Reviewerly/exHarmony huggingface dataset:
Agent Based Queue Aware Scheduling for Distributed Multicore System

In the current world of big data analytics and huge processing request from the client server system the service requirement from the computing cores has been growing exponentially. In this paper we proposed a novel idea wherein the agent will check the ready queue length dynamically and based on the threshold value comparison further scheduling has been done. There are three different variants of scheduling and load balancing are identified with respect to the queue length. Always the queue length is directly proportional to the number of tasks given to a particular node in the distributed system. Compared to the traditional scheduling algorithms, our approach increases the cores' throughput by 2.5%. In addition, we calculated that as compared to the preceding approaches, the typical hold time for work in the ready state has decreased to 1.25%. The primary benefit of this endeavor is when multiple jobs are submitted to the server then the proposed algorithm speed up the process of getting the jobs executed at a faster rate.
A bert model for sms and twitter spam ham classification and comparative study of machine learning and deep learning technique

With the high popularity of online social networks and with the prosperity of the SMS and Tweets, the increasing number of spam messages has become a severe problem and spammers find these platforms easily approachable to trick users in to spiteful and malicious activities by posting spam messages. The need to block spam messages requires us to develop new spam detection technologies. In our paper we build a spam detector prototype using BERT (Bidirectional encoder and representations for transformer) pre-trained model that classifies messages by understanding their actual meaning and context, and we trained our spam detector model with SMS dataset named V.1 dataset and UtkMl’s Twitter dataset. To test the performance and evaluate our model we used precision, recall and F measure metrics. A comparative study of different machine learning and Deep Learning algorithms and BERT is performed. Here BERT outperforms all other algorithms and is able to achieve an AUC, F1 score and Accuracy of 96.10%,92 and 91.71% respectively.
Eye Detection from Face Images covered in Face-masks using HAAR features

The contagious illness known as COVID-19 made wearing a mask an essential part of daily life. Mask-covered faces cannot be detected by the current eye detection methods. Many biometric identification systems, like iris recognition, depend on accurate eye detection. Thus, in this study, an efficient method using machine learning for detecting eyes of people wearing mask is presented. Haar-cascade classifier is used to implement real-time eye detection from a live stream via webcam. From the live stream, frames are extracted and saved as images. Dataset was prepared by collecting face images of people wearing mask under various background. Haar-cascade classifier which was trained using 2000 positive and 4000 negative images is used to detect the position of eyes. According to the results on dataset, the system could attain an average accuracy of 96.72%.
Machine Learning Model for Temperature and Humidity Automatic Control in Smart Poultry Farm

This study designed the Smart Poultry Farm system model using the Machine Learning KNN (K-Nearest Neighbor) method. The model building is based on the dataset obtained from simple if-then-else statements on real poultry. The iteration test was performed using various iteration numbers of 10, 100, 1,000, and 10,000 using 210 data sets. The K values tested are 1, 3, 5, 7, and 9. The most optimal accuracy value obtained from random samples is 97.36%; this value is obtained in research with two outputs, 10 iterations, and the Kvalue is 3.
Analysis of Secure EHR Storage Methods on Blockchain and Integrating ML to Predict Chronic Kidney Disease

An Electronic Health Record(EHR) is the digital form of the patient’s health data, including vital signs, demographic details, clinical notes, X-rays, medical images, etc. When discussing EHR processing and management, storing this vast information is the main challenge. Although cloud computing’s centralized storage model is one of the best ways to store large amounts of data, it is not secure. Blockchain technology has the potential to revolutionize EHR storage systems and provide data security. Directly keeping the vast EHR data on blockchain may be costly and inefficient. The best method to store the data is off-chain in a Blockchain-based EHR system. Implementing machine learning (ML) in the healthcare industry aims to anticipate diseases earlier so patients can receive higher-quality medical care. Integrating these two disruptive data-driven technologies can highly improve the quality of healthcare. First, this paper analyzes three different off-chain EHR storage methods: Storj, InterPlanetary File System(IPFS), and CosmosDB, based on their Storage and Access Time. From the experimental results, IPFS has the fastest Storage and Access Time. Second, we integrate blockchain and ML technology to predict Chronic Kidney Disease(CKD) from the CKD dataset stored on the IPFS. Finally, using IPFS storage, a record is accessed, and the prediction time is 0.4 sec for detecting CKD or NOCKD is measured.
Performance Analysis of Smart Irrigation System using Fuzzy Logic Method

Indonesia is a country that is rich in types of plants. One of the plants that is often in demand in Indonesia is Chili Plants, but plantations in Indonesia still use a lot of irrigation systems or traditional watering, which is watering only based on seeing how dry or wet the soil is, so that a lot of occurrences occur. crop failure due to improper irrigation or watering. This problem can be solved by making a tool to monitor the humidity in the chili cultivator’s land, by using this tool the water discharge can be adjusted according to the soil moisture. So that not a lot of water is wasted on the chili cultivator’s land. This tool can also monitor the condition of the land for chili cultivators, if the plantation land is dry, the sensor will send a signal to the application that the land is dry and requires water flow, if the humidity on the plantation land is met, the sensor will send a signal to the application so that the water stops flowing, with these parameters in this study apply with fuzzy logic chili irrigation system. This tool can be used as a guide for chili cultivators, to monitorhumidity on the land of chili plant cultivators so that the irrigation system can be monitored accurately and can be done anywhere at any time, without having to make a drain. This results in a high-quality crop.
Autoencoder Image Denoising to Increase Optical Character Recognition Performance in Text Conversion

Document digitization has an important role in helping the company’s activities be more efficient, such as detecting text in invoice document images using optical character recognition (OCR). However, writing in images has many problems, especially tediously saved documents that can cause noise or interference in the picture, resulting in difficultly recognized writing. Our research aims to build an autoencoder for denoising text images and evaluate the OCR’s performance in converting the denoised image into text. The first step in the research is to test the OCR characteristics on the original text image and the text image given Gaussian noise. The next step is to build the optimal autoencoder model for denoising by studying the effect of dataset size and optimizer type. The last step is to test the OCR performance on the denoised text image produced by the optimum autoencoder model. The test results show that datasetsize affects denoising performance and OCR performance. From several autoencoder models compared, the autoencoder with dataset size =40 has the optimum performance, where the MSE values of the model for train and validation are 1277 and 1385, respectively. With images denoised from the optimum model, the OCR performance in converting images into text is 100%.
Image Captions and Hashtags Generation Using Deep Learning Approach

social media are fantastic tools for public communication. Social media has become an integral part of our everyday lives, and an increasing number of individuals use it for marketing and communication. Social networking enables you to demonstrate your skills and knowledge without leaving home. Companies exert significant efforts to make social media more controlled and valuable while avoiding negative repercussions. They accomplish this with artificial intelligence (AI), which enables them to develop unique applications and algorithms. It can eliminate inappropriate information or spam automatically, for instance. The description and hashtags that grab the reader's attention are among the most critical aspects of a social media post's success. Typically, individuals generate multiple captions and hashtags before selecting the optimal content for a post. Occasionally, they employ content writers, which requires time, effort, and money. The suggested method makes correct captions and hashtags using conventional neural networks (CNN) trained on image datasets containing captions
On the Key Schedule of Lightweight Block Cipher

The key schedule, an essential part of the cipher(cryptography), is often neglected during the cipher algorithm design. However, a compromised key schedule leads to the entire cipher's successful attack. In this article, we reviewed the elements to consider when creating an excellent key schedule, proposed a methodology to achieve it, designed a key schedule with the proposed method, implemented the design, and analyzed the result to confirm that it meets the specifications. Our proposed key schedule is specially designed for devices with limited memory size, processing ability, and storage. So, this article's suggested method and design were done to achieve a secure key schedule using minimal resources, especially in hardware implementation.
Quantitative Approach to Automated Diagnosis of Malaria from Giemsa-Thin Blood Stain using Support Vector Machine

Automating the process of malaria diagnosis is very crucial; malaria is a deadly disease with an annual infection rate between 300–500 million and a death rate of 1 million yearly. The diagnosis approach is manual and is subject to human error. In this current work, we automate the process of diagnosis and provide results in quantitative form with a diagnostic tool deployed on a web server to eradicate limiting the access to the service to a physical location. The input to the developed diagnostic tool is a Giemsa-stain blood image which undergoes image processing using Otsu segmentation to identify regions of the red blood cells, and a trained SVM classifier iterate through the red blood cells to determine the infected ones. The trained SVM achieved accuracy and precision of 88% and 87% against the validation dataset. The count of infected red blood cells against total red blood cells in the image is used to compute the quantitative result which is the level of severity and number of infected cells per uL of blood, based on the World Health Organization (WHO) standard. A couple of Giemsa-stain blood images were uploaded for diagnosis, our web-based diagnostic tool achieved 90.55%, 85.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 100% for average count (both total red blood cells and total infected red blood cells in the processed Giemsa-stain blood images) accuracy, severity classification accuracy and negative test accuracy respectively. The system's average time to complete a diagnosis is 2.2824 seconds, this is a very short time which will create a near-real-time experience for the users of the service.
Opinion mining analytics of IoT ecosystem by Profile of Mood State with Logistic Regression

Internet of Things continues to redefine modus operandi across diverse socioeconomic and professional domains thereby generating an un-abating global discuss on the adoption and functionalities of smart devices. Since emotions play a critical role in decision making according to the psychological domain of emotion science, the paramount importance of periodic delineation of stakeholders' mood is imperative for policy makers. Whereas opinion mining analytics of IoT discussions have returned state-of-the-arts, there is need to address germane factors seldom factored into existing literatures. This study therefore consolidates on current frameworks through a bi-modal descriptive and content-based analytics of IoT ecosystem for detecting key mood domain and the BlueCheckCredibility status of IoT tweeters using Profile of Mood State and Nomogram-based analytics. With a 99.5% precision rate by Logistic regression model, social characteristic attributes of acquired ethnographic data points turns mutually exclusive to the credibility status of IoT opinion molders while tweet properties contributes higher discriminative tendencies for identifying negative IoT emotions. The impact of Internet of Things on data science is likewise unraveled through bi-gram content analytics to identify topical discussions encapsulated in the acquired tweet corpus.
Towards Finding An Optimal S-box For Lightweight Block Cipher

Implementing conventional cryptography like Advance Encryption Standard (AES) requires hardware resources beyond what constrained devices like RFID tags can offer and still perform their primary function. This limitation gave rise to lightweight cryptography to secure constrained devices. The block cipher is the branch of the cryptography scheme that is mostly considered for lightweight cryptography. A key component of the block cipher largely responsible for its security, implementation cost, and efficiency is the Substitution Box (S-box). Most of the time spent in block cipher development is used to find the best S-box with high resistance against known cryptanalysis attacks. However, finding the optimal S-box among the huge possible permutations has always been challenging. The wrong choice of S-box has led to the exploit of some cryptography (cipher). This paper focuses on finding an optimal 4-bit x 4-bit S-box for the lightweight block cipher that will guarantee the cipher security against differential and linear cryptanalysis. We achieved our aim by considering research findings from 1990 to date, to determine the optimal S-box properties and their best values. The S-box properties include and are not limited to differential uniformity, Linearity, and “BOGI Applicability”. Differential uniformity measures resistance to differential attack. S-box Linearity measures resistance to linear cryptanalysis attack. And “BOGI-Applicable S-box” determines if an S-box can implement the “BOGI Strategy”. The “BOGI Strategy” is a strategy that synchronizes the design of a block cipher permutation layer with its S-box to eliminate the S-box weakness. The concluded best values for the S-box characteristics were incorporated into an algorithm and implemented using the C++ programming language. Sample optimal S-boxes were generated using the suggested metric values. The generated S-boxes comply with the “BOGI strategy”, which eliminates the S-box weaknesses that cryptanalysts would otherwise have exploited.
Comparative Analysis of the Performance of Various Support Vector Machine kernels

Support Vector Machine (SVM) in dealing with a classification problem, separates classes using decision boundaries with the primary objective of establishing a large margin between support vectors of the respective class groups; it utilizes kernels to achieve non-linear decision boundaries. This current work examines the performance of four SVM kernels (Sigmoid, Linear, Radial Basis Function (RBF) and Polynomial kernel functions) in addressing classification problems using two datasets from two domains. The two datasets are the Knowledge Discovery in Dataset (KDD) and a set of features extracted from voiced and unvoiced frames. The Polynomial kernel function had the best classification performance on the KDD dataset with accuracy and precision of 99.77% and 99.8% respectively but recorded the worst performance against the voice-feature dataset with an accuracy of 74.96%. Inductively, the polynomial kernel can be best suited for some classification datasets but can return the worst classification performance on another classification dataset. The RBF shows consistent high performance across the two data domains with accuracies of 96.04% and 99.77% and can be considered a general-purpose kernel guaranteed to yield satisfactory classification performance regardless of the dataset type or data domains. The performance of polynomial kernels over the two separate datasets supports the “No Free Launch Theorem”, which when applied to machine learning, means that if an algorithm performs well over a class of problem, it may have worse performance on other class of problem. This implies that there might not be one specific machine learning algorithm that gives the best possible performance for a set of problems, it is therefore important for researchers to try out various algorithms before concluding on the best possible result on any dataset.
Development of Smart (Light Dependent Resistor, LDR) Automatic Solar Tracker

In this study, a Smart (Light Dependent Resistor, LDR) Automatic Solar Tracker is intended and successfully developed. It was developed with unique design criteria such that it instantly aligns the solar panels position perpendicular the position of the sun, resulting in a 42% increase in efficiency of the generated energy when compared to a fixed axis solar panel. A low-cost solar tracker set-up is uniquely set up to act as the solar radiation sensor/detector which is used to rotate the solar panels via the electric motors to position the panels at a specific angle determined by the light dependent resistor of the tracker system. A microcontroller Arduino Uno is used as the microcontroller unit, and the Analogue Digital Converter ports are used to interconnect the sensor units. To connect the solar arrays, the Direct Current motor driver ULN 293D is used to rotate the solar panel at the highest solar power angle. The digital model was conceptualized on Autodesk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Fusion™ 360, using four Light Dependent Resistors. The performance analysis for the simulated model and developed system shows that the maximum current drawn by the solar tracking sensor is less than 0.72 mA. “Arduino UNO”, Proteus 7.6 ISIS” and “Code Vision AVR” are used to write the program code and to transmit it to the Arduino Uno microcontroller for parameter estimation.
Developing an Interoperable Crime Management System

Enhancing the interagency collaborative approach to crime management in developing nations cannot be overemphasized. This study presents a computerized system posed to enhance interagency collaboration in combating crime in Nigeria. Interagency Crime Management System (ICMS) is a web-based application that accepts input through an Application Program Interface (API) provided to the individual applications being used by different security agencies under the Federal Government of Nigeria. The system is based on a relational database with schemas very close to those of law enforcement agencies. The system was designed to accept data in the same input format from each of these agencies. These are the criminal code, name of criminal, nationality, state of origin, the local government of origin, age, nature of the crime committed, location of the crime, a section of the penal code violated, current status as at when reporting, officer-in-charge, and reporting agency. To achieve this, the study adopted the Structured System Analysis and Design Methodology (SSADM). The process starts with designing a framework for ICMS, then furthered by developing an algorithm for ICMS, and Laravel was employed for the implementation of the algorithm developed. The database management software employed for the application, PostgreSQL, stores every detail of the crime using a unique reference number for every record stored in the database to facilitate easy retrieval. This system also provides a search facility to query the database about information relating to the various categories of crime, criminals, and crimes that are common in particular geographical location and provide analytics tools such as charts for research and analysis purposes.
An Improved Password-authentication Model for Access Control in Connected Systems

Data breaches and information theft are among the major challenges confronting the legitimate information system user in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. In recent times, following the deepening of the digital space, mobile computing devices have become convenient resources to drive most day-to-day socioeconomic efforts. These devices are valuable as they host sensitive data, which if compromised may result to economic chaos. Consequently, strengthening access control measures becomes crucial. This study focuses on the strengths of the traditional password authentication mechanisms used on information systems. It identified the weaknesses and proposes a two-part improved password authentication model. The object-oriented methodology is adopted to analyse the components of the proposed password authentication cycle including the flow of activities, and the security relationships. Following a detailed analysis, we specify a simple algorithm to guide the implementation of the model.
Implementation of Malaria Parasite Detection and Species Classification Using Dilated Convolutional Neural Network

Malaria is an infectious disease caused by a bite of an Anopheles Mosquito which has caused a lot of death. Diagnosis of malaria is made by examining a red blood cell of an infected patient using a microscope, which takes time and requires a qualified laboratory expert to examine, read and interpret the results obtained. Convolutional Neural Network (CNN) has played important role in image classification; however, it has exhibited some problems in consuming computing resources which is one of the limitations of CNN. To reduce this problem, this paper presented a Dilated Convolution Neural Network for malaria parasites detection and species classification using blood smear images. A direct classification was carried out to detect infected and uninfected malaria parasites. Subsequently, species classification was carried out using 3 convolutional layers and Convolution2D for convolution operation while a dilation rate of 2 was used for the convolution layers. The model was trained with a publicly available dataset of 27699 images with a performance accuracy of 99.9% for parasite detection and species classification of 99.9% for falciparum, 64.6% for Malarie, 39.1% for Ovale and 37.3% for Vivax.