Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Short-Term Memory and Word Embedding For Sentiment Analysis of User Review

Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation also affects the performance of sentiment analysis models. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis, it is necessary to use the right labeling method and good text representation to be used as an embedding layer. This study proposes sentiment labeling using Lexicon and Long Short-Term Memory (LSTM) as well as used FastText as embedding words in sentiment classification. As a corpus, the InSet Lexicon Dictionary is employed for feature extraction. The sentiment data used is the reviews given by users on several applications provided on Google Play. The results showed that the LSTM network using Word-embedded FastText with a dimension of 300 words received a small error value of 0.111 with an accuracy of 95.55% for data labeled based on Lexicon.
Beneficial bacteria stimulate host immune cells to counteract dietary and genetic predisposition to mammary cancer in mice

Recent studies suggest health benefits including protection from cancer after eating fermented foods such as probiotic yogurt, though the mechanisms are not well understood. Here we tested mechanistic hypotheses using two different animal models: the first model studied development of mammary cancer when eating a Westernized diet, and the second studied animals with a genetic predilection to breast cancer. For the first model, outbred Swiss mice were fed a Westernized chow putting them at increased risk for development of mammary tumors. In this Westernized diet model, mammary carcinogenesis was inhibited by routine exposure to Lactobacillus reuteri ATCC‐PTA‐6475 in drinking water. The second model was FVB strain erbB2 (HER2) mutant mice, genetically susceptible to mammary tumors mimicking breast cancers in humans, being fed a regular (non‐Westernized) chow diet. We found that oral supplement with these purified lactic acid bacteria alone was sufficient to inhibit features of mammary neoplasia in both models. The protective mechanism was determined to be microbially‐triggered CD4+CD25+ lymphocytes. When isolated and transplanted into other subjects, these L. reuteri‐stimulated lymphocytes were sufficient to convey transplantable anti‐cancer protection in the cell recipient animals. These data demonstrate that host immune responses to environmental microbes significantly impact and inhibit cancer progression in distal tissues such as mammary glands, even in genetically susceptible mice. This leads us to conclude that consuming fermentative microbes such as L. reuteri may offer a tractable public health approach to help counteract the accumulated dietary and genetic carcinogenic events integral in the Westernized diet and lifestyle.
Scenario Generation of Renewable Energy Resources Based on Denoising Diffusion Probability Models

As renewable energy sources continue to gain a stronger foothold in active distribution grids, the challenge of managing uncertainty in their integration has grown more pronounced. This paper introduces an approach for generating scenarios of renewable energy based on a denoising probability generation model. By leveraging historical datasets and discerning the error relationship between observed and predicted curves, this method enables the derivation of a probability distribution of prediction errors for renewable energy output curves. This addresses the reliability needs of the power system when dealing with uncertain situations. Various static metrics, alongside the operational results of a 33-node IEEE distribution network, demonstrate the efficacy of this scenario generation approach in producing high-quality scenarios. As a result, it enhances the reliability of distribution grid operations and outcomes.
Advancing Crime Analysis and Prediction: A Comprehensive Exploration of Machine Learning Applications in Criminal Justice

The criminal justice system faces the challenge of effectively combating crime while respecting the rights of individuals. In recent years, machine learning has emerged as a powerful tool to assist law enforcement agencies, legal professionals, and criminal justice systems in addressing this challenge. This research study explores the application of machine learning techniques to various aspects of criminal analysis, including predictive policing, crime forecasting, suspect identification, and risk assessment. In this study, different data sources are used to train and evaluate the learning model, including criminal history data, image analysis, and social media content. The study uses a variety of algorithms, from statistical models to deep learning, to extract insights and patterns from data. This research focuses on the technological aspects of machine learning and decision-making in criminal investigations. The results of our analysis demonstrate the potential of machine learning to increase the effectiveness and efficiency of criminal justice. Predictive police models identify crime hot spots with a high degree of accuracy, allowing law enforcement to allocate resources. Crime prediction models provide insight into preventing future crimes. Crime prediction models provide insight into preventing future crimes. Suspect detection and risk assessment models assist in crime investigation and offender rehabilitation. This research study also emphasizes the importance of ethical considerations in deploying machine learning solutions within the criminal justice system. The study discusses the issues related to bias, fairness, and privacy, emphasizing the need for transparent and accountable algorithms.
An Educational Tool for Browsing the Semantic Web

The Semantic Web is an extension of the current Web where information is represented in a machine processable way. It is not separate from the current Web and one of the confusions that novice users might have is where the Semantic Web is. In fact, users can easily encounter RDF documents that are components of the Semantic Web while they navigate among Web pages. In addition, the number of RDF documents on the Web is ever-increasing and a lot of educational ma- terials in RDF format become available on the Web. In this paper, we propose a simple Semantic Web browser by which non-expert users such as secondary school students who do not know tech- nical details about the Semantic Web can browse through HTML documents and RDF documents.
Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

This paper presents spatial mathematical model in order to identify sites for the wind farms installment which can have significant support for the planners in the area of strategy and management of wind power use. The suggested model is based on combined use of Geographical Information Systems (GIS) with multi-criteria techniques of Best-Worst method (BWM) and MultiAttributive Ideal-Real Comparative Analysis (MAIRCA). Rough numbers and fuzzy logic are used to exploit uncertainty during data analysis in spatial mathematical model. The model is applied on the case study. Rough BWM model is used to determine weight coefficients of the criteria and rough MAIRCA method is used to rank separated sustainable locations. The implementation of MAIRCA method has shown that the location L3 is the most suitable for the wind farm in the area covered in the case study. Therefore, the suggested spatial mathematical model can be successfully used to identify the potential suitable sites for the wind farms in other areas with similar geographic conditions.
Knowledge discovery in deep blue

Deep Blue was the first chess computer to defeat a reigning human world chess champion in a regulation match. A number of factors contributed to the system's success, including its ability to extract useful knowledge from a database of 700,000 Grandmaster chess games—a process that has implications for any non-chess knowledge-discovery application involv-A vast database of human experience can be used to direct a search.
Genotypic diversity of Dictyosphaerium–morphospecies (Chlorellaceae, Trebouxiophyceae) in African inland waters, including the description of four new genera

Trebouxiophytes of the Dictyosphaerium-morphotype from inland waters of Africa were studied using a polyphasic approach of SSU and ITS rDNA phylogeny, secondary structure of the ITS and observations made with a light microscope. Although the morphological criteria for differentiating species and genera are scarce, the genetic diversity of these algae was very high. Based on our genetic analysis findings, we described four new genera containing five new species: Compactochlorella dohrmannii, Compactochlorella kochii, Kalenjinia gelatinosa, Marasphaerium gattermannii and Masaia oloidia. Diversity and distribution of Chlorella-related colonial chlorophytes in the tropical and temperate zones were compared and discussed.
Sentiment-Score Analysis of P2P Lending Industry on Android Applications in Indonesia

In 2022, P2P lending is a rollercoaster ride, upside down, characterized by the lack of profit accounting in the sense that it always makes a loss. The changes in the P2P lending industry in 2021 and 2022 need to be analyzed. The opinions of users of peer-to-peer (P2P) lending related to the Importance-Performance Analysis (IPA) quadrant are discussed in this study. There are various steps involved in achieving this. First, get information from Google Play Store online reviews. Then, employ Latent Dirichlet Allocation (LDA) methods to hunt for important qualities or critical performance problems. After comparing the mean score and mean compound or sentiment weight, construct IPA maps. All P2P lending platforms are registered with the Financial Services Authority (OJK) and use the Android operating system, of which 52 applications are being case studies to capture the p2p lending industry. The purpose of this study was to assess whether the features or services were perceived as good, sufficient, over the top, or even no important in P2P lending industry. The findings of this study demonstrate how stakeholders can benefit from the methodology when assessing the P2P lending market. Through a comparison of 2021 and 2022, it becomes evident that discussions concerning delays in the payment of investment returns by lenders will come to the forefront in 2022. This study can help an economic analyst or researcher in conducting extensive P2P lending industry evaluations.
Parasitized Malarial Infected Cell Detection through Variance Scaling Kernel Initializer Conv2D

Medical pathologists physically examine Plasmodium falciparum parasites infected blood cells that have been infected through a magnifying glass as part of the traditional diagnostic process, which is time-consuming and liable to mistakes. CNN and advanced technology have recently been used to conduct research on optimizing this procedure to identify orientation and geometrical information from blood sample particle images. With this view, this research recommends Variance Scaling Kernel Initializer Conv2D (VSKIC) Sequential model for predicting the infected malaria cell. The Malaria Cell Images Dataset from KAGGLE, which contains 27,558 visuals of plasmodium parasites has been used for execution in the suggested VSKIC model. For the purpose of predicting a malarial cell infection, images are preprocessed and fitted using the VSKIC model. Three convolutional layers merged with max pooling layer, and three dense layers that are weight initialized with variance scaling kernel initializer are used as preprocessing steps on the malaria cell images for the purpose of identifying the presence of affected malarial tissue. The picture files were separated into training and evaluation images, and the training Malaria Cell images were used to evaluate the effectiveness of the VSKIC model along with the standard conV2D paradigms. While comparing the suggested model with the available CNN methods, the execution observations made with Python on a V100 Nvidia GPU workstation using 64 and 250 training intervals demonstrates that the proposed VSKIC model predicts with the higher accuracy of 98.96% and Area under curve of 97.8%.
A Comprehensive Study on BLE Enabled IoT Based Tracking System (BITS) for Indoor Environment

With the development of the Internet of Things (IoT) indoor location-aware applications have become a useful tool for locating objects in enclosed spaces. One of the most significant modern advances in IoT is Bluetooth Low Energy (BLE), which is especially suitable for extremely low-power sensors powered by CMOS coinage battery cells. With its reasonable accuracy and low-effort setup, BLE is a suitable application for indoor tracking systems. Intermittently, the BLE nodes send out messaging packets that contain the UUID (Universally Unique Identifier). In this research, an efficient tracking system has been designed for indoor localization in hospitals. To locate patients. , positions, and doctors, a BLE-enabled device has been utilized to automatically scan the area. Depending on their movement the positional updates are transmitted to the server through Wi-Fi. An efficient, lightweight server-side application has also been developed to efficiently track and manage the data. To determine the coverage across the BLE node, the quantitative coefficient of transmission strength has been utilized. After a thorough investigation at various points, it has been observed that the suggested approach shows 25.9% better accuracy than the other competing scheme which shows the effectiveness of the system.
CSI-based Sensing with NOMA of Multiple Sensing Users for ISAC

As wireless technology continues to evolve, the integration of sensing and communication layers is becoming increasingly necessary. However, this integration presents significant technical and commercial challenges. The main challenges are the issues of coexistence, scheduling, and interference which arise due to the increase in the number of signals transmitted over networks. Transmitting signals orthogonally may not be spectrally efficient, hence the need for non-orthogonal multiple access (NOMA) technology is increasing in the future wireless technology. This paper proposes the use of integrated sensing and communication (ISAC) with iterative channel estimation (ICE) to overlap sensing and communication signals, for multiple sensing users (SUs). To assess the effectiveness of this approach, we compared the bit error rate (BER), mean square error (MSE), and spectral efficiency of sending both sensing users (SUs) and communication users (CUs) using non-orthogonal and orthogonal techniques.
A patch antenna with wide band and wide beamwidth

Wideband wide beamwidth antennas play key roles in radio monitoring, and wideband wide scanning angle arrays design. This paper presents the design of a wideband patch antenna providing wide beamwidth. The antenna consists of a cross-shaped and two rectangular patches, and two vertical feeding structures in the differential configuration. All metal patterns are printed on laminates for easy fabrication purpose. The feeding structures, each with two exponential equation governed slots, create wideband impedance matching and wide beamwidth simultaneously. Simulation result shows that this antenna features a fractional bandwidth of 81.6% and a wide beamwidth, reaching its peak of 243° on the E-plane and 122° on the H-plane. Moreover, the antenna has a radiation efficiency more than 90% across the whole bandwidth. Because of its wide bandwidth and beamwidth, the proposed antenna can be utilized in radio signal monitoring, wide scanning angle antenna array construction.
A Large Swing, 40-Gb/s SiGe BiCMOS Driver with Adjustable Pre-Emphasis for Data Transmission Over 75-Ohm Coaxial Cable

A fully differential 40-Gb/s cable driver with adjustable pre-emphasis is presented. Based on a distributed limiting architecture, the circuit can supply up to 3.6 V peak-to-peak per side in a 75 Omega load with variable pre-emphasis ranging from 0 to 400%. S-parameter measurements show 42 dB differential small-signal gain, a bandwidth of 22 GHz, gain peaking control up to 25 dB at 20 GHz and input and output reflection coefficients better than -lOdB up to 40 GHz. Additional features of the driver include output amplitude control (from 1 Vpp to 3.6 Vpp per side), pulse-width control (35% to 65%) and an adjustable input DC level (1.1 V to 1.8 V) allowing the circuit to interface with a SiGe BiCMOS or MOS-CMLSERDES.
Event Prediction in Event-Centric Knowledge Graphs Using BERT

The dataset provided by the Knowledge Graph Reasoning Challenge is an event-centric knowledge graph representation of avatar behavior in a virtual space. Therefore, it contains information about the entire virtual space, including parts of the virtual space that are hidden in the video. However, some information cannot be obtained by any means; for example, when sensor data from the real world are collected. In this paper, we report a method for predicting hidden parts using BERT and the results of experiments using this method, using the Challenge dataset as complete data for training.
An Intelligent Area-segmentation Enabled Hybrid Routing Method in Mega-constellations

With the rapid development of very high-speed inter-satellite links, effective and intelligent routing strategy has become a hot topic in the next generation (6G) mega-constellations. However, constrained by the high dynamic time-varying topology and limited on-board processing capability, the existed routing schemes incur excessive control overhead, huge latency, and packet loss rate caused by the congestion. In this paper, an intelligent area-segmentation enabled hybrid routing method is proposed, combining centralized and distributed methods and adapting to the congested area dynamically. Specifically, with software-defined features, only the Low Earth Orbit (LEO) satellites with congested links report the state information to the controllers, consisting of both Geostationary Earth Orbit (GEO) satellites and Ground Computing Center (GCC), to periodically calculate the accurate congested areas using Deep Q- Network (DQN) algorithm. Numerical simulation results indicate that, the proposed method reduces control overhead by orders of magni-tude and decreases the packet loss rate by approximately 40% in mega-constellations, toward the future resilient and scalable 6G communication networks compared with the traditional schemes.
Linear Quadratic Regulator Control for Multi-Port DC-DC Converter for Standalone Photovoltaic System

The purpose of this work is to propose a dependable control approach for the Voltage Regulator-Battery Energy Storage System (VR-BESS) power converter used for renewable energy sources (photovoltaic system with battery). This method considers multivariable control based the Linear Quadratic Regulator (LQR). The small signal modeling served as the foundation for the mathematical study that was carried out in this research. As a result, the matrices of the converter for the two modes of operation at input voltage source or battery voltage. The simulation was carried out using the Simulink approach provided by the MATLAB program. When compared to the traditional pole placement method, the obtained results indicate that the suggested LQR method in the VR-ESS presented high accuracy along with smooth operation and less maximum overshoot in the output voltage. This was the case as a result of the method's reduced maximum overshoot in the output voltage.
Effectiveness of Digital Marketing Strategies in the Food Delivery Business Based on Hybrid BiGRU-CRF Approach

The use of digital apps to allow meal delivery is one of the most rapidly increasing trends in the online retail business. The advent of online tools has introduced novel approaches to the food industry. Consumers may now access menus from a wide variety of eateries anytime, anywhere thanks to online ordering. Online purchasing is now more accessible than ever before because to developments like digital wallets, net banking, and cash on delivery, as well as the elimination of minimum order amounts. Online meal delivery services have seen tremendous growth and widespread use as the digital barrier between urban and rural areas has shrunk and people everywhere have easy access to smartphones. Incorporating preprocessing, feature selection, and model training, the suggested methodology has many moving components. Before feeding data to a learning algorithm, data normalization ensures that the data is of sufficient quality. Normalization techniques such as Min-max, Z-score, and the median are all utilized at this stage. The proposed approach utilized chi-square and document frequency analyses to narrow down the features to employ. After the attributes are gathered, BiGRU-CRF trains the models. The findings of the suggested method are significantly better than those of the BiGRU and CRF methods. Extremely high (98.71%) chances of success make this a viable option.
Identifying Land features from satellite images using deep learning

Land use and Land Cover changes have been subject to study for longer time due to its impact on climate and environment. Identification of Land features helps in keeping track of changes occur over time. Feature extraction or identification of remote sensing images can be done using deep learning techniques such as CNN. Feature extraction on low resolution satellite images is difficult and we propose to use various deep learning techniques to effectively identify land features using CNN. In this Study we experiment with parameterize multi filter CNN which can be used as the training depth. This will help in further research towards remote sensing image analysis on feature identification using novel CNN techniques.
Sonic Signatures: Sequential Model-driven Music Genre Classification with Mel Spectograms

Music genres, with their diverse sonic landscapes and distinct characteristics, have been a subject of profound interest in audio analysis. This research investigates the application of digital image processing in the field of music genre classification, utilizing Mel spectrogram images obtained from audio files. This study employs a sequential approach to analyze the 'GTZAN Dataset,' which consists of 10,000 documented Mel spectrogram images that represent ten distinct music genres. The dataset was partitioned in a systematic manner into three separate segments. This partitioning allowed for thorough training and evaluation of the model, with a distribution ratio of 60% for training, 20% for validation, and 20% for testing. The sequential model, which is based on deep learning tenets effectively captures complex genre-specific characteristics from Mel spectrograms in order to achieve accurate music genre categorization. By utilizing a dataset consisting of 6,000 training photos and 2,000 validation photos, the model's parameters underwent refinement. Subsequently, an evaluation was conducted on a distinct set of 2,000 test photographs, which unveiled a remarkable accuracy rate of 94%. During the course of the research, performance metrics such as accuracy and loss graphs were employed to monitor the learning progress of the model during the training phase. Moreover, the examination of the confusion matrix in the testing phase provided insight into the effectiveness of the model, resulting in notable performance measurements. This confirms the model's strength in accurately categorizing music genres. This research makes a substantial contribution towards the advancement of autonomous systems that possess the ability to accurately classify music genres by utilizing spectrogram representations. The model's accuracy of 94% serves as evidence of its effectiveness, indicating its possible applications in systems for recommendations, music indexing, and content organization. This emphasizes its significant contribution to the field of audio content analysis and classification approaches.