One of the most prevalent and disabling mental conditions that seriously influence society is stress and depression. The use of social networking to improve the detection of stress and depression may require automatic health monitoring systems. Sentiment analysis refers to the use of content mining and natural language processing techniques with the goal of identifying feelings or opinions. full of emotion Computing is the study and development of systems and equipment that can recognize, understand, process, and imitate human (...) effects. Deep learning and sentiment analysis approaches could provide effective algorithms and frameworks for a target evaluation and observation of mental issues, particularly depression and stress. This research discusses the use of sentiment analysis and deep learning approaches for the detection and monitoring of stress and depression. Additionally, a fundamental foundation for a multimodal framework that integrates estimating investigation and extensive methods for processing feelings is given. This framework will be used to assess for stress and sadness. The paper specifically traces the fundamental problems and compares them to the framework's structure. (shrink)

This study focuses on developing a comprehensive sentiment analysis framework aimed at understanding sentiments expressed in social media posts, enhancing online reputation management for brands. Given the overwhelming volume of user-generated content across platforms, we instituted a methodical approach leveraging advanced machine learning techniques. Specifically, we used Python libraries such as TensorFlow for deep learning functionalities and PyTorch for natural language processing tasks. Our models classify sentiments into three categories: positive, negative, and neutral, while simultaneously analyzing trending (...) patterns and user opinions. The results demonstrate a significant classification accuracy, providing evidence for the framework’s efficacy in real-time social media monitoring. Ultimately, our findings emphasize the strategic role of sentiment analysis in improving brand reputation, enabling better consumer engagement, and informing marketing strategies. (shrink)

Social media has transformedevery communication and posting modality into sharing and expression of opinions. Everyday, an enormous amount of data shared on Twitter, Facebook, Reddit, and Instagram portrays varied emotions that range between positive endorsements and negative criticisms. This is the very aspect of sentiments that make up the core basis through which marketing strategies are developed, along with improvement of customer services and the promotion of brand loyalty for businesses and organizations. Traditional approaches to sentiment analysis will (...) struggle with dynamic and voluminous data from social media, which makes it require very high tools that would be able to analyze them in real-time. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...) classify cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. T. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...) classify cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...) classify cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group (...) can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

Social media sites offer a huge data about our everyday life, thoughts, feelings and reflecting what the users want and like. Since user behavior on OSNS is a mirror image of actions in the real world, scholars have to investigate the use SM to prediction, making forecasts about our daily life. This paper provide an overview of different commonly used social media and application of their data analysis.

Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing (...) accuracy 96% .Testing dataset organized into one folder (age-gender-test) and contains 1676 audio files related to 39 of age and gender categories and 40 epoch. (shrink)

This study analyzed the utilization of social media platforms and university students' attitudes towards academic activities in Cross River State. A descriptive survey research design was adopted for the study. The population of this study comprised all the private and public university students in Cross River State. A sample of 1,600 students, which cuts across the three universities in the area of study, was selected using the convenience sampling technique. A questionnaire (r=.849) and a rating scale (r=.786) were used (...) as the instruments for data collection. Findings emerged amongst others that the utilization status of social media platforms by university students is generally high; the utilization of Facebook, WhatsApp, Instagram, Twitter, WeChat, Skype, Snapchat, Eskimi, Telegram and YouTube social media platforms has a significant composite influence (F=52.453, p<.05) on university students' attitudes towards academic activities. Relatively, utilization of Facebook was the highest predictor (t=11.222, β=.232, p<.05), followed by the utilization of WhatsApp (t=11.068, β=.229, p<.05), utilization of Twitter (t=5.977, β=.118, p<.05), and the utilization of Instagram (t=2.772, β=.056, p<.05), in that order. It was recommended that secondary school students should develop positive attitudes towards their academic activities and abhor activities that will lead to a decline in the learning outcomes. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often (...) suggested that generics are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (shrink)

Abstract: Age and gender classification has been around for a long time, and efforts are still being made to improve the findings. This has been the case since the inception of social media platforms. Visible understanding has become more important in the computer vision society with the emergence of AI increase in performance and help train a model to achieve age and gender classification. Although these networks built for the mobile platform are not always as accurate as the larger, (...) more resource- intensive networks we've come to know and love, they stand out when it comes to the accuracy trade-off. Despite the importance of these attributes in our daily lives, the ability to estimate them efficiently from face images is still far from meeting the requirements of commercial applications. (shrink)

Abstract of of the anthology: Digitalisation encompasses all areas of life and is thus also relevant for theology and the Church. This is not just about the possible uses of social media; in this anthology, digitalisation is understood as a phenomenon that shapes society, the Church and theology with the help of various technologies, such as artificial intelligence. In this context, theology and the Church represent places of digitalisation on the one hand, and, at the same time, the discourse (...) on digitalisation can be advanced with the help of theological thinking on the other. This anthology is both theologically ecumenical and is anchored in an international context. The editors' research focus is on ethics. -/- With contributions by: Aaron J. Butler, Heide A. Campbell, Michael Fuchs, Stephen Garner, David J. Gunkel, Florian Höhne, Markus Huppenbauer, Peter G. Kirchschläger, Birte Platow, Raphael Salvi, Thomas Schlag, Peter Seele, Gotlind Ulshöfer, Joachim Wiemeyer and Lukas Zapf. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical (...) critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

Despite iterative learning design being increasingly implemented, such approaches are often delineated by well-defined periods of design/implementation. However, second-order cybernetics, which suggests a participatory approach to learning design, involves responsively adapting learning environments to meet students’ needs, treating them as agentic participants in the classroom. In our mixed methods study, we investigate whether such a process can facilitate egalitarian participation and collaborative interactions in a technology-assisted classroom. We use the example of a graduate psychology class of 17 students and suggest (...) that adaptation of live-chat activities by a participant observer on the Reddit social media platform that supplemented the in-person lecture dynamically, using a network analysis and qualitative ethnography as a modelling facility mimicked the ongoing feedback loops of social media platforms, enabling students to use social media with a critical eye, and engage in productive collaboration. Our quantitative results present network graphs for weekly eigen centrality to understand the egalitarian nature of the network, and transitivity to understand the likelihood for collaboration between more than two agents. Our qualitative results elaborate selected Reddit posts, and weekly field notes to explain how redesigning the chat weekly helped augment lecture-based discussion with the instructor and critique of student presentations, spurring egalitarian participation through a space-place dialectic. Students also provided end-semester feedback that was analyzed using inductive coding, to design future courseware. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

During an epidemic, it is very important to follow the instructions issued by the official authorities. One big step at this point is Social Distancing. It is very difficult for the authorities to keep a detailed look at whether social distancing norms are being followed or not. We meet situations where instead of following these rules patiently, people become frightened and often crowd the space. Therefore, for simple monitoring for the authorities (e.g. police officers) we propose a solution (...) using the Deep Learning process that will notify them as soon as a public violation occurs i.e., if the number of people is above the specified limit (the limit on the number of people that are allowed to be in one place at a time, specified by the government) or if the distance among two people is less than the specified limit. This program will not only reduce the effort and effort required to manually inspect each and every nearby location, but also help in saving time with the help of fast analysis in simple terms, CCTV cameras will help simultaneously monitor each public places. (shrink)

The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and (...) detecting the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. (shrink)

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed Deep Convolutional (...) Neural Network (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (shrink)

The Responsibility of Freedom of Speech and the Role of Media in Propagating Correct Knowledge -/- In modern societies, freedom of speech is often heralded as a fundamental right, allowing individuals to express their thoughts and opinions freely. However, the concept of absolute freedom of speech is not without its flaws. When viewed through the lens of natural laws, including the universal law of balance, the unchecked exercise of speech can lead to societal imbalances. It is essential to recognize that (...) while individuals should be allowed to voice their opinions, this freedom comes with the responsibility to ensure that speech contributes positively to societal harmony, personal growth, and the well-being of others. This responsibility extends beyond individual action and applies to all forms of media, which play a significant role in shaping public discourse and the flow of information. -/- The Concept of Freedom of Speech and its Pitfalls -/- Freedom of speech, in its ideal form, allows individuals to express their ideas and beliefs without fear of censorship or retribution. This principle is grounded in the belief that diverse viewpoints contribute to the flourishing of democratic societies. However, the unrestricted nature of this freedom can also lead to unintended consequences. When individuals or groups disseminate harmful misinformation, spread hate speech, or manipulate public sentiment for selfish gain, the foundational balance within society is disrupted. The unchecked expression of falsehoods or inflammatory rhetoric can create divisions, incite violence, or foster ignorance—outcomes that are contrary to the well-being of individuals and the harmony of society. -/- The spreading of false beliefs, misinformation, or gossip can be especially damaging. Such behavior not only distorts reality but also erodes trust within communities, making it more difficult for people to distinguish between truth and falsehood. False beliefs can lead to irrational fears, biases, or hatred, and when spread, they contribute to the further fragmentation of societies. In the age of digital media, these harmful narratives can quickly go viral, reaching vast audiences before they can be corrected, amplifying their negative impact. -/- From the perspective of the universal law of balance, the free exchange of ideas must be aligned with the greater good, ensuring that speech contributes to constructive discourse and societal progress. Words are powerful, and their impact on both the individual and collective mind can shape reality in profound ways. Therefore, freedom of speech should not be seen as an absolute right devoid of responsibility. Rather, it must be accompanied by an awareness of its potential effects and a commitment to using it to foster understanding, unity, and truth. -/- The Role of Media In Shaping Knowledge -/- Media, whether traditional or digital, plays a central role in how information is disseminated to the public. In today’s interconnected world, media outlets, including news organizations, social media platforms, and content creators, have an unprecedented influence on shaping public opinion. Given this influence, media must be held to high standards of responsibility, as it is the primary vehicle through which society consumes knowledge. -/- The responsibility of the media extends beyond merely reporting facts; it includes the duty to ensure the accuracy, reliability, and relevance of the information shared. Media outlets must prioritize the dissemination of truth, fact-check their sources, and avoid sensationalism that can distort public perception. Misinformation, especially when propagated through powerful media channels, can have lasting consequences on public trust, societal stability, and personal decision-making. The rapid spread of gossip, rumors, or fake news can lead to unnecessary panic, conflict, or even harm, demonstrating the real dangers of unchecked speech. -/- In the age of information overload, the role of the media is to sift through the noise and present knowledge in a balanced, accessible, and responsible manner. This involves not only reporting on events but also offering analysis and context that help the public understand the deeper implications of the information being shared. Media must act as a filter, ensuring that the knowledge people receive aligns with reality, helps individuals make informed decisions, and contributes to the greater good. -/- The Need for Media Literacy and Accountability -/- As media consumers, individuals must also take responsibility for their engagement with information. Media literacy—the ability to critically analyze, evaluate, and interpret media content—is a crucial skill in today’s world. With the proliferation of social media platforms, where misinformation can spread rapidly, it is more important than ever for individuals to be discerning about the sources of information they trust. Education systems worldwide must prioritize media literacy as part of their curricula, teaching students how to identify credible sources, distinguish between fact and opinion, and recognize the potential for manipulation in media messages. -/- For the youth, empowering them to critically engage with media is especially vital. With the rise of social media and peer-to-peer communication, young people are particularly susceptible to the influence of misinformation and gossip. The spread of rumors or false information among peer groups can have serious social consequences, including damaged reputations, broken friendships, and even bullying. It is essential to foster a sense of personal responsibility in the youth by teaching them the importance of verifying information before sharing it and the harmful effects of spreading unverified or negative content. -/- Youth should be empowered to practice empathy and discernment when interacting with information. Schools, families, and communities can collaborate to teach the younger generation about the consequences of gossip and misinformation—how they can harm individuals, societies, and even themselves. Encouraging open dialogue about the ethical use of information and the value of truth can help instill the importance of responsible speech in the youth. By creating a culture that values truth over sensationalism and empathy over gossip, we can work toward a more informed and balanced society. -/- At the same time, media organizations must be held accountable for the content they produce and share. This accountability requires strong ethical guidelines and transparency, ensuring that media outlets prioritize public welfare over profits or political agendas. The role of media is not merely to entertain or persuade but to educate, inform, and serve as a reliable source of knowledge. Failure to adhere to these responsibilities can lead to misinformation, polarization, and the erosion of public trust. -/- The Importance of Legal Laws in Preventing the Spread of False Information -/- While personal responsibility and media literacy are essential, legal frameworks also play a crucial role in curbing the spread of false information. In the Philippines, laws like the Cyber Libel Law (Republic Act No. 10175) serve as vital tools for combating online defamation, false information, and harmful content. This law holds individuals accountable for spreading libelous statements or malicious rumors online, providing a legal recourse for those affected by misinformation. -/- Such legal measures are important because they act as deterrents, encouraging individuals and organizations to think carefully before sharing content that could harm others or society at large. Legal laws like the Cyber Libel Law help ensure that the digital space is not misused for malicious purposes, such as spreading false beliefs, gossip, or misinformation that can cause public harm. These laws serve to protect individuals’ reputations and prevent the viral spread of content that could lead to social unrest or personal injury. -/- Furthermore, legal frameworks can also push media outlets and digital platforms to take more proactive measures in monitoring and controlling the spread of misinformation. By enforcing penalties for the dissemination of harmful content, governments can encourage media organizations and platforms to adopt stricter content policies and invest in fact-checking mechanisms. Legal safeguards not only protect individuals but also promote accountability, ensuring that those responsible for the creation and dissemination of false information are held liable. -/- While freedom of speech is crucial, the presence of legal frameworks ensures that this right is not abused to the detriment of others. These laws help strike a balance between protecting the right to express opinions and maintaining societal harmony by preventing the spread of falsehoods and malicious gossip. -/- The Balance Between Freedom of Speech and Responsibility -/- In light of the universal law of balance, freedom of speech must be exercised with a sense of duty. The right to express oneself must not come at the expense of societal harmony or the well-being of others. Media, as a conduit for knowledge and discourse, must prioritize accuracy, accountability, and ethical standards in its operations. This includes ensuring that the information they share is balanced, relevant, and rooted in truth. -/- At the same time, it is important to recognize that all individuals, including media practitioners, are part of a larger system. Every word spoken, every article written, and every broadcast made contributes to the collective reality we all experience. The impact of these actions ripples out into society, influencing minds, shaping behaviors, and creating a feedback loop that can either strengthen or weaken the fabric of society. By embracing responsibility in speech and media practices, we can help ensure that our collective decisions align with the natural balance of the world around us, fostering a society that values truth, understanding, and mutual respect. -/- Conclusion -/- The concept of freedom of speech, while important, cannot be viewed as a blanket entitlement devoid of responsibility. The power of speech, when unchecked, can disrupt the natural balance of society, leading to harm, division, and ignorance. Media, as a central vehicle for disseminating knowledge, holds a critical responsibility in ensuring that the information shared is accurate, reliable, and serves the greater good. In this context, freedom of speech should be understood as a tool for fostering truth and unity, not as an unregulated license to mislead or divide. -/- Legal frameworks, such as the Cyber Libel Law, provide essential safeguards, holding individuals accountable for the harm caused by false information. By aligning speech and media practices with the principles of balance, responsibility, and truth, society can move toward a more informed, harmonious, and just future. Moreover, empowering the youth to become responsible consumers and sharers of information is essential in cultivating a generation that values truth, empathy, and ethical communication, ultimately promoting a balanced and well-informed society. -/- . (shrink)

How political opinions are spread on social media has been the subject of many academic researches recently, and rightly so. Social platforms give researchers a unique opportunity to understand how public discourses are perceived, owned and instrumentalized by the general public. This paper is instead focussing on the political discourses themselves, and how a specific machine learning technique - vector space models (VSMs) -, can be used to make systematic and more objective discourse analysis. Political footprints are vector-based (...) representation of a political discourse in which each vector represents a word, they are produced thanks to the training of the English lexicon on large corpora of text. This paper describes a simple implementation of political footprints, some heuristics on how to use them, and their application to four cases: the U.N. Kyoto Protocol and Paris Agreement, the 2008 and 2016 U.S. presidential elections. The reader will be given some reasons to believe that political footprints produce meaningful results, suggestions on how to improve them and validate the results. (shrink)

Introduction. The development of legal culture and a culture of human rights in the modern world through media technologies, is acquiring special significance in connection with the processes of globalization and the spread of media in recent decades. The purpose of the article is to study the prospects for the use of media education in the formation of the legal social culture and a culture of human rights. Materials and methods. Based on a study of domestic and foreign sources, (...) issues of media education, media literacy, spiritual and moral education, the legal culture of society, the phenomenon of post-truth and ways of forming critical, creative thinking are considered. The use of general scientific, philosophical, and socio-pedagogical methods has made it possible to study media education as a dialogue of learning, stimulating the development of rational, critical thinking, focused on the search for the value foundations of intellectual and social activity. Results. The development of the field of information and communication technologies determines the principles for the formation of the content and orientation of modern education. Media education is interlinked with the development of democracy and human rights. It influences the formation of a culture of citizen participation, their active social position, civic and political culture. Media education plays a significant role in shaping the legal culture of society since critical media research and information research focuses on the analysis of power structures and structures of dominance in the media. A study of the interpretations of the concepts of "media education" and "media literacy" made it possible to show that media education focuses a person on a critical approach to media content. One of the main issues of media education is teaching a person the skills to critically study media and media technologies, which involves addressing the technological, cultural and historical specifics of specific media used at a specific time and place. Information and communication technologies have changed the way of life, work, communication, and ways of selfpresentation, the formation of values, participation in socially significant events. Therefore, a critical approach to mass media should be based on knowledge of socio-philosophical theories, ethics and research in the field of mass media. Discussion. Mass media are constructing a history of human rights, which updates the topic of the media policy of human rights, combining socio-legal, cultural and media theories. Education in the field of acquiring information perception skills, the ability to correctly understand the importance of audiovisual images, to competently handle and navigate information flows are necessary for the life of a modern person in society. (shrink)

Holistic Education as the Ultimate Defense Against Misinformation -/- Introduction -/- In the modern world, young minds are constantly exposed to various influences—media, social networks, religious teachings, and cultural traditions. Many of these influences do not prioritize truth but instead serve political, economic, or ideological agendas. As a result, false beliefs and propaganda have become powerful tools in shaping public perception and decision-making, often leading to societal imbalance, irrational behavior, and unnecessary suffering. -/- Given this reality, a holistic educational (...) system provides the best solution. By integrating critical thinking, the universal law of balance in nature, and foundational knowledge, education can serve as a shield against misinformation. Just as propaganda can manipulate young minds, education—when structured properly—can empower them to think independently, seek truth, and reject false narratives. -/- The Power of Influence: Media, Propaganda, and False Beliefs -/- Throughout history, those in power have controlled narratives through propaganda, misinformation, and selective truth-telling. Media corporations, political leaders, religious institutions, and even educational systems have used information as a weapon, shaping how people perceive reality and make decisions. -/- 1. Media Influence – The media has the ability to set public agendas, exaggerate fears, or distract people from critical issues. For example, mainstream news can focus on divisive social issues while ignoring deeper economic or political corruption. -/- 2. Religious and Cultural Dogma – Many belief systems discourage questioning and critical thinking, demanding blind faith instead of rational analysis. This leads to a rigid mindset that resists change, even when faced with scientific or factual evidence. -/- 3. Political and Economic Propaganda – Governments and corporations often manipulate information to serve their interests. Politicians use fear-based messaging, while corporations promote consumerism and materialism, influencing people’s behaviors without them realizing it. -/- Because young minds are impressionable, they are especially vulnerable to these influences. If false beliefs and misinformation can be deliberately spread and accepted as truth, then why shouldn’t a holistic educational system be used to reverse this process and teach students how to think critically? -/- Holistic Education as a Solution -/- A holistic educational system is not just about memorizing academic subjects. It must focus on developing independent thinking, analytical reasoning, and a deep understanding of reality. Instead of simply feeding students facts, a holistic approach would train them to evaluate information critically and recognize misinformation. -/- 1. The Universal Law of Balance in Nature -/- The foundation of this educational system should be the universal law of balance in nature—the principle that every action and decision must align with the natural equilibrium of life. This law ensures that: -/- Decisions are based on reality rather than emotions or biases. -/- All actions are examined for their long-term consequences on individuals and society. -/- People are trained to recognize imbalance in information and behavior. -/- By integrating this law into education, students will develop a natural resistance to misinformation because they will instinctively analyze whether a piece of information aligns with reality or disrupts balance. -/- 2. Critical Thinking and Logical Analysis -/- Students must be trained to question everything—including media, religious teachings, political statements, and social norms. They should learn: -/- How to recognize logical fallacies in arguments. -/- How to cross-check sources before believing information. -/- How to identify hidden biases in media and propaganda. -/- By sharpening their analytical skills, students will develop a mindset that seeks truth rather than blindly accepting narratives. -/- 3. Media and Information Literacy -/- Most young people today consume news and content through digital platforms, where misinformation spreads easily. Schools should teach: -/- How media operates (advertising, corporate influence, political agendas). -/- How narratives are constructed to manipulate emotions. -/- How to fact-check information using independent sources. -/- With these skills, students will be able to differentiate between objective reality and manipulative messaging. -/- 4. Emotional Intelligence and Psychological Resilience -/- Propaganda often exploits human emotions—fear, anger, hope, or desire. When individuals react emotionally rather than rationally, they become easier to manipulate. Schools must teach students how to: -/- Recognize emotional manipulation in media and politics. -/- Maintain psychological balance when confronted with conflicting information. -/- Make decisions based on reason rather than impulsive emotions. -/- By strengthening emotional intelligence, students will not only think critically but also remain calm and rational in the face of manipulation. -/- The Impact of Holistic Education on Society -/- If this holistic educational system were widely implemented, its impact on society would be profound. A well-informed, critically thinking population would: -/- Resist political and corporate manipulation. -/- Reject false beliefs and harmful ideologies. -/- Hold leaders accountable for their actions. -/- Make balanced, rational decisions in both personal and collective matters. -/- This shift would lead to a world with less conflict, less corruption, and fewer social crises. Instead of societies being controlled by misinformation and fear, they would be governed by rational analysis and ethical decision-making. -/- Challenges and Implementation Strategies -/- While holistic education is the ideal solution, implementing it will require overcoming several challenges: -/- 1. Resistance from Established Institutions – Many religious, political, and media institutions benefit from controlling narratives. These groups may oppose an educational system that promotes independent thinking. -/- Solution: Gradually introduce holistic education through pilot programs, starting in progressive schools and expanding based on success. -/- 2. Teacher Training and Curriculum Development – Many educators are themselves products of biased educational systems. -/- Solution: Train teachers to embrace critical thinking, logical reasoning, and unbiased knowledge dissemination. -/- 3. Government and Policy Barriers – Governments often dictate education policies based on political interests. -/- Solution: Collaborate with international organizations and independent educational institutions to introduce holistic education frameworks outside government control. -/- Conclusion -/- If false beliefs, media influence, and propaganda can shape young minds, then a holistic educational system can do the same—but for truth, rationality, and balance. The battle for the future is not just about controlling information but about empowering individuals to analyze, evaluate, and make decisions based on the universal law of balance in nature. -/- Implementing such an educational approach is not just an option—it is a necessity. A world that prioritizes balanced decision-making over manipulation will be a world with greater harmony, less suffering, and more rational leadership. -/- The question is no longer whether we need holistic education, but how soon we can implement it. The longer we delay, the more young minds will be shaped by misinformation. Now is the time to act. -/- . (shrink)

This study sought to investigate the Human Resource and Management (HRM) Program of a preschool hereinafter referred to as the Early Years Learning Center (EYLC) in Mandaluyong City for purposes of this research study. This is a qualitative case study that delved particularly into the issue of employee retention, especially of seasoned teachers and staff of the subject learning center. It used the interview method to generate an in-depth analysis as it revisited its HRM Program. To triangulate the data gathered, (...) documents from websites and social media as well as artifacts were also utilized. There is a dearth of research on the Human Resource and Management Program in preschool education. Most studies are from India and China. In terms of the HRM Program in education, SWOT analysis was frequently used to help improve the program. There were negligible sources that used the balanced scorecard at the preschool level. Hence, a combination of the SWOT analysis and the Balanced Scorecard method were utilized - the SWOT Analysis to determine the problems the learning center faces vis-à-vis its HRM Program, and the balanced scorecard in the search for a solution or strategy to help solve the problem. Based on the study, these combined tools proved to be effective in revisiting the HRM Program of schools. After interviewing the Directress of EYLC, findings disclosed that to be able to improve the retention of employees in the learning center, the focus should be given to the hiring policies and procedures keeping in mind the job security of its targeted employees. This brings forward concerns over advertising schemes utilizing different venues and platforms as well as issues involving the provision of seasoned employees with a retirement package, comprehensive Health Management Organization (HMO), and a continuing professional development program. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive (...) feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remote sensing monitoring has two important sub fields viz, classification and change detection. Change detection from remotely sensed images is a process that utilizes the images acquired over the same geographical area at different times to identify the changes that may have occurred between the considered acquisition dates. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are (...) limited to four spectral bands, which is insufficient to identify some natural vegetation formations. To address this issue, this work intends to use multispectral and high spatial resolution data (Sentinel-2) to do the task of natural vegetation analysis using a popular CNN architecture. This proposed project concept is completely based on Deep Learning architecture using Remote Sensing technology for vegetation of lands in Hassan city of Karnataka India. The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The changes of various vegetations are compared and their changes difference is displayed. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

A growing number of young people with disabilities is pursuing university education. Available research on the impact of Information and Communication Technologies on this matter has mainly focused on assistive technologies and their compensatory role for the adjustment of this group of students to the tertiary setting. However, limited research has looked at the role played by digital technologies such as social media and mobile devices in the transition to university, a critical period of change for all students but (...) more challenging for those with impairments. This paper presents the findings of an empirical study that investigated the experiences of students with vision impairments aged 18 to 24 who were transitioning to a New Zealand university. The findings draw from observations, a researcher diary, focus groups, individual interviews, and data from social media. The study found that new technologies play several enabling roles that help students to manage diverse transition challenges. These roles not only include aspects such as impairment compensation, communication, information, and learning but also support arrangement, collaboration, and social connection and participation. By incorporating in the analysis the potential of digital tools, the article updates and expands the understanding of the role of ICTs in higher education from experiences and views of young people with vision impairments. (shrink)

After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable (...) calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

-/- Reprogramming Society: Aligning Human Learning, Education, and AI with the Universal Law of Balance -/- Introduction -/- Throughout history, human societies have struggled with misinformation, irrational decision-making, and social imbalance. The root cause of these issues lies in the way human minds are programmed from birth. Negative thinking and behavior are not inherent traits but the result of flawed learning systems that fail to align with the universal law of balance in nature. To correct this, a holistic transformation (...) of education, governance, media, and AI is necessary. This essay explores how all human learning follows natural laws, how misinformation disrupts balance, and how a properly designed educational system—supported by AI—can reprogram society toward rational and stable decision-making. -/- The Principle of Learning and the Universal Law of Balance -/- All humans are born with the same fundamental learning mechanisms, guided by feedback and adaptation. Learning is a continuous process of correcting imbalances through experience, much like how natural systems maintain equilibrium. However, when incorrect information, false beliefs, or societal conditioning interfere, they create negative programming that leads to irrational decisions and harmful behaviors. -/- Under the universal law of balance, every thought and action should ideally contribute to stability. When individuals or societies operate outside this balance—due to ignorance or misinformation—they generate problems such as corruption, social injustice, and environmental destruction. Therefore, for humanity to function harmoniously, its learning systems must be structured to filter out falsehoods and reinforce rational thinking based on natural laws. -/- The Role of Education in Correcting Negative Programming -/- Current educational systems fail because they do not integrate the principle of balance. Instead of fostering critical thinking and truth-based decision-making, they often reinforce biases, outdated ideologies, and economic or political agendas. This misalignment perpetuates misinformation, leading to poor governance and societal dysfunction. -/- To correct this, education must be redesigned to follow the universal law of balance. This means: -/- 1. Teaching critical thinking – Students must learn how to evaluate information and recognize misinformation. -/- 2. Eliminating dogma-based teaching – While religious and cultural beliefs can coexist with science, education should be rooted in objective reality. -/- 3. Promoting holistic understanding – Instead of fragmented knowledge, subjects should be interconnected to reflect the natural balance of life. -/- 4. Instilling ethical decision-making – Education should train individuals to make choices that maintain societal equilibrium rather than disrupt it. -/- Governance, Media, and the Spread of Misinformation -/- Education alone is not enough; governance and media must also align with the law of balance. Governments often create policies based on political or economic pressures rather than natural equilibrium, leading to resource mismanagement, social inequality, and environmental crises. Meanwhile, media—whether traditional or digital—plays a crucial role in shaping public perception but often spreads misinformation for profit or control. -/- To fix these issues: -/- Governance must be based on rational, balanced decision-making, using policies that prioritize sustainability and long-term stability over short-term gains. -/- Media should be held accountable for spreading misinformation, ensuring that public discourse is grounded in truth rather than manipulation. -/- The Role of AI In Reprogramming Society -/- Artificial intelligence offers a unique opportunity to accelerate the reprogramming of human minds and societal systems. Unlike human institutions, AI can process vast amounts of data without emotional or ideological bias. If AGI is programmed according to the universal law of balance, it can function as an analytical system that: -/- Filters misinformation from education, governance, and media. -/- Validates the accuracy of knowledge before it influences decision-making. -/- Guides leaders and citizens toward rational, balanced choices. -/- However, for AI to achieve this, it must be designed with a deep understanding of the universal formula. AI developers must either be educated on this principle or integrate it directly into AI training models to ensure that future AGI serves as a force for balance rather than chaos. -/- Conclusion -/- Human society is at a critical juncture where misinformation, flawed education, and unbalanced governance threaten global stability. Since all negative human thinking and behavior stem from negative programming, the solution lies in a complete reformation of education, governance, and media—guided by the universal law of balance. AI, if programmed correctly, can serve as the ultimate tool to reprogram society, ensuring that human learning, decision-making, and leadership align with the natural order. Only through this holistic transformation can humanity achieve true progress and harmony. -/- . (shrink)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. (...) In the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

In recent years, the prediction of air quality has become a critical task due to its significant impact on human health and the environment. With urbanization and industrial growth, the need for accurate air quality forecasting has become more urgent. Traditional methods for air quality prediction are often based on statistical models or physical simulations, which, while valuable, can struggle to capture the complexity of air pollution dynamics. This study explores the use of deep learning techniques to predict air (...) quality, providing a comparative analysis of different neural network models and their performance. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several (...) constraints, especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

This study aimed to unveil the sentiments of college students' feedback on their teacher's teaching performance during online classes. The research utilized a qualitative approach, explicitly employing text mining. Orange software determined the most frequently used words illustrating teachers' teaching performance during online courses. Moreover, Latent Semantic Indexing (LSI) was utilized to reveal how those frequently used words were structured. Meanwhile, the study showed that the words "teacher," "class," "us," "student," "online," "teaching," "happy," "learning," "understand," and "good" were most frequently (...) used in college students' sentiments regarding their teacher's performance during online classes. The study also indicated that most students have positive feedback on their teachers' teaching performance in online courses. Therefore, the findings suggest that teachers have the abilities and knowledge to successfully impart lessons to their students despite the enormous dilemma the pandemic has brought about in every sector, especially in education. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful (...) Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating (...) habits. (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big data analysis is becoming one (...) of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Since at least 2016, many have worried that social media enables authoritarians to meddle in democratic politics. The concern is that trolls and bots amplify deceptive content. In this chapter I argue that these tactics have a more insidious anti-democratic purpose. Lies implanted in democratic discourse by authoritarians are often intended to be caught. Their primary goal is not to successfully deceive, but rather to undermine the democratic value of testimony. In well-functioning democracies, our mutual reliance on testimony also (...) generates a distinctively democratic value: decentralized testimonial networks evade control by the state or powerful actors. The deliberate exposure of deception in democratic testimonial networks undermines this value by implicating citizens in their own epistemic corruption, weakening the resilience of democratic society against authoritarian pressure. In this chapter I illustrate that danger through a close reading of recent Russian social media interference operations, showing both their epistemic underpinnings and their ongoing political threat. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

The rise of social media has brought with it a significant increase in fake profiles, which are used for malicious activities like spamming, impersonation, and spreading misinformation. Existing detection methods, which rely on manual reporting and basic automated systems, are often insufficient in identifying these sophisticated fake accounts. To address this challenge, we propose the development of an AI-driven software application designed to automatically detect and report fake social media profiles. By analyzing user behavior, content patterns, and metadata, (...) the system will provide a more effective and scalable solution for crime branches and investigative agencies, helping to enhance online security and protect users from the dangers posed by fake accounts. (shrink)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of expectations: (...) the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

his project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by (...) first capturing an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. (shrink)

.Terrain recognition is critical in various applications, including autonomous navigation, disaster response, and remote sensing. Traditional methods rely heavily on convolutional neural networks (CNNs), which require significant computational resources for high accuracy. Vision transformers (ViTs) have recently emerged as a novel approach to image processing, offering superior capability in processing long-range dependencies in visual data. This paper proposes a terrain recognition model based on Vision Transformers, aiming to improve classification accuracy and processing efficiency on complex terrain datasets. Key steps include (...) pre-processing satellite imagery, feature extraction through transformer architecture, and performance evaluation. Our results demonstrate that ViTs significantly enhance recognition accuracy, making them a promising alternative to CNNs in terrain analysis tasks. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

This study examines social media's quantitative effect on juvenile criminality. The researcher intends to quantify how social media usage affects juvenile delinquency. The research will examine mediating elements, including peer influence, self-esteem, and antisocial content. This study may educate parents, educators, politicians, and mental health experts on adolescent social media usage hazards. This study aims to establish evidence-based social media mitigation and youth development solutions. This research employed quantitative methodologies. The target population for this study will (...) be two hundred (200) criminology students of Headstart College of Cotabato. Participants will be selected using a time-efficient random selection strategy. The findings of a power analysis will be used to calculate the sample size needed to achieve the desired level of statistical significance. The researcher will administer the survey questionnaire face-to-face after proper coordination with the College head. Respondents had enough time to complete the survey questions. The researcher and respondents will complete the questionnaire in their own time to gather and retrieve data: data quantification, categorization, and statistical description. Weighted mean and correlation were utilized to assess the association between social media use and teenage delinquency and behavior. The researcher carried out all methods using SPSS Application Guide as statistical software. The study revealed that excessive social media use is associated with negative outcomes such as mental health issues, decreased social interactions, and potential impacts on academic performance. Additionally, the level of parental monitoring and communication regarding social media usage was varied, and family conflict related to social media use was evident to some extent. There needs to be more research done to determine the mechanisms that link adolescent social media usage and behavior. Longitudinal studies may help explain what happens and why, as well as suggest any necessary countermeasures. (shrink)