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New Approach of Artificial Intelligence in Digital Forensic Investigation: A Literature Review 1

International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Computing the Bounds of the Number of Reticulations in a Tree-Child Network That Displays a Set of Trees

arXiv

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arXiv 2023年

作者： Wu, Yufeng Zhang, Louxin Department of Computer Science and Engineering University of Connecticut StorrsCT06268 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this paper, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees. In addition, we also present some theoretical results on bounding from above the number of reticulations. Through simulation, we demonstrate that the new lower bounds on the reticulation number for tree-child networks can practically be computed for large tree data. The bounds can provide estimates of reticulation for relatively large *** Codes 05C30 © 2023, CC BY.

关键词： Phylogenetic

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhushan Fulkar Pawan Patil Gaurav Srivastav Promod Mahale Department of Artificial Intelligence and Data science Faculty of Engineering and Technology Wardha Maharashtra India Artificial Intelligence and Data science Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs.

关键词： Deep learning Training Visualization Uncertainty Rain Crops Real-time systems

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Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate science

arXiv

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arXiv 2023年

作者： Bommer, Philine Kretschmer, Marlene Hedström, Anna Bareeva, Dilyara Höhne, Marina M.-C. Department of Machine Learning Technische Universität Berlin Berlin10587 Germany Understandable Machine Intelligence Lab Department of Data Science ATB Potsdam14469 Germany Institute for Meteorology University of Leipzig Leipzig Germany Department of Meteorology University of Reading Reading United Kingdom BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Machine Learning Group UiT the Arctic University of Norway Tromso9037 Norway Department of Computer Science University of Potsdam Potsdam14476 Germany

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multi-layer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods Integrated Gradients, layer-wise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods - gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for randomization skill. We find architecturedependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method. © 2023, CC BY.

关键词： Multilayer neural networks

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference 41

Classification under Nuisance Parameters and Generalized Lab...

引用

41st International Conference on machine learning, ICML 2024

作者： Masserano, Luca Shen, Alex Doro, Michele Dorigo, Tommaso Izbicki, Rafael Lee, Ann B. Department of Statistics and Data Science Carnegie Mellon University Pittsburgh United States Machine Learning Department Carnegie Mellon University Pittsburgh United States Department of Physics and Astronomy Università di Padova Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Lulea Techniska Universitet Lulea Sweden Universal Scientific Education and Research Network Italy Department of Statistics Universidade Federal de São Carlos São Paulo Brazil

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data X as covariates leads to biased predictions and invalid uncertainty estimates of labels Y. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pretrained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models. Copyright 2024 by the author(s)

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Adla Aryan Abdul Aleem Mohammed Manish Chabra Syed Saarib Rasheed Mohammed Adnan Mohammed Abdul Raoof Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Department of Artificial Intelligence & Data Science Methodist College of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process.

关键词： Measurement Computer science Accuracy Feature extraction Classification algorithms Convolutional neural networks Reliability Optimization Consumer electronics Convergence

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ML Powered Analytics for Sensing Demand in Consumer Industry

ML Powered Analytics for Sensing Demand in Consumer Industry

引用

IEEE Punecon

作者： Rahul Shanbhogue Yashwanth D N Anwesh Reddy Paduri Panish Ramakrishna Srikanth Prabhu Muralidhara Sarma Dhulipati Narayana Darapaneni Data Science & Machine Learning PES University Bangalore India Department of Commerce and Management Koneru Lakshmaiah Eduaction foundation Hyderabad India Director – AIML Great Learning/Northwestern University Illinois USA

The consumer goods industry is facing significant challenges in meeting the consumer's everevolving demands and preferences. The e-commerce and online delivery brought in the ease of access to purchase products, while the popularity of social media brought the social sentiment angle which are impacting customer purchases patterns. These factors are leading to a rise in influencer marketing campaigns and have enabled businesses or brands to reach consumers faster. The goal of the study is to address these challenges and to explore the use of machine learning and big data analytic techniques to provide insights by understanding consumer purchase patterns, sales data, market and social media trends, news feed and stock levels to predict future demand for products in advance which will better enable inventory planning.

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A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Order Mode in a Sub-mode Microwave SIW Strucutre for Recent 5G Applications

A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Orde...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Amjaad T. Altakhaineh Saqer S. Alja'afreh Chaoyun Song Data Science and Machine Learning Dept Pioneers International Academy Amman Jordan Dept of Electrical Engineering Mutah University Alkarak Jordan Department of Engineering King's College London London United Kingdom

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a single structure, multi-port antenna for 5G millimeter wave (mmWave) applications. The proposed antenna is constructed by exploiting the higher-order modes of a fraction-mode microwave substrate-integrated waveguide (SIW) cavity at 28 GHz (quadrature mode substrate-integrated waveguide (QMSIW)). Such properties exhibit spatial multiplexing capabilities, giving the single cavity a MIMO capability. Firstly, two perpendicular T-shaped microstrip ports (Port 1 and Port 2) are installed to excite TE205 higher-order mode of the QMSIW cavity at 28 GHz. To extend the MIMO capability, a 4×4 MIMO QMSIW antenna is created by adding two extra coaxial feeding ports (Port 3 and Port 4), the higher-order modes of the cavity are reused by leveraging two orthogonal inverted L-shaped slots, in which each slot is excited using coaxial cables at 28 GHz. The proposed antenna exhibited a wide -10 dB impedance and > 11 dB isolation based on the simulation environment without using decoupling structures. Finally, we believe this work offers a promising contribution to present and future multi-port single antenna, MIMO, and polarization applications.

关键词： Phased arrays Microwave antennas Slot antennas 5G mobile communication Simulation Microwave communication Millimeter wave propagation Space division multiplexing Millimeter wave communication Substrates

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How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?

arXiv

引用

arXiv 2023年

作者： Kostin, Julia Krahmer, Felix Stöger, Dominik Technical University of Munich Department of Mathematics Germany Munich Center for Machine Learning Germany Technical University of Munich Munich Data Science Institute Germany Germany

In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution. Reformulation of blind deconvolution as a low-rank recovery problem has led to multiple theoretical recovery guarantees in the past decade due to the success of the nuclear norm minimization heuristic. In particular, in the absence of noise, exact recovery has been established for sufficiently incoherent signals contained in lower-dimensional subspaces. However, if the convolution is corrupted by additive bounded noise, the stability of the recovery problem remains much less understood. In particular, existing reconstruction bounds involve large dimension factors and therefore fail to explain the empirical evidence for dimension-independent robustness of nuclear norm minimization. Recently, theoretical evidence has emerged for ill-posed behavior of low-rank matrix recovery for sufficiently small noise levels. In this work, we develop improved recovery guarantees for blind deconvolution with adversarial noise which exhibit square-root scaling in the noise level. Hence, our results are consistent with existing counterexamples which speak against linear scaling in the noise level as demonstrated for related low-rank matrix recovery problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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