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Generative artificial intelligence for sustainable development: predictive trend analysis in key sectors using natural language processing

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Discover Applied sciences 2025年第6期7卷 1-26页

作者： Sharma, Chetan Sharma, Shamneesh Bhardwaj, Vivek Dhaliwal, Balwinder Kaur PW-Institute of Innovation PhysicsWallah Limited Lucknow India upGrad Education Private Limited Bangalore India School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Generative Artificial Intelligence (GenAI) has revolutionized multiple industries by improving efficiency and promoting innovation in fields such as business, education, healthcare, and cybersecurity. This study evaluates the transformative impact of GenAI on advancing the United Nations' Sustainable Development Goals (SDGs) by analyzing research trends and applications. In this research, authors utilized Latent Dirichlet Allocation (LDA), a Natural Language Processing (NLP) technique. The work delineates emergent themes and sector-specific progressions in GenAI research. The dataset consists of 2162 research articles published from 2020 to 2025, obtained from the Scopus database. The abstracts of these publications provide the foundation for analysis, facilitating a thorough examination of literature. The authors provided 10 topics, which are recent trends that future researchers can explore. The results indicate GenAI's capability in processing automation, creative enhancement, and innovation promotion while addressing ethical concerns such as prejudice, privacy, and social effects. The study indicates potential directions for ethical technology adoption by analyzing trends in GenAI applications. This work highlights the essential requirement for interdisciplinary methods and ethical frameworks to optimize GenAI's contributions to innovation while assuring alignment with sustainable and equitable development goals. © The Author(s) 2025.

关键词： Ethical technology

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Securing the Digital Realm: Unmasking Fraud in Online Transactions Using Supervised Machine Learning Techniques 6th

Securing the Digital Realm: Unmasking Fraud in Online Transa...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Reddy, G. Yuktha Kokatnoor, Sujatha Arun Kumar, Sandeep Department of Computer Science and Engineering School of Engineering and Technology Christ University Bangalore India

ISBN: (纸本)9789819774227

A key component of contemporary banking systems and e-commerce platforms is identifying fraud in online transactions. Traditional rule-based techniques are insufficient for preventing sophisticated fraud schemes because of the increasing complexity and number of expanding online transactions. This research study examines the development of fraud detection methods, emphasizing data analytics and machine learning (ML) models. The study also focuses on the fact that developing efficient fraud detection systems requires continuous observation, data preprocessing, feature selection, and testing of models. Seven ML models, Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbors (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) are considered for classifying the dataset into fraudulent or not. During the experimentation study, it was observed that XGBoost yielded the highest accuracy of 99% when compared to other models. Users can determine which features significantly influence the model's predictions by using XGBoost's feature significance insights. Additionally, XGBoost provides integrated support for managing missing values in data, negating the requirement for imputation and other preprocessing procedures. Due to these, it performed better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Optimizing Menstruation Symptom Management: A Machine Learning Approach to Personalized Nutrition Recommendations

Optimizing Menstruation Symptom Management: A Machine Learni...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Logapriya, E. Surendran, R. Sathish Kumar, J.P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals' day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly. © 2024 IEEE.

关键词： Support vector machines

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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgment Prediction with Legal Clue Tracing

SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgmen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Pengjie Zhang, Wang Ding, Yulong Zhang, Xuefeng Yang, Shuang-Hua College of Science Northeastern University China School of Computer Science and Engineering Southern University of Science and Technology China University of Reading Department of Computer Science United Kingdom

ISBN: (纸本)9781665410205

Legal Judgment Prediction (LJP) aims to form legal judgments based on the criminal fact description. However, researchers struggle to classify confusing criminal cases, such as robbery and theft, which requires LJP models to distinguish the nuances between similar crimes. Existing methods usually design handcrafted features to pick up necessary semantic legal clues to make more accurate legal judgment predictions. In this paper, we propose a Semantic-Aware Dual Encoder Model (SEMDR), which designs a novel legal clue tracing mechanism to conduct fine-grained semantic reasoning between criminal facts and instruments. Our legal clue tracing mechanism is built from three reasoning levels: 1) Lexicon-Tracing, which aims to extract criminal facts from criminal descriptions;2) Sentence Representation Learning, which contrastively trains language models to better represent confusing criminal facts;3) Multi-Fact Reasoning, which builds a reasons graph to propagate semantic clues among fact nodes to capture the subtle difference among criminal facts. Our legal clue tracing mechanism helps SEMDR achieve state-of-the-art on the CAIL2018 dataset and shows its advance in few-shot scenarios. Our experiments show that SEMDR has a strong ability to learn more uniform and distinguished representations for criminal facts, which helps to make more accurate predictions on confusing criminal cases and reduces the model uncertainty during making judgments. All codes will be released via GitHub. © 2024 IEEE.

关键词： Semantics

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EAODroid: Android Malware Detection Based on Enhanced API Order

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Chinese Journal of Electronics 2023年第5期32卷 1169-1178页

作者： HUANG Lu XUE Jingfeng WANG Yong QU Dacheng CHEN Junbao ZHANG Nan ZHANG Li School of Computer Science and Technology Beijing Institute of Technology Department of Media Engineering Communication University of Zhejiang

The development of smart mobile devices brings convenience to people's lives, but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in the information age. Based on the fact that malware heavily resorts to system application programming interfaces(APIs) to perform its malicious actions,there has been a variety of API-based detection *** of them do not consider the relationship between APIs. We contribute a new approach based on the enhanced API order for Android malware detection, named EAODroid, which learns the similarity of system APIs from a large number of API sequences and groups similar APIs into clusters. The extracted API clusters are further used to enhance the original API calls executed by an app to characterize behaviors and perform classification. We perform multi-dimensional experiments to evaluate EAODroid on three datasets with ground truth. We compare with many state-of-the-art works, showing that EAODroid achieves effective performance in Android malware detection.

关键词： Performance evaluation Privacy Programming Information age Malware Mobile handsets Behavioral sciences

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data

Extended Multi-scale Feature Fusion and Balanced Generative ...

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2024 Workshop on Electronics Communication engineering, WECE 2024

作者： Xiao, Ting Sun, Minqian Chen, Haoyuan Li, Jiaqi Qiu, Yixing Wang, Zhe Lu, Dan Department of Computer Science and Engineering East China University of Science and Technology China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University China School of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Image inpainting has made remarkable progress in recent years due to the availability of sufficient training data. However, when the training data is insufficient, i.e., image inpainting in few-shot regimes, the performance of existing image inpainting methods degrades drastically. Two challenges impede image inpainting under few-shot regimes, adequate information and unstable training. To address the above issues, this paper proposes a new method for image inpainting under limited data. Specifically, an Extended Multi-scale Feature Fusion (EMFF) module is proposed to capture representative contextual information by progressively reducing the number of channels for features with increasing receptive fields, thereby utilizing effective information. Additionally, we leverage a multi-scale discriminator whose cross-layer features are mixed for supplementation to formulate a Balanced Generative Adversarial Network (BGAN) to stabilize the training dynamics. Comprehensive experiments demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Image fusion Image quality Feature fusion Education and training Image restoration Convolution Data modeling Image enhancement Gallium nitride Semantics

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A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTATION ACROSS ANIMALS AND MACHINES 12

A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Garimella, Manjulata Pak, Denizhan Wood, Justin N. Wood, Samantha M.W. Department of Informatics School of Informatics Computing & Engineering Department of Psychological & Brain Sciences Cognitive Science Program Program in Neuroscience

Newborn brains rapidly learn to solve challenging object perception tasks, including segmenting objects from backgrounds and recognizing objects across new viewing situations. Conversely, modern machine learning (ML) algorithms are "data hungry," requiring more training data than brains to reach similar performance levels. How do we close this learning gap between brains and machines? Here, we introduce a new benchmark-a Newborn Embodied Turing Test (NETT) for object segmentation-in which newborn animals and machines are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning. First, newborn chicks were raised in controlled environments containing a single object rotating on a single background, then their recognition performance was tested across new backgrounds and viewpoints. Second, we performed "digital twin" experiments in which artificial agents were reared and tested in virtual environments that mimicked the rearing and testing conditions of the chicks. We inserted a variety of ML "brains" into the artificial agents and measured whether those algorithms learned common object recognition behavior as chicks. All newborn chicks solved this one-shot object segmentation task, successfully learning background-invariant object representations that generalized across new backgrounds and viewpoints. In contrast, none of the artificial agents solved the task, instead learning background-dependent representations that failed to generalize across new backgrounds and viewpoints. This digital twin design exposes core limitations in current ML algorithms in developing brain-like object perception. Our NETT is publicly available for comparing ML algorithms with newborn chicks. We argue that NETT benchmarks can help researchers build embodied AI systems that learn as efficiently and robustly as newborn brains. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Animals

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