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The increased social media usage in modern history instigates data collection from various users with different backgrounds. Mass media has been a rich source of information and might be utilized for countless purposes, from business and personal to political determination. Because more people tend to express their opinions through social media platforms, researchers are excited to collect data and use it as a free survey tool on what the public ponders about a particular issue. Because of the detrimental effect of news on social networks, many irresponsible users generate and promote fake news to influence public belief on a specific issue. The U.S. presidential election has been a significant and popular event, so both parties invest and extend their efforts to pursue and win the general election. Undoubtedly, spreading and promoting fake news through social media is one of the ways negligent individuals or groups sway societies toward their goals. This project examined the impact of removing fake tweets to predict the electoral outcomes during the 2020 general election. Eliminating mock tweets has improved the correctness of model prediction from 74.51 percent to 86.27 percent with the electoral outcomes of the election. Finally, we compared classification model performances with the highest model accuracy of 99.74634 percent, precision of 99.99881 percent, recall of 99.49430 percent, and an F1 score of 99.74592 percent. The study concludes that removing fake tweets improves the correctness of the model with the electoral outcomes of the U.S. election. 

The paper highlights the challenges non-computer science professionals encounter when managing and analyzing heterogeneous data sources. To address these issues, it details the innovative learning methodologies employed in Data Science and Engineering (DSE) courses at the University of the District of Columbia. These courses are specifically designed to equip students from diverse disciplines with the skills needed to effectively apply DSE techniques within their respective fields. The outcomes underscore the transformative power of DSE education in fostering transdisciplinary research, enhancing the research capabilities of both computer science and non-computer science students, and driving innovation and scientific discovery across a broad spectrum of domains, 

This research paper delves into the evolving landscape of fine-tuning large language models (LLMs) to align with human users, extending beyond basic alignment to propose "personality alignment" for language models in organizational settings. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. Through an original case study, we demonstrate the necessity of personality fine-tuning for AIs and raise intriguing questions about applying human-designed tests to AIs, engineering specialized AI personality tests, and shaping AI personalities to suit organizational roles. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment, offering a foundational anchor for future exploration in human-machine teaming and co-existence. 

Self-attention transformers have demonstrated accuracy for image classification with smaller data sets. However, a limitation is that tests to-date are based upon single class image detection with known representation of image populations. For instances where the input image classes may be greater than one and test sets that lack full information on representation of image populations, accuracy calculations must adapt. The Receiver Operating Characteristic (ROC) accuracy thresh-old can address the instances of multi-class input images. However, this approach is unsuitable in instances where image population representation is unknown. We consider calculating accuracy using the knee method to determine threshold values on an ad-hoc basis. Results of ROC curve and knee thresholds for a multi-class data set, created from CIFAR-10 images, are discussed for multi-class image detection. 

An immense volume of data is produced by sensor devices in the fields of aquaponics, hydroponics, and soil-based food production, where these devices track various environmental factors. Data stream mining is the method of retrieving data from fast-sampled data sources that are constantly streaming. The accuracy of data obtained through data stream mining is largely determined by the algorithm utilized to filter out noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise-suppression algorithms have been proposed in the literature. They have been evaluated based on the mean squared error metric (MSE). The Long Short-Term Memory – LSTM filter (MSE: 0.000999943) performs better noise suppression than other traditional filters – Kalman (MSE: 0.0015982). We propose a new noise suppression filter – LSTM combined with Kalman (LSTM-KF). In LSTM-KF, the Kalman filter acts as an encoder and the LSTM becomes the decoder, resulting in a significantly lower MSE – 0.000080789592. The LSTM-KF is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost. 

Enormous amounts of data are generated each day by sensor devices. In agriculture, these devices continuously monitor numerous environmental properties in the fields of aquaponics, hydroponics, and soil-based food production. Data stream mining is the process of extracting data from continuous, rapidly sampled data sources. The data accuracy that can be achieved in data stream mining is highly dependent on the algorithm chosen to suppress noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise- suppression algorithms have been proposed in the literature. The proposed LSTM (Long Short-Term Memory) filter performs better noise suppression than other traditional filters – Kalman and moving average filters. The LSTM filter is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost. 

A mini quadrotor can be used in many applications, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detection. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substantially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.