Specific Dataset Research Articles

Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity

The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow.Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances.To evaluate EvoFlow’s effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow’s unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow’s specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered.

Malware Detection with Artificial Intelligence: A Systematic Literature Review

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is limited by the widespread use of obfuscation. Dynamic analysis is not impacted by obfuscation but is defeated by ubiquitous anti-analysis techniques and requires more computational power. Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and, combined with poor quality datasets, this can lead to a situation where a model achieves high accuracy with only one specific dataset.

A improved pooling method for convolutional neural networks

The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it to control whether the output features of the input data are based on the maximum values or weighted averages. By learning the optimal pooling strategy during training, our custom pooling layer can effectively capture and represent discriminative information in the input data, thereby improving classification performance. Experimental results show that the proposed T-Max-Avg pooling layer achieves good performance on three different datasets. When compared to LeNet-5 model with average pooling, max pooling, and Avg-TopK methods, the T-Max-Avg pooling method achieves the highest accuracy on CIFAR-10, CIFAR-100, and MNIST datasets.

MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy

The incidence of bladder cancer is on the rise, and its molecular heterogeneity presents significant challenges for personalized cancer therapy. Transcriptome data can characterize the variability among patients. Traditional machine-learning methods often struggle with high-dimensional genomic data, falling into the ’curse of dimensionality’. To address this challenge, we have developed MVMSGAT, an innovative predictive model tailored for forecasting responses to neoadjuvant therapy in bladder cancer patients. MVMSGAT significantly enhances model performance by incorporating multi-perspective biological prior knowledge. It initially utilizes the Boruta algorithm to select key genes from transcriptome data, subsequently constructing a comprehensive graph of gene co-expression and protein–protein interactions. MVMSGAT further employs a graph convolutional neural network to integrate this information within a multiview knowledge graph, amalgamating biological knowledge maps from various scales using an attention mechanism. For validation, MVMSGAT was tested using a five-fold cross-validation approach on two specific GEO datasets, GSE169455 and GSE69795, involving a total of 210 bladder cancer samples. MVMSGAT demonstrated superior performance, with the following metrics (mean ± standard deviation): AUC-ROC of 0.8724±0.0511, accuracy of 0.7789±0.068, F1 score of 0.8529±0.0338, and recall of 0.9231±0.0719. These results underscore the potential of MVMSGAT in advancing personalized treatment and precision medicine in bladder cancer.

An automated approach for binary classification on imbalanced data

Imbalanced data are present in various business sectors and must be handled with the proper resampling methods and classification algorithms. To handle imbalanced data, there are numerous resampling and learning method combinations; nonetheless, their effective use necessitates specialised knowledge. In this paper, several approaches, ranging from more accessible to more advanced in the domain of data resampling techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suitable combinations of techniques for a specific dataset by extracting and comparing dataset meta-feature values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results than state-of-the-art solutions and with a much smaller execution time.

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

Aggregating intrinsic information to enhance BCI performance through federated learning

Insufficient data is a long-standing challenge for Brain–Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

Survey: Rainfall Prediction Precipitation, Review of Statistical Methods

Rainfall precipitation prediction is the process of using various models and data sources to predict the amount and timing of precipitation, such as rain or snow, in a particular location. This is an important process because it can help us prepare for severe weather events, such as floods, droughts, and hurricanes, as well as plan our daily activities. Processing rainfall data typically involves several steps, which may vary depending on the specific data set and research question. Here is a general overview of the steps involved: (1) Collecting data: Rainfall data can be collected using various methods, including rain gauges, radar, and satellite imagery. The data can be obtained from public sources, such as government agencies or research institutions. (2) Quality control: Before using the data, it's important to check for errors or inconsistencies. This may involve identifying missing or incomplete data, outliers, or inconsistencies in measurement units. Quality control can be performed manually or using automated software. (3) Pre-processing: Once the data has been quality controlled, it may need to be pre-processed for analysis. This may involve aggregating the data to a specific temporal or spatial resolution, such as daily, monthly, or annual averages, or converting the data to a specific format. (4) Analysis: The processed data can be used for various types of analysis, such as trend analysis, frequency analysis, or spatial analysis. These analyses can help to identify patterns, changes, or relationships in the data. (5) Visualization: Finally, the results of the analysis can be visualized using graphs, maps, or other types of visualizations to help communicate the findings. Overall, processing rainfall data requires careful attention to detail and a clear understanding of the research question and data sources.

Nitrogen retrieval in grapevine (Vitis vinifera L.) leaves by hyperspectral sensing

Nitrogen (N) is a critical macronutrient that directly affects grapevine yield and quality but is also highly mobile in soil and can cause environmental contamination when over-applied. Monitoring leaf N content is useful for assessing vine N status and thereby improving N management plans, and the ability to assess N by remote sensing is desirable. Remote sensing technology has been utilized for retrieving N in agronomic crops since the 1990s. However, remote sensing has not been widely adopted for N management mainly due to variability in spectral patterns that emerge when training a model on one dataset and then applying it to a different dataset. Differences in environmental conditions, phenological stages, and plant varieties contribute to variability. This study aimed to understand the major factors limiting the generalizability of N-level retrieval algorithms in grapevine: the impact of leaf age and how calibrating nitrogen content, using either leaf dry mass (Nmass) or leaf area (Narea), influence the outcomes. This study also compares the performance among five major N retrieval approaches. We used spectral data from a hand-held hyperspectral sensor measuring wavelengths from 350 to 2500 nm from 664 individual leaf samples obtained from two grapevine varieties on three different sampling campaigns. Our findings indicate that while machine learning and chemometric approaches offer high accuracy levels (R2 = 0.78), the physical modeling approach utilizing the PROSPECT radiative transfer model (RTM) is capable of consistently retrieving N independent of specific dataset conditions and with an acceptable level of accuracy (R2 = 0.45) while using only 50% of the hyperspectral data. The estimation of Nmass using Proteinmass, which was calculated from Proteinarea retrieved by RTM and ground truth LMA, demonstrates a high potential for consistent retrieval of Total N (TN) in grapes. We also found that while calibrating a model by Narea can eliminate the negative impact of leaf age on prediction accuracy, Nmass still performed better in four out of five approaches tested. This study shows that a hybrid model that combines machine learning and RTM can alleviate the impact of leaf age on prediction accuracy while reducing 50% of spectral bands required and maintaining an acceptable level of accuracy (R2 = 0.54).

Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104

AbstractSupervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning‐based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870‐5‐104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over‐sampling, random under‐sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under‐sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1‐Score, receiver operating characteristics curves, and area under the curve. Additionally, 10‐fold cross‐validation shows no indication of overfitting in the created intrusion detection system model.

Comparative performance of machine learning models for the classification of human gait

The efficacy of human activity recognition (HAR) models mostly relies on the characteristics derived from domain expertise. The input of the classification algorithm consists of many characteristics that are utilized to accurately and effectively classify human physical activities. In contemporary research, machine learning techniques have been increasingly employed to automatically extract characteristics from unprocessed sensory input to develop models for Human Activity Recognition (HAR) and classify various activities. The primary objective of this research is to compare and contrast several machine learning models and determine a reliable and precise classification model for classifying activities. This study does a comparison analysis in order to assess the efficacy of 10 distinct machine learning models using frequently used datasets in the field of HAR. In this work, three benchmark public human walking datasets are being used. The research is conducted based on eight evaluating parameters. Based on the study conducted, it was seen that the machine learning classification models Random Forest, Extra Tree, and Light Gradient Boosting Machine had superior performance in all the eight evaluating parameters compared to specific datasets. Consequently, it can be inferred that machine learning significantly enhances performance within the area of Human Activity Recognition (HAR). This study can be utilized to provide suitable model selection for HAR-based datasets. Furthermore, this research can be utilized to facilitate the identification of various walking patterns for bipedal robotic systems.

Prediction of diabetes using supervised classification

Diabetes is one of the common chronic diseases that impacts 28.7 million people in the US as of 2019, accounting for 8.7% of the total population. Early identification of diabetes is very important in disease control and management. A number of prior studies provided compelling evidence that machine learning can help identify diabetes early allowing for timely treatment. It remains a challenge to appropriately assess, optimize and refine the classification models based on specific dataset for diabetes prediction with high accuracy. In this study, we aimed to develop a model with improved accuracy for diabetes prediction. We employed six learning algorithms, logistic regression, k-nearest neighbors (k-NN), support vector machine (SVM), decision tree, random forest, and gradient boosting on the Pima Indians Diabetes Dataset. The performance of each model was evaluated for the prediction of diabetes in validation datasets using accuracy, precision, recall, and F1-score. Gradient boosting provided an accuracy of 81.8%, outperforming all other classification models in most of the performance measures. Collectively, the gradient boosting model appeared to provide an appropriate algorithm for diabetes prediction with high accuracy based on the diagnostic measurements gathered in this specific dataset. Of note, the insights yielded from this exploratory study may only be applicable to this subpopulation of diabetes patients. It remains to be further validated with datasets derived from more diverse diabetes populations before the findings can be generalized to a wider diabetes patient population.

MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection

After several years of development, deep synthesis technology has made significant progress in image and video synthesis. Deep forgery represented by Deepfakes has become a research hotspot, which is used as a tool for disinformation attacks. The current strongly discriminative models can have good performance on specific datasets, even close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$100\%$</tex-math></inline-formula> accuracy. Unfortunately, since a specific discriminative method only fits a specific data distribution, and different forgery methods or datasets have different data distributions. These methods fail to achieve high performance in cross-dataset detection. In response to this problem and focusing on the actual situation, we adjust the strong generalization detection across the dataset to the generalization detection of unseen fake video. We propose Multi-Crise-Cross Attention and StyleGANv2 Generative Adversarial Network (MCS-GAN). Firstly, we built a Generative Adversarial Network (GAN) framework to learn the distribution of real face data and generate corresponding face images. Secondly, to break the high stitch between the fake region and the background, the model needs to have strong enough feature analysis and pixel restoration capabilities. Therefore, we propose a generator consisting of a Multi-Crise-Cross-Attention (MC) encoder and a StyleGANv2 (SG2) decoder. Finally, to avoid the situation where as long as a face is normal or different faces are abnormal, we set a latent space encoding discriminator and increase the ratio of latent space vector, so as to detect anomaly generated by the forgery operation acting on latent space. We conduct some model generalization experiments on videos on the Internet and some popular deepfake databases. The results show that the accuracy of our method is better compared with the best methods.

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning with Neural Architecture Search.

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data. However, existing federated learning MR image reconstruction methods rely on models designed manually by experts, which are complex and computationally expensive, suffering from performance degradation when facing heterogeneous data distributions. In addition, these methods give inadequate consideration to fairness issues, namely ensuring that the model's training does not introduce bias towards any specific dataset's distribution. To this end, this paper proposes a generalizable federated neural architecture search framework for accelerating MR imaging (GAutoMRI). Specifically, automatic neural architecture search is investigated for effective and efficient neural network representation learning of MR images from different centers. Furthermore, we design a fairness adjustment approach that can enable the model to learn features fairly from inconsistent distributions of different devices and centers, and thus facilitate the model to generalize well to the unseen center. Extensive experiments show that our proposed GAutoMRI has better performances and generalization ability compared with seven state-of-the-art federated learning methods. Moreover, the GAutoMRI model is significantly more lightweight, making it an efficient choice for MR image reconstruction tasks. The code will be made available at https://github.com/ternencewu123/GAutoMRI.

Breast Cancer Prediction: A Fusion of Genetic Algorithm, Chemical Reaction Optimization, and Machine Learning Techniques

Breast cancer is currently one of the most prevalent cancers affecting women globally. Uncontrolled growth and division of breast cells lead to the formation of tumors, marking the onset of breast cancer. Predicting breast cancer is essential for early detection, making treatment plans, and implementing preventive measures, ultimately improving patient outcomes and reducing mortality rates. In recent years, numerous studies have been published to predict breast cancer where researchers use a variety of methods. Most investigations have been conducted using narrow and specific datasets, often resulting in a lack of accuracy. Such methods may not be suitable for clinical use. The study aims to address the limitations of existing models in terms of robustness and generalization across diverse datasets. In our study, we employed two metaheuristic algorithms, namely, genetic algorithm (GA) and chemical reaction optimization (CRO) with machine learning techniques, including support vector machine (SVM), decision tree, random forest, and XGBoost. GA and CRO are used to optimize the feature selection process. It enables machine learning algorithms to predict more accurately. Experiments were conducted on three datasets, namely, Wisconsin Breast Cancer (WBC), Breast Cancer‐the University of California, Irvine (BC‐UCI), and Breast Cancer Coimbra (BCC) datasets. The datasets contain 569, 286, and 116 instances, respectively. The classifiers with optimized features consistently outperformed the classifiers without feature optimization in terms of accuracy, precision, recall, specificity, and F1 score. Among the compared methods published recently, our method attained the highest accuracies of 99.64% in the WBC dataset and 98% in the BCC dataset, as well as the second highest accuracy of 99.12% in the BC‐UCI dataset. Comparative analysis demonstrated the superiority of our approach over existing methods.

A trawl collected dataset of Johnius (Actinopterygii, Sciaenidae) species in central-western Taiwanese waters.

Sciaenidae is one of the most important coastal fisheries in Taiwan, both in production and economic value. It is also significant as the main targetted diet of Chinese white dolphins, Sousachinensis, especially for the genus Johnius, such as J.taiwanensis, J.belangerii and J.distinctus, which is primarily found in central-western Taiwan coastal waters. Despite an abundance of Johnius species occurrences reported in the Global Biodiversity Information Facility (GBIF) and the Taiwan Biodiversity Information Facility (TaiBIF) data portals (Mozambique, Australia, Taiwan, Korea, India, Indonesia, South Africa, Pakistan, Vietnam and China), there are no specific datasets that properly document the regional distribution of this genus, especially in Taiwanese waters. Thus, this paper describes a dataset of genus Johnius occurrences in waters on the central-western coast of Taiwan. The data collection for the present study was conducted from 2009 until 2020 and comprised 62 sampling events and 133 occurrence records. All fish specimens were collected by trawling in Miaoli, Changhwa and Yunlin Counties, Taiwan and brought back to the lab for identification, individual number count and body weight measurement. These processing data have been integrated and established in the Taiwan Fish Database and published in GBIF. This dataset contains six Johnius species and 2,566 specimens, making it comprehensive Johnius fish fauna and spatial distributional data on the coastal habitat in central-western Taiwanese waters. This dataset contains 133 occurrence records of Johnius species (Sciaenidae) with 2,566 specimens, making it the most extensive public dataset of Johnius distribution records in Taiwan. The publication of this dataset through the TaiBIF and GBIF dataset platforms demonstrated that the number of Johnius spatial and temporal records in Taiwan waters is influenced by the topographical structure of the Changyun Rise (CYR) in combination with the cold current of the China Coastal currents and bound with the warm currents of the Kuroshio and the South China Sea on the central-western coast of Taiwan. The data serve as the foundation for understanding the biogeography and Johnius species ecology in Taiwan's coastal waters, which present a 2°C water temperature difference split at the CYR.

Unsupervised domain adaptation with deep network based on discriminative class-wise MMD

&lt;abstract&gt; &lt;p&gt;General learning algorithms trained on a specific dataset often have difficulty generalizing effectively across different domains. In traditional pattern recognition, a classifier is typically trained on one dataset and then tested on another, assuming both datasets follow the same distribution. This assumption poses difficulty for the solution to be applied in real-world scenarios. The challenge of making a robust generalization from data originated from diverse sources is called the domain adaptation problem. Many studies have suggested solutions for mapping samples from two domains into a shared feature space and aligning their distributions. To achieve distribution alignment, minimizing the maximum mean discrepancy (MMD) between the feature distributions of the two domains has been proven effective. However, this alignment of features between two domains ignores the essential class-wise alignment, which is crucial for adaptation. To address the issue, this study introduced a discriminative, class-wise deep kernel-based MMD technique for unsupervised domain adaptation. Experimental findings demonstrated that the proposed approach not only aligns the data distribution of each class in both source and target domains, but it also enhances the adaptation outcomes.&lt;/p&gt; &lt;/abstract&gt;

A Generative Neighborhood-Based Deep Autoencoder for Robust Imbalanced Classification.

Imbalanced data classification is an actual and important issue in many real-world learning applications hampering most classification tasks. Fraud detection, biomedical imaging categorizing healthy people versus patients, and object detection are some indicative domains with an economic, social and technological impact, which are greatly affected by inherent imbalanced data distribution. However, the majority of the existing algorithms that address the imbalanced classification problem are designed with a particular application in mind, and thus they can be used with specific datasets and even hyperparameters. The generative model introduced in this paper overcomes this limitation and produces improved results for a large class of imaging and time series data even under severe imbalance ratios, making it quite competitive.

Information Recovery-Driven Deep Incomplete Multiview Clustering Network.

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multiview data. To date, existing IMC methods usually bypass unavailable views according to prior missing information, which is considered a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this article, we propose an information-recovery-driven-deep IMC network, termed as RecFormer. Concretely, a two-stage autoencoder network with self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote representation learning and further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.

Relationships between (Un)known consequences of Artificial Intelligence usage in an organizational or societal context

This study investigates the relationships between (un)known consequences of artificial intelligence (AI) and usage in an organizational or societal context. The study adopts a qualitative approach and toes an interpretive constructivist perspective. Used the resources of AI to construct the data corpus on which content analysis was performed. Additionally, to triangulate we also drawn on interview of six specialists with varied knowledge as consultants, strategists, developers and users, and also data from Digital Week held by PwC. Results show that the consequences of AI are complex and multifaceted thus requiring thorough planning, constitution of GenAI teams among users from business focal points to pose adequate and diverse questions and build consistent algorithms in line with policies to mitigate risks of negative impacts. Results also show that consequences of AI are highly context-dependent and can vary based on the specific application and dataset. The conceptions that have implications on the unknow known consequences of AI are Automation, Improved efficiency, Enhanced decision making, Personalization, Cost reduction, Healthcare advancements, Autonomous decisions, Natural Language Processing (NLP), Predictive Maintenance, Environmental monitoring, Fraud detection and Education & Training. Others are Scientific Research, Customer Services, Accessibility, Agricultural baseline, Security and Entertainment. Overall, to harness the values, addressing the challenges is inevitable to avoid pitfalls by turning GenAI more inclusive through continuous experimentation and maximizing the benefits of AI. AI have left a lasting imprint on our society and will continue to do so as AI technologies advance and become more integrated into various aspects of our lives. This study contributes to the debate on AI and serves as a limelight to the academia, professionals, and policy makers.