 4.94%, 5.31% and 7.84% less misclassified by FL than CE.
How many appropriate added synthetic data to improve semantic segmentation?

Nowadays, autonomous vehicle technology plays a more important role in our lives. Many people pay attention to driverless cars, but, in fact, every industry needs technology that increases safety. But the problem is that the data must be diverse. We proposed a data synthesis in which the number of the two sources needed to be a reasonable ratio. In this paper, the ratio between synthetic and real data is observed experimentally with clearly and noise injection data in the Yolact and Detectron2 model. In the benchmark test, the optimal ratio is 3.5 and 2.5 for Yolact and Detectron2, respectively. Moreover, provided that training data with mixing data method provide better accuracy than one data type training method.
Rad-Former: Structuring Radiology Reports using Transformers*

Several professional societies have advocated for structured reporting in radiology, citing gains in quality, but some studies have shown that rigid templates and strict adherence may be too distracting and lead to incomplete reports. To gain the advantages of structured reporting while requiring minimal change to a radiologist's work-flow, the present work proposes a two-stage abstractive summarization approach that first finds the key findings in an unstructured report and then generates and organizes descriptions of each finding into a given template. The method uses a large manually annotated dataset and a taxonomy and other domain knowledge that were prepared in consultation with several practising radiologists. It can be used to structure reports dictated by radiologists and as post- and pre-processing steps for machine-learning pipelines. On the subtask of label extraction, the method achieves significantly better performance than previous rule-based approaches and learning-based approaches that were trained on automatically extracted labels. On the task of summarization, the method achieves more than 0.5 BLEU-4 score across 8 of the 10 most common labels and serves as a strong baseline for future experiments.
Utilisation of Solar Rooftops for Energy Reduction: a case study for Airport Rail Link

This paper proposes the solution to energy reduction in a traction railway power supply system using solar PV rooftop installations at train stations. The train operation and energy consumption are simulated in MATLAB program using a case study of the Airport Rail Link (ARL) commuter line in Bangkok, Thailand. The station roof areas are estimated from the satellite view of Google Map. The result shows that a 28 percent reduction in daily traction energy consumption can be achieved, regardless of the power losses in the catenary system and traction substation. Therefore, the energy cost from the grid is also reduced. This considerable amount of the saved energy is worth considering feasibility study in terms of return on investment.
A Lightweight Blockchain Framework for secure transaction in resource constrained IoT devices

Internet of Things (IoT)-based applications are making a big impact in various sectors of our daily life, like smart cities, smart healthcare, smart industries, etc. IoT networks usually require a centralized server or certain authorities to manage and control devices scattered over the network. As a result of centralized management, IoT network security is put at risk. By integrating blockchain technology with IoT, we can create a decentralized and secure network. However, traditional blockchain implementation on IoT devices is not the best solution. It requires high computational capacity to achieve consensus over data communicated among devices, which is not feasible for resource constraint IoT devices. Furthermore, the data generated by IoT devices is massive, making it impossible to keep in blockchain after a certain point. Several models have been proposed in the literature to address these challenges. In 2021, Fusion Chain was proposed as a lightweight blockchain framework that was shown to be the lightest of all models. However, this model is not secure. In this paper, we highlight the various security issues within this framework. We then propose a secure version of Fusion chain, termed Fusion Chain-S, that overcomes these security issues and also provides enhanced storage and access policies for every data element. Also, to make it fault-tolerant, we have used COAP (Constrained Application Protocol) as an integrity channel. We provide a comprehensive security analysis of our framework and prove it using BAN logic. To the best of our knowledge, our improved model is the most lightweight and secure among the other peer models.
Blockchain-based Secure Storage and Management of Electronic Health Record using a Smart Card

The use of Blockchain for consistent storage, management, and sharing of healthcare records has received a lot of recent attention as a more reliable and dependable alternative to cloud-based storage. With the potential of Blockchain technology in the health sector being explored, its practical applications in real-world healthcare scenarios on a large scale are facing challenges like scalability, the authenticity of records, and management of dynamic consent of the patients for data access and sharing of medical records. The paper describes the implementation of a secured and efficient system for the storage of medical records on the Blockchain. The proposed implementation focuses on scalability, the authenticity of medical records, data privacy, and controlled access and uses Proof-of-Authority as a consensus algorithm on a Permissioned-Blockchain and two-factor authentication through a smart card. The paper also introduces a constant-time searching algorithm for faster records retrieval of Electronic Health Records. While the Proof-of-Authority consensus mechanism resolves the scalability issues, the smart card acts as the access control token, that ensures data privacy and makes it convenient for the users to operate.
A New Architecture of Augmented Reality Engine

Augmented reality (AR) technology has been a rapidly developing research hotspot in the past ten years. AR combines computer-generated virtual scenes with the real world to achieve sensory enhancements beyond reality. As the middleware between the operating system layer and the application layer, the AR engine is a platform-based underlying technology and the cornerstone of building an augmented reality ecosystem. In this paper, a new AR engine architecture is proposed, and the core modules such as architecture composition, data management, algorithm integration, and multi-platform support interfaces are described in detail. Also, Nanhu AR engine (NHAR) is developed based on the new architecture. And an application is developed using NHAR to test marker-based tracking and marker-less tracking function of the developed engine.
An Effective approach for preparation of News Summary using web scraping techniques

Due to the Internet’s quick development and far reaching use, network media has turned into another window for people to grasp the rest of the world. News is a medium through which people might find out about their environmental factors, yet a huge number of reports are distributed consistently on the Internet. A major need in individuals’ lives is to sort out some way to get the news things we want from the web rapidly and definitively. This strategy tries to assemble news from specific sites and give it to guests in a reasonable and compact way. Clients might utilize specific watchwords to find news that they are keen on, which takes into account personalization. The viable advantage of this technique is that it gives quick, proficient, and simple admittance to news that individuals care about, need, and are keen on.
A Survey On Text Summarization in Urdu Language using Machine Learning Techniques

The principal commitment of this paper is to analyze and understand the various text summarization methods. We initially present a few ideas related to the extraction of particular text from the. Specifically, a few difficulties in extraction of summarization of text and various methods presented. Additionally, this paper concentrates on Urdu Language summarization and issues which can be faced by Urdu Language summarization. The challenges examined are nonexclusive and relevant to each conceivable situation in text summarization analysis. Then, existing technology of extractive synopsis procedures are examined that attention on the distinguished difficulties. This paper presents a great understanding for text summarization techniques summarization there since 40 years. This method gives the best solution to the problem by suppressing the content and giving important summary of algorithm is easy to applicable. In this method it removes the extra sentences which does not make any sense which improves informative content.
Review Paper on Facial Recognition Techniques

In this review paper, we focus on facial recognition techniques that use machine learning(ML) algorithms to examine facial features and identify individuals. These techniques include PCA, LDA, ICA, SVMs, and ANNs. Each of these methods has its own benefits and drawbacks, and the best approach for a particular application will depend on factors such as the accuracy and speed required, the type of data available, and the specific use case. In tzhis review paper, we provide an overview of these techniques and discuss their capabilities and limitations, as well as the challenges and opportunities associated with their use for facial recognition. We also consider the ethical and legal implications of facial recognition and the need for responsible and transparent use of technology.
The Role of Artificial Intelligence in Education

The paper depicts the design of the Intelligent Tutoring System for computing to assist students learn Computing. The essential thought of this tutoring system is an orderly presentation into the idea of computing. The system presents the subject of computing and manages naturally created problems for the student to understand. The Tutoring System incorporates three fundamental model architectures: Domain knowledge model architecture, student model architecture, and Pedagogical Module Architecture. Utilizing the domain knowledge module architecture, the ITSs ought to have the option to take care of issues concerning the academic module which is given to the students. The incorporation of cognitive science concepts into ITSs culminated, is what currently considered an intelligent tutoring program. Such structures provide knowledge of pedagogical methods and the knowledge of the student as a pedagogical module and a student module, in addition to the information about the teaching content. One of the limitations that influenced the development of ITS is the difference in time zone. ITS development exceptionally relied upon the teamwork of the project team. The members of the team can identify and remove the bugs in codes via common cooperation, but the time zone difference can affect the bug removal process because of changing time plans.
Identification of Gender and Age using Classification and Convolutional Networks

Today’s technology advances rapidly. A person’s face is extremely important in helping to recognise them specifically. When seen from a human viewpoint, it is fairly simple to determine a person’s gender and age just on visuals, but when viewed from a computer perspective, it is not possible. The main goal of this project is to create an algorithm that accurately predicts a person’s age and gender. HAAR cascade is one of the most used methods. Using HAAR Cascade, create a model for this project that can determine a person’s gender. Different male and female photos that were both positive and negative were used to train the model’s classifier. Various face traits are taken off. The input image’s gender will be determined by the HAAR Cascade classifier with its help. Deep Convolution neural network is used in it. It functions well even with little data. Use the Caffe deep learning framework for the age estimation challenge. Caffe offers extendable code and expressive architecture. Caffe can handle more than 60M images per day. It is one of the quickest convent implementations available as a result.
Degeneration of the Grover search algorithm with depolarization in the oracle-box wires

Grover’s search algorithm and similar techniques are widely used in quantum information science. Communication lines with the so-called oracle are one of inevitable vulnerabilities of quantum search. The impact of localized dephasing and amplitude damping on Grover’s algorithm had already been discussed in recent literature. In this paper, we study the influence of depolarization in the oracle-box wires on the search process. It is shown that even low level of noise is sufficient to degenerate Grover’s algorithm. Complementarity relations between the relative entropy of coherence and the success probability in the presence of depolarization are studied as well.
Quadcopter Navigation using Telemetry Data

In this project, we demonstrate the usage of telemetry data implied on a Quadcopter. A Quadcopter (also known as Drone or UAV) is controlled automatically by an embedded system known as the “Flight Control System.” Sensors, GPS and a telemetry system are coupled with an airframe and computer system in this scenario. These components must perform autonomously in various missions and situations while being supervised by a ground base station or controller that serves as a remote transmitter. UAVs can also be effectively employed in so-called “D-cube” missions, which are more explicitly characterized as Dangerous, Dirty, or Dull.
Optical Character Recognition Technology using Machine Learning

The creation of software that is capable of performing OCR in multiple languages is the goal of this project. The primary objective of optical character recognition (OCR) software is to convert pre-existing paper documents or picture files into digital versions that can be edited. Character and word detection are the two stages that are involved in the operation of multilingual OCR software. In the case of a method that is more complex, an OCR will additionally operate on the detection of sentences in order to maintain the structure of a document. Our project’s goal was to better manage some of the more complicated recognition issues, such as distinct language alphabets, by better understanding, utilising, and improving the open source Optical Character Recognizer (OCR) software. This was our project’s primary objective.
An Implementation of Blockchain Technology in Combination with IPFS for Crime Evidence Management System

Crime is an unlawful act and is punished by the authority, in order to prove crime, evidence is necessary. Evidence obtained from crime scene is crucial as it act as proof of crime, Digitalization of evidences is need of hour. During the entire process of investigation heterogenous format of data is generated and integrity of the sensitive data has to be maintained as sensitive data passes through the various levels of intermediaries forming Chain of Evidences (CoE). Evidence needs to be tramper proof and should be protected from any kind of alterations. In order to build strong system with immutability, integrity, and legitimacy features blockchain technology is more suitable. The digital evidence can be transferred in a transparent way between the parties involved without any central authority using blockchain technology. This paper focuses on how blockchain based solutions can help in building a strong secure system. The system is implemented using Ethereum platform to achieve integrity, immutability transparency as well as tampering can be identified by any one at any time.
A Systematic Investigation of Cyber Harassment Intention Behaviors and Its Impacts on Social Media Platforms

Several technologies have been able to fundamentally affect our lives in just a few decades by integrating themselves into our society. News and opinion are now mostly shared on social media. There are both positive and negative aspects to this. Nowadays, practically everyone has a smartphone or other internet-accessing gadget. Modern technology becomes available to kids at progressively younger ages. The majority of them use social media, which contributes to the rise of cyber harassment and the negative aspects of the internet. Due to the ease of access to these platforms, cyberbullying is prevalent on social media. Moreover, when dealing with others, the perpetrators have the ability to remain anonymous. Cyberbullying statistics for 2022 highlight the alarming incidence of harassment in the virtual world, and they are disturbing. According to these data, Cyberbullying sufferers are more than twice as likely to self-harm or entertain suicidal thoughts as a coping mechanism. Anxiety and sadness might develop in certain victims of cyberbullying. Psychologists believe that social media has contributed to an increase in stress and depression over the last decade. On the other side, the most typical targets for online bullies are young children, women, and individuals with non-traditional sexual orientations. Students are often unable to concentrate on their studies because they are afraid of the trolls. As a result, this Research Essential focuses on Cyber harassment statistics and facts in order to raise awareness among parents, students, and authorities worldwide and reveal alarming facts about virtual harassment, the intention of the harasser, its impact, the relationship between victim and harasser, and its many different shapes and forms.
Information Extraction From Text Messages Using Natural Language Processing

In unstructured text, process of looking for patterns is called text mining. Finding particular elements in documents written in natural language is a related task of information extraction (IE). Text mining has grown as an importance in study field. It is discovery of the new and past undiscovered information by computers from a variety of hand-written sources automatically. We currently live in a time marked by extreme stress and mental illnesses. A greater number of people are committing suicide as a result of the excessive levels of stress and strain because they tend to increase the amount of people who exhibit risky inclinations. Family conflict, work dissatisfaction, health issues, etc. are common sources of stress. In the modern computing environment, people are pleased to express their opinions and sentiments on social media with friends and family members by using tools like electronic communication. The suitable text mining technique can increase efficiency and cut down on the time and effort needed to retrieve relevant information. The Natural Language Processing technique which is used to extract important information from text messages is briefly discussed and analyzed in this work.
Fake News Detection Using Machine Learning

The emergence of the World Wide Web and the quick uptake of social media platforms (like Facebook, Instagram and Whatsapp) have made it possible for information to be disseminated in ways that have never previously been seen in the history of humanity. Online hiring has changed the hiring pattern. In particular, putting job adverts on corporate websites and career portals includes looking for a sizable pool of qualified candidates worldwide. Unfortunately, it has become yet another forum for scammers, which threatens applicants’ privacy and harms the reputation of companies. The topic of detecting recruiting fraud and scams is addressed in this case study. Three machine learning models are used in the construction of an effective recruitment fraud detection model, which includes a number of significant organizational, job description, and kind of remuneration features. The proposed system uses different Three Machine learning approaches like Support Vector Machine, Random Forest, Naive Bayes Classifier to determine whether a job posting is genuine or false. We extracted features from data using two techniques: Term Frequency-Inverse Document Frequency (TF-IDF) and Bag-of-Words (BoW). As a result, three models achieve decent results. An ensemble model is created by training three independent machine learning models using different segments of samples and then using a simple majority vote of the three models to determine the final predictions. As a result, three models achieve decent results and an accuracy of more than 98.18% have been achieved using Random Forest Model.