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A life engineering perspective on algorithms, AI, social media, and quantitative metrics: A review of three of the most thought-provoking books on the impact of tech on human life

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Informatik-Spektrum 2024年第5期47卷 141-147页

作者： Bigea, Georgiana Mumtaz, Maria Portmann, Edy Swaminathan, Jennifer Vedasri, Nakka University of Fribourg HumanIST Institute Fribourg Switzerland Joint Masters in Computer Science Fribourg Switzerland

This academic paper delves into the captivating intersection of life engineering and algorithms, artificial intelligence (AI), social media, and quantitative metrics on human life, through a comprehensive review of three thought-provoking books. In each critical review, the authors add their own thoughts and impressions, as computer science graduates and scholars, illustrating the impact that these eye-opening books have on them. The first book, "Weapons of Math Destruction" by Cathy O’Neil, delves into the hidden dangers of algorithmic decision-making. O’Neil uncovers how algorithms can perpetuate discrimination, biases, and unfairness in domains such as education, advertising, criminal justice, employment, and finance, and emphasizes the need for ethical considerations, transparency, and human judgment in algorithmic systems. The second book, "Atlas of AI" by Kate Crawford, takes a multidimensional approach to AI beyond mere algorithms and deep learning. Crawford addresses issues such as labor exploitation, surveillance technologies, classification systems, wealth concentration, and environmental consequences due to AI. The book calls for responsible and ethical considerations in the development and usage of AI. Shoshana Zuboff’s "The Age of Surveillance Capitalism" is the third book, focusing on the pervasive influence of tech giants like Google and Facebook. Zuboff exposes the dynamics of surveillance capitalism, wherein personal data is extracted and exploited for economic gains. The book illuminates how this form of capitalism erodes privacy, reshapes societal structures, and challenges democratic norms. Illustrating the essence of these disruptive narratives and the tense dialogue taking place between ethicians or scholars and technology developers, this research examines the profound social, economic, and environmental implications brought forth by these transformative technologies. Ultimately, the paper advocates for the embrace of responsible and ethical t

关键词： Deep learning

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Tracing Transaction across bitcoin address behaviour for pattern analysis 1

Tracing Transaction across bitcoin address behaviour for pat...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Parihar, Ankita Kumar, Amit Kumar, Abhishek Chandigarh University Masters of Engineering - Cse Department of Computer Science India

ISBN: (纸本)9798350392111

Internet introduce modern digital currencies which have evolved from our fiat currencies. As the world faced a recession driven by centralized assets, an evolution took place. Additionally, banks charged negative interest on tangible property. When money was in danger, the Internet provided a solution that altered the global system of currency ***, the pioneering cryptocurrency that has revolutionized digital finance. An unchangeable record of every transaction is kept through the use of blockchain technology in it. The public disclosure of a cryptocurrency's whole the formation and transaction history fosters user confidence. People on the Bitcoin network deal with one another using anonymous Bitcoin addresses, never disclosing their true identities. To optimize their privacy and convenience, individuals frequently utilize many distinct email addresses. In this study it also examines academic literature to understand the various uses of Bitcoin. A comprehensive review of existing research is conducted to detail the characteristics and applications of Bitcoin. The paper utilizes primary information from scholarly articles and secondary data from publicly accessible case studies that are relevant to the topic. Beyond developing Bitcoin faces several challenges, including regulatory uncertainty, scalability issues. Although its initial appearance seemed to indicate hope for a better future, now it is hard to predict how bitcoin will emerge. © 2024 IEEE.

关键词： Decentralized finance

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis 16

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arora, Rakhi Dixit, Nitin Sihare, Muskan Kinger, Shreshtha Shivhare, Sneha Kulshreshtha, Komal Institute of Technology & Management Computer Science and Engineering Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (纸本)9798331505264

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python. © 2024 IEEE.

关键词： Logistic regression

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Enhancing PM2.5 Predictions Using Combination of Graph Convolutional Network and Bi-LSTM 9

Enhancing PM2.5 Predictions Using Combination of Graph Convo...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kunar, Prakash Nithish, R. Sudha Hindustan Institute of Technology and Science Department of Masters of Computer Applications India

ISBN: (纸本)9798350397376

In recent years, the process of industrialization and urbanization has accelerated the global economy, which benefits people but has serious environmental consequences. Air quality has become a major concern for both countries and individuals. We propose a very effective method for predicting PM2.5 levels in Chennai using Graph Convolutional Networks (GCN) and Bi-directional Long Short-term Memory (Bi-LSTM) models. As input features, we use historical PM2.5 data, wind speed, humidity, temperature and precipitation. To improve the model's robustness, data preprocessing methods such as Data Cleaning, Data, Data Transformation and Data Reduction was *** shifting the time-series data by a small time-interval, Randomly scaling the data by a small factor and Randomly applying noise to the data was done during data Augmentation. GCN works by propagating information between nodes in the graph, with each node's representation being updated based on the representations of its neighbors. In this case, the nodes would correspond to different locations in the city, and the edges would represent the spatial relationships between them. whereas the Bi-LSTM component captures the temporal relationships. The proposed method is evaluated on a real-world data-set and shows significant improvement in prediction accuracy compared to traditional methods. The performance of the model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE). © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection 3rd

Deep Learning Precision Farming: Identification of Banglades...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Siddiki, Marjuk Ahmed Rony, Mohammad Abu Tareq Naim Hossain, Md. Saha, Pritom Islam, Mohammad Shariful Ahmed, Ishtiak Satu, Shoykth Shaharior Ahammad, Mejbah Nazmul Alam, Shah Md. Computer Science and Engineering Daffodil International University Dhaka Bangladesh Noakhali3814 Bangladesh Noakhali3814 Bangladesh Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Computer Science and Engineering Shaikh Burhanuddin Post Graduate College Dhaka Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University TempeAZ United States

ISBN: (纸本)9789819739363

The dynamic potential of Convolutional Neural Networks (CNN) enhanced by Transfer Learning offers a promising avenue for object recognition within the realm of agriculture. To explore this potential in the context of Bangladeshi agriculture, this study leverages CNN enhanced by Transfer Learning to identify Bangladeshi-grown fruits from a dataset featuring 24 distinct types, including Mango, Jackfruits, and Lychee, among others. Utilizing an extensive dataset that encapsulated high-quality images of these fruits, we embarked on a comparative analysis of six advanced CNN architectures: VGG19, Xception, ResNet152V2, MobileNet, DenseNet201, and NASNetLarge. The results were illuminating. While VGG19 lagged behind with an accuracy of 48%, most models showcased impressive performance, with DenseNet201 emerging as the frontrunner at a remarkable 95% accuracy. The findings not only underscore the transformative role of Transfer Learning in optimizing CNN performance, especially when faced with limited labeled datasets, but also spotlight the applicability of these models in revolutionizing quality control and management within the Bangladeshi agricultural sector. As the demand for automated and accurate agricultural systems gains traction globally, the insights from this research offer a foundation for further exploration and application in similar contexts, demonstrating the scalability and adaptability of Transfer Learning-enhanced CNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Facial Expression Recognition (FER) System Using Deep Learning 1

Facial Expression Recognition (FER) System Using Deep Learni...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Yan, Ooi Juin Ashraf, Humaira Ihsan, Uswa Jhanjhi, Nz Ray, Sayan Kumar Kavita Taylor's University Masters of Applied Computing Selangor Malaysia Taylor's University School of Computer Science SCS Malaysia International Islamic University Department of Computer Science Islamabad Pakistan Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Sentiment analysis of both text and images have been widely implemented machine learning application. Facial expression recognition (FER) is a widely known application of sentiment analysis of image data. This application has already been implemented in various industry, but challenges such as the complexities of facial features and different image angle still remain. This project aims to develop a FER system capable of classifying human expressions through facial images using deep learning techniques. A CNN-based architecture was proposed and training using the FER2013 dataset, which is a commonly used facial expression dataset. Overall, the trained model achieved an accuracy of 63.97% through the dataset. The trained CNN model was then employed to develop a web-based prototype of a FER system through Python Flask. © 2024 IEEE.

关键词： Sentiment analysis

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Effective K-means clustering algorithm for efficient data mining 2

Effective K-means clustering algorithm for efficient data mi...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Dubey, Pranjal Rajavat, Anand Shri Vaishnav Vidyapeeth Vishwavidyalaya Masters of Engineering Computer Science Engineering Indore India Shri Vaishnav Vidyapeeth Vishwavidyalaya Computer Science and Engineering Indore India

ISBN: (纸本)9798350347982

With the advancement of technology and the increase of data, Data mining needs to be more efficient. Therefore, data mining along with machine learning and statistics gets effective to face today's scenario. Machine learning provides an easy way to accomplish tasks and statics provides a standard computational skill to understand, manage and analyze data. The work surrounds the enhancement of the K-means clustering algorithm to make it more effective and to create accurate clusters over large data sets. There are some flaws in the classical K-means algorithm the major is that it cannot work over large and dense data points. This paper aims to improve the k-means algorithm so that it can create more smooth and more accurate clusters over highly dense data points. To accomplish this window density method is being used to create more homogeneous clusters. This will make the K-means algorithm suitable for all shapes and sizes of clusters. This will also enable handling noise effects, spotting outliers, and discovering clusters other than hyper ellipsoids. We have introduced the K-means algorithm with window density and derived that the results are more accurate than the original K-means algorithm as we have evaluated the outcomes through purity measure. © 2023 IEEE.

关键词： K-means clustering

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Healthy Eats - Online Food Delivery That Urge To Eat Healthy and Live More 6

Healthy Eats - Online Food Delivery That Urge To Eat Healthy...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Reddy, Janupally Vedika Reddy, Janupally Bindu Madhuri Computer Science and Engineering B V Raju Insitute of Technology Medak Narsapur India Masters in Computer Science Texas State University 601 University Dr San MarcosTX78666 United States

ISBN: (纸本)9798350394634

The demanding growth for the healthy food options and increasing organic food has led us, to the emergence of this new innovation healthy Eats website which is a perfect destination to urge eat healthy and live more,Moving from greater Himalayas to Kanyakumari India has diversified historical cuisines and our culture is strongly bonded from years to organic food and farms which our ancestors has been cultivated, cook and served, while compared to other cuisine it is important to make informed choices in this website we will achieve the process to make healthy food access to each and every individual, In this website we provide a unique feature where the customer can do a Zoom call to the chief and monitor regarding the food freshness, customer preference’s and food hygiene as well as customer can also customise their food and can lively view and suggest their preference and order from their near by vicinity .After the post pandemic we come across enormous change in the food habits,life style of people to overcome daily life style disorders, stress and lack of time we observe huge demand for the organic food,Since 10 years due to heavy civilization and huge urbanization, people have shifted to urban areas, due to lack of time and of healthy organic food in their desired way will create a huge demand to this website. Through our website we encourage farmers to supply the organic products ensuring the profit to the both the parties .Our healthy Eats will be working as the interface for both farmers and customers,Through our Healthy Eats website we will approach the farmers and then buy the organic products from the farmers and we check the farmers have the organic certification to ensure that their products meet the organic *** also consider components such as variations,cost of the production, and market needs. Transparency and Integrity are the main keys to maintain the long term partnerships. Our website will also ensure the profit-sharing model that will b

关键词： Websites

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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