Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Approach for Fake News Detection using Ensemble Techniques

The internet has made it possible for people to access news and information from all over the world, at any time and from any device. Because of the increased use of social media outlets like Twitter and Facebook, news can reach thousands of people in a very short time. It is challenging to find informative content since false information is provided to lead readers to believe false ads. Additionally, Spammers frequently lure readers to click on links that lead to phony websites by using spectacular news headlines. A hybrid approach incorporating ensemble learning with natural language processing should be used to solve this issue. This paper offers a concept and a mechanism for identifying false news using an enhanced machine learning-based approach with a voting classifier. The ensemble learning model helps us to mitigate the individual faults of various models while utilizing their combined strengths to identify patterns in the datasets. By aggregating data points from various domains, classifiers can automatically identify false news and the authenticity of news after being trained on a data set. Improved accuracy, robustness, adaptability, and interpretability are the outcomes of the voting classifier.
A task offloading scheme with Queue Dependent VM in fog Center

Mobile devices still have limited processing resources despite the advancements they have undergone, hence a technology that supports these devices is required. Fog computing pertains to a new technology that supports such resource-constrained devices. To enhance the level of service and user experience, end devices can delegate the work to nearby fog nodes. This article suggests a brand-new offloading method for planning and processing tasks produced by edge devices to the proper computing devices. The M/M/c/K queueing model is used to describe the system, and it determines the optimal number of virtual machines to activate in the fog layer for active processing of the offloaded jobs. The needed number of VMs in the fog layer is optimised by this technique, which also reduces the delay-sensitive tasks queue waiting time. The effectiveness of the model is verified with various numerical results.
A Comprehensive Analysis of Advanced Change Detection Technique for Planning and Monitoring Sustainable Mango Crop Areas using Landsat-8 Images

An efficient and accurate mango (Mangifera Indica L.) crop planning is a very important factor for sustainable mango crop production. Mango is a perennial fruit crop and alternate bearer, which is a major challenge for production sustainability. The main objective of this study is to detect the land cover changes using satellite images for sustainable land cover planning in three main mango-producing districts, namely Lucknow, Bulandshahr, and Meerut of Uttar Pradesh State, India. In this study, Landsat-8 OLI images from 2013–14 to 2020–21 have been used to detect the changes by implementing the log ratio (LR) and principal component analysis (PCA) fusion-based change detection method. In order to extract meaningful information from Landsat-8 images, Red and NIR band images were used to create soil-adjusted vegetation index (SAVI) images. Thereafter, the fusion-based change detection method using LR-PCA has been applied to retrieve the enhanced image. To obtain the final change images with the detected temporal changes in the area under the mango crop, image thresholding has been used. After conducting a detailed analysis, it is observed that the change detection accuracy of the LR-PCA fusion-based change detection technique is retrieved as 92%, which is quite high. In the future, proper mango monitoring techniques may play a very important role in developing sustainable mango orchards and maintaining environmental health. Future applications of this study will include change detection in mango crop areas in all major mango-producing districts in Uttar Pradesh.
Towards a smart pervasive conversational agent for COVID-19 psychological assistance based on NLP

Diverse countries throughout the world were quar-antined due to the novel pandemic known as COVID-19, even after vaccination,. As a result of this grim circumstance, most socioeconomic and political spheres have encountered deep crisis and from there people have experienced stress, anxiety, depression, and even suicide, In this paper, we propose a smart pervasive conversational agent for psychological assistance during and after COVID-19 quarantine, which could converse with a regular citizen to raise awareness of the genuine threat of the outbreak and the importance of vaccination. Our proposed conversational agent could be able to recognize and manage stress and anxiety using natural language understanding (NLU) and international stress and anxiety scales. The messages given by our agent and its mode of communication may help to alleviate anxiety following the world's lockdown. Our agent's comment threads and management styles may be able to soothe people's worry during the world's lockdown. Our proposed approach is a mobile healthcare service with three interdependent units: an input processing (IP) that performs natural language understanding (NL), a Storage that stores every interaction, and a response manager (RM) that controls the responses of our conversational agent.
Continuous Health Monitoring System for Patients Using IoT

Modern technology plays a crucial role in healthcare not only for reducing sensitive bias but also for improving transmission, documentation. , and display device performance. It's veritably essential to monitor vital signs and post-operative progress. Therefore, the latest advancement in healthcare communication is utilizing the IoT (Internet of Things) technology. The Internet of Things accelerates healthcare and plays a significant role in various healthcare procedures. The current method only detects oxygen saturation, blood pressure, pulse rate and body temperature which doesn’t count the rate of breathing. In the proposed system, there is an advantage of counting the rate of breathing which uses ESP32-S as a microcontroller that acts as the bridge to connect and transfer data with multiple sensors like the spO2 sensor, Respiratory sensor, and heartbeat sensor. The control unit collects the data and transmits it to the network via IoT, hence furnishing Live monitoring of the patient’s healthcare Additionally, the system consists of an LCD display that continuously displays the patient's health status. The data can be viewed anytime and anywhere by the caretaker.
A Predictive Model for Road Traffic Data Analysis and Visualization to Detect Accident Zones

The usage of vehicles were increased throughout the world, the count of accidents is also increased globally. One of the world's major concerns is recognized to be the number of traffic accidents. In each year, there are increasingly more traffic accidents. Consequently, it has a significant impact on a nation's society, economy, and progress. As accidents occur in various places and at various times, identifying where they are more likely to occur can be complicated. This paper presents a Predictive Model to analyze and visualize road traffic accident zones in Tamil Nadu. The zones are categorized by four different attributes such as low, medium, high and very high. Data analysis is done in order to make a determination. The Real time data are acquired and interpreted using Latent Class Clustering Analysis (LCA). The outcome is represented through Cartogram visualization technique. Accident analysis is done to identify the reason or causes of an accident in attempt to prevent similar instances from occurring repeatedly. As a result, it is simpler to recognize the zones so the data is easier for the users to absorb.
Internet of Things based Smart Water Leakage Monitoring and Alert System

It is critical to manage water supply in a safe and efficient manner. Significant amounts of water are lost each year as a result of leaks in water distribution systems (WDN). As a result, locating leaks in a reliable manner is critical. Water leaks can be detected using monitoring software that analyses the flow of water. Apps that track the user’s location can be extremely useful. If this technology is widely adopted, it will aid in the resolution of water management issues such as human error and waste of limited water resources. Water leaks can be located predictably using the Internet of Things (Internet of Things). Many countries struggle to get enough water, but the situation is not hopeless. It is critical to accurately locate water leaks in order to reduce system resistance.
AI-Based Techniques for the Detection of Neuro-Developmental Disorders: A Survey

The ability to identify Central Nervous System (CNS) abnormalities at an early stage has sped up the development of automated diagnostic tools in the new era of emerging technologies. One of the most complicated behavioural impairments, neurodevelopmental disorder (NDD), is the main cause of disability, particularly in children. A range of illnesses are linked to NDD, and three diverse disorders that belong under this umbrella are Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) and Childhood Disintegrative Disorder(CDD). Diagnosing these disorders is difficult because of their high comorbidity. Traditional NDD recognition is based on qualitative criteria, which lengthens the diagnosing process. Deliberations by researchers, scientists, and academics are thus needed to provide more automated diagnostic tools hinged on quantitative factors that not only are reliable and accurate but are also accessible to everyone. This makes the diagnosis of NDD a crucial area. This paper highlights various automated diagnostic systems that use artificial intelligence approaches based on quantitative parameters for the early detection of ASD, ADHD and CDD. The study concludes by highlighting unresolved difficulties in the literature that need to be further investigated in order to provide a more effective NDD analysis.
A Lightweight Algorithm for Text-Classification by Text Mining

The digital revolution and the development of communications and social media have produced billions of articles in all fields. Automatically classifying articles has a major role in developing systems and artificial intelligence applications based on textual data mining. Many methods rely on machine learning for classification, but these methods require large resources and time. This paper presents a new, simple, and fast automated method for classifying texts and articles within their domains without relying on machine learning algorithms. The proposed algorithm is based on sequential steps in textual data mining in addition to the DMOZ ontology. The proposed algorithm was applied in the form of a web service and was tested on various texts. The results showed the effectiveness of the proposed method, with an accuracy of over 90%. In the future, we suggest applying the proposed algorithm to Arabic texts and creating a specific ontology for Arabic words.
Deep Learning with Encoders for Intrusion Detection Systems (IDS)

An Intrusion Detection System (IDS) is often used to keep safe and efficiently utilise communication network. And generally this is achieved by monitoring the network regularly for possible intrusions. Nowadays in networks, zero-day attacks are common type of intrusions. So it became necessary to use IDS, which is capable of detecting zero-day attacks. Machine Learning (ML) algorithms are suitable in designing such IDS. Deep Learning (DL), being part of ML, have promising approaches that can be used in designing IDS, which is capable of detecting intrusions of new and old types. This paper proposes DL based IDS which create model of normal network traffic. This model classifies attacks based on the various features present in the dataset. The proposed model uses a Convolution Neural Network, bi-directional Long Short Term Memory (LSTM), and a stack of encoders to handle spatial and temporal features more effectively. The model achieves a low False Positive Rate and high Detection Rate compared to existing models.
Effective Security Mechanisms against Distributed Denial of Services

In general, the popularity of DDoS attacks being used as a weapon to harm the opposite party, is on the rise. Hence, is the need for the security from such disruptive attacks. There have been several attempts at detecting, preventing and mitigating the DDoS attacks with several model proposals being put forward. In this paper, we have reviewed some research papers along with the models and mechanisms proposed in them. As the conclusion, we have picked out the mechanism which we found to be the most effective against the DDoS attacks.
Mushroom Classification Model to Check Edibility using Machine Learning

The human body gains a lot from mushrooms. At the same time, not every mushroom that we see is edible. Some mushrooms have medical properties; at the same time, some might have deadly viruses that are not good for humans. We as human beings love to eat these mushrooms, but it is essential to check and then consume them as it can be a matter of life and death. Here we are set to study this paper to examine behavioral attributes of mushrooms such as the structure, size of the surface, cap tone, lamella, which is also known as the gill, and stalk as well as the smell place where the mushroom was grown, and population. The machine learning algorithm we will use would be K Means clustering. The accuracy of the classification problem, the coefficient matrix, and the duration are taken to build a classification model on the datafile be taken into consideration. The highest-ranked column for classification is the odour contributing to the highest classification accuracy.
Role of Artificial Intelligence-Enabled Recruitment Processes in Sourcing Talent

The artificial intelligence (AI) based human resource (HR) process related to recruitment and selection has gone from just being an assisting hand to a vital source thanks to the technological advancements made by the industry in the last few decades. From a tactical activity to a strategic business imperative, the recruitment function in organizations has changed many hats. For organizations and managers, AI-enabled recruiting services to source the right talent are a big sell-out based on the ease of use and the time and effort it saves for everyone. But even then, not many organizations except those in the IT and ITeS sectors are using AI extensively in all their recruitment processes. The motivation for this research stemmed from the increasing usage of AI across the different functional domains in business. The study was conducted utilizing both primary and secondary data. In this study, the perceived value of an AI platform for organizations that are using AI in the various HR processes is also ascertained. Novel themes that emerged from the usage of AI in sourcing talent were analyzed.
Optimizing Medical Healthcare Services Using Big Data Analytics

Big data is a very large size of datasets which come from many different sources and are in a wide variety of forms. Due to its enormous potential, big data has gained popularity in recent years. Big data enables us to investigate and reinvent numerous fields, including the healthcare industry, education, and others. Big data specifically in the healthcare sector comes from a variety of sources, including patient medical information, hospital records, findings from physical exams, and the outcomes of medical devices. Covid19 recently, one of the most neglected areas to concentrate on has come under scrutiny due to the pandemic: healthcare management. Patient duration of stay in a hospital is one crucial statistic to monitor and forecast if one wishes to increase the effectiveness of healthcare management in a hospital, even if there are many use cases for data science in healthcare management. At the time of admission, this metric aids hospitals in identifying patients who are at high Length of Stay namely LS risk (patients who will stay longer). Once identified, patients at high risk for LS can have their treatment plans improved to reduce LS and reduce the risk of infection in staff or visitors. Additionally, prior awareness of LS might help with planning logistics like room and bed allotment. The aim of the suggested system is to precisely anticipate the length of stay for each patient on an individual basis so that hospitals can use this knowledge for better functioning and resource allocation using data analytics. This would contribute to improving treatments and services.
LSTM and Prophet Model Fusion Framework for Bitcoin Candlestick Visualization and Price Prediction Forecasting

Bitcoin is a decentralized digital currency that has climbed substantially in recent years, reflecting the fact about the amount of rise in its price. Even though investors and traders pay a lot of attention to Bitcoin, there is very little we know about its actual nature. Bitcoin's value is incredibly fickle. No doubt, there are many advantages of trading and investing in Bitcoin, but there are also many risks due to the inconstant nature of its price. Price forecasting is necessary to reduce the danger posed by Bitcoin's price volatility. The prior studies favored using only past (or historical) data and neglected real-time data. Ultimately, the results had some inaccuracies. To combat this issue and make a more accurate prediction, this work suggests integrating both past and live data. The objective of this paper is to visualize the bitcoin candlesticks (i.e., Open, High, Low, and Closing prices) and forecast the future price using historical and real-time data. The dataset used for the current study is taken from Tiingo. The specific reason for choosing Tiingo is that its APIs are built to be performant, and consistent and support extensive filters to speed up our development time. We specifically used a fusion of two deep learning models, the Stacked LSTM (Long Short-term memory) model and Prophet model. The outcomes of this study assist traders and bitcoin consumers to make educated choices regarding bitcoin investments. Moreover, this study is a crucial tool for researchers to comprehend the behaviors of the bitcoin market. Therefore, the appropriate question, to begin with, is: Can Deep Learning accurately forecast bitcoin prices?
Health and Position Tracking for Mountain Climbers

The well-being checking and efficient monitoring for mountain climber is gaining its importance nowadays. Several precautions must be taken regarding the safety of climbers. In order to track the wellness of the climbers they must carry pulse rate sensors suitable communication devices to connect with the control rooms. At worst heath conditions, it turns out to be incompetent for the mountain climbers to utilize it and communication issues increases inefficiency after a particular altitude. The health and position tracking system with Global Positioning System (GPS) to detect the live location and furthermore measuring the temperature and heartbeat of the climber is proposed in the work which overcomes the shortcomings of the techniques used by the mountain climbers. Global System for Mobile Communication (GSM) module is also connected with the device for interfacing the messages, ease of accessing network. Hence information regarding health and position of climbers will be transferred using GPS position and message through GSM providing information continuously to the rescue team. Besides, it includes additional features such as climber alertness program which monitors climber’s activities from time to time. An emergency switch is also provided with the climber in case of emergency purposes. The system is very helpful for getting health status information of climber and providing them instant help.
Design of A Novel Model that Aids Task Estimation in Comparison with a Regression Tree

One of the sectors with the fastest recent growth is the software sector. Project management is key to the software industry's success. One of the most crucial tasks in software project management is to determine the cost and effort associated with software development in the initial stages of the project. There are various conventional techniques for estimating software development efforts. However, there is a lot of interest in using machine learning techniques to address the issue of effort estimates. Out of these machine learning techniques, regression tree-based models have been focused on more in this paper due to their ability to generalization and understandability.
Design of Power Delay Efficient Wallace Muliplier

In applications involving image processing, multipliers are crucial. Area, potency, and delay analysis of the circuit are a few of the different metrics used to describe the performance of digital analysis. The 4-bit AXB is multiplied by a 4X4 Wallace tree resulting in the formation of a fractional creation, which causes an incrimination in latency. The delay is further decremented by inserting a 4-2 compressor in the fractional creation generation phase and it is used to implement 8bit Wallace multiplier to reduce delay. The simulated result shows a decrease in power and delay.
Innovative Process of Amorphous Cellulose – Graphene Oxide Hybrid structure bead

The amorphous cellulose (AC) and amorphous cellulose-graphene oxide hybrid structure (ACGO) bead was successfully fabricated via aqueous sulfuric acid solvent system. The methodology was compared to previous solvent systems in two perspectives including practical and economic perspectives to emphasize the advantages of the methodology. Two bead series were obtained: white AC bead and light brown ACGO bead. The light brown colour suggested that GO was successfully embedded in AC bead. In both perspectives, the proposed methodology was the most practical and efficient comparing to the other methodologies used in this study. Hence, the ACGO bead can be considered as one of the alternative “Green” materials for various applications.
Deep Learning vs. Feature Extraction Based Multi-Objective Approach for Sentiment Classification

Sentiment analysis is used for classification and analyzing of text through data and text mining. The expected output is generated using the identification and extraction of the information from the various sources provided in the Internet that helps in various kind of business and collection of data for researchers. In recent days deep learning and machine learning technologies are used for sentiment analysis for accurate prediction and analysis. Sentiment analysis can be done in 3 different ways like document level aspect level and sentence level. Not only the web sources text messages blogs articles emails are analyzed for extraction of text related to our searches. Sentiment analysis has significant role in in natural language processing and emotional tone identification. Sentiment analysis can be used for numerous applications for market research, reputation of a specific brand, consumer reception of products, success evaluation of marketing campaign and much more. It can be applied in different types of fields for collection of data using text extraction from various sources. The main objective of the study is the development of multi objective approach and comparison of deep learning versus feature extraction for sentiment classification. The proposed multi-objective sentiment classification model give the output of 99% accuracy using multi objective approach.