Machine Learning and Applications: An International Journal (MLAIJ 2026)
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Prazo de submissão de trabalhos: 06/06/2026
Machine Learning and Applications: An International Journal (MLAIJ)Citations, h-index, i10-index of MLAIJScope & TopicsMachine Learning and Applications: An International Journal (MLAIJ)is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.Topics of interest include but are not limited to, the followingFoundations of Machine LearningStatistical Learning Theory and GeneralizationOptimization for ML (Convex, Non Convex, Large Scale)Probabilistic Modeling, Bayesian Learning and Graphical ModelsCausal Inference, Causal ML and Counterfactual ReasoningOnline Learning, Meta Learning and Continual LearningMulti Task Learning, Transfer Learning and Domain AdaptationTheory of Deep Learning and Emergent BehaviorsDeep Learning and Representation LearningNeural Network Architectures and Training TechniquesSelf Supervised Learning and Contrastive LearningGenerative Models (GANs, Diffusion Models, VAEs)Diffusion Models for Images, Text, Time Series, Molecules and GraphsFoundation Models, LLMs, Vision Language Models and Multimodal ModelsEfficient Deep Learning (Pruning, Quantization, Distillation)Representation Learning for Structured, Temporal and Graph DataReinforcement Learning, Decision Making and Embodied AIDeep Reinforcement Learning and Policy OptimizationMulti Agent RL, Game Theory and CoordinationOffline RL, Safe RL and Risk Sensitive RLWorld Models, Embodied AI and Interactive LearningRL for Robotics, Control Systems and Real World DeploymentHierarchical RL and Skill DiscoveryPlanning Augmented Models and Decision TransformersNatural Language Processing, Speech and Multimodal AILarge Language Models and Instruction Tuned ModelsRetrieval Augmented Generation (RAG) and Knowledge Grounded ModelsLong Context Models, Memory Augmented Models and Tool Using LLMsText Generation, Summarization and Dialogue SystemsSpeech Recognition, Speech Synthesis and Audio Language ModelsVision Language Models, Video Language Models and Multimodal FusionNLP for Low Resource Languages and Cross Lingual LearningComputer Vision, Perception and GraphicsImage Classification, Detection and Segmentation3D Vision, Scene Understanding and SLAMVision Transformers and Diffusion Based Vision ModelsVideo Understanding, Action Recognition and Motion PredictionGenerative Vision Models, Neural Rendering and 3D GenerationEmbodied Perception and Interactive VisionVision Language Action Models for RoboticsData Mining, Knowledge Discovery and Graph LearningGraph Neural Networks (GNNs) and Graph Representation LearningKnowledge Graphs, Reasoning and Neuro Symbolic AILarge Scale Data Mining and Pattern DiscoveryTime Series Forecasting, Anomaly Detection and Predictive ModelingSimulation Based ML and Synthetic Data GenerationML for Structured, Relational and Heterogeneous DataTrustworthy, Explainable and Responsible AIExplainable AI (XAI) and Mechanistic InterpretabilityFairness, Accountability, Transparency and Ethics in MLRobust ML, Adversarial Attacks and DefensesJailbreak Resistant LLMs and Safety EvaluationPrivacy Preserving ML (Differential Privacy, Federated Learning, Secure ML)Safety Critical ML and ReliabilityAI Governance, Risk Assessment and Policy Aligned MLML Systems, Hardware Acceleration and Efficient ComputingDistributed and Parallel ML SystemsTraining and Inference Optimization for Foundation ModelsML Compilers, Optimization and Deployment FrameworksEdge ML, TinyML and On Device LearningEdge Native Foundation Models and Distributed InferenceNeuromorphic Computing and Brain Inspired MLEnergy Efficient ML, Green AI and Carbon Aware ML PipelinesApplied Machine Learning and Domain Specific MLHealthcare and Life SciencesMedical Imaging, Diagnostics and Clinical Decision SupportComputational Biology, Genomics and Drug DiscoveryDigital Health, Wearables and Personalized MedicineML for Neuroscience and Cognitive ModelingML for Digital Therapeutics and Clinical Decision AutomationScience and EngineeringML for Physics, Chemistry, Materials Science and Climate ModelingPhysics Informed ML and Scientific Machine Learning (SciML)Differentiable Physics, Neural Simulators and ML Accelerated SimulationML for Robotics, Autonomous Systems and ControlML for Smart Cities, IoT and Cyber Physical SystemsBusiness, Finance and Social SystemsML for Finance, Risk Modeling and Fraud DetectionRecommender Systems, Personalization and User ModelingSocial Network Analysis and Computational Social ScienceML for Policy Simulation and Societal Impact ModelingEmerging TrendsAgentic AI, Autonomous AI Systems and Multi Agent LLM EcosystemsTool Using AI, Planning Augmented LLMs and Autonomous AgentsProgram Synthesis, AI for Code and ML Guided Theorem ProvingQuantum Machine Learning and Quantum Inspired AlgorithmsAutoML, Neural Architecture Search (NAS) and Hyperparameter OptimizationML for Foundation Model Alignment, Safety and GovernanceML for Autonomous Scientific Discovery and Robot ScientistsML for Synthetic Biology, Bio Inspired Algorithms and Living SystemsPaper SubmissionAuthors are invited to submit papers for this journal through E-mail:mlaijjournal@airccse.orgor throughSubmission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.Important DatesSubmission Deadline:June 06 , 2026Authors Notification:June 25, 2026Final Manuscript Due:June 29, 2026Publication Date:Determined by the Editor-in-ChiefCurrent IssueMarch 2026, Volume 13, Number 1Decision Making in Scientific Machine LearningFull TextMark Temple-Raston, USAEnhanced Machine Learning Classification with Weighted Loss Penalty on Credit Card Fraud DetectionFull TextJiawei Zhang1, Xin Zhang1and Xinyin Miao2,1PRA Group (Nasdaq: PRAA), USA,2American Airlines Group Inc (Nasdaq: AAL), USADecember 2025, Volume 12, Number 4About an Automating Annotation Method for Robot MarkersFull TextWataru Uemura and Takeru Nagashima, Ryukoku University, JapanAI Based Transformation Projects-Language Processing Environments for Audit and Governance (LPE_AG) - the Proof of ConceptFull TextAntoine Trad, Paris La-Defense France, FranceAI Based Transformation Projects-ML and NLP Managed Audit, Control, and Governance (ML_ACG) - the BasicsFull TextAntoine Trad , Paris La-Defense France, FranceSeptember 2025, Volume 12, Number 3Preserving Employees' Personal Knowledge During the Transition to Automated ProductionFull TextEkaterina Mashina, GermanyJune 2025, Volume 12, Number 2Comparing Classifiers in the Presence of Errors in True Label Assignment in Medical DatasetsFull TextVishwa Vallabh Angampally and Eugene Pinsky, Metropolitan College Boston University, USAMarch 2025, Volume 12, Number 1Revolutionizing Big Data with AI-Driven Hybrid Soft Computing TechniquesFull TextPraveen Kumar Myakala, Anil Kumar Jonnalagadda and Prudhvi Naayini, Independent Researchers, USAUnderstanding the Heterogeneous Impact of Remittances on Saving Behaviour in Uzbekistan: A Machine Learning ApproachFull TextMasuda Isaeva, Asakabank, UzbekistanCluster-Specific Propensity Score Weighting To Stabilize Treatment Effect EstimationFull TextHardev Ranglani, EXL Service Inc, USAArtificial Intelligence and Cloud Computing in Healthcare: Innovations and ImpactsFull TextMohammad Amir Salari, Saint Louis University, USAArtificial Intelligence Based Business Transformation Projects-the Role of Data Sciences in Mixed-Methods Patterns (RDSMMP)Full TextAntoine Trad, IBISTM, FranceEstimating the Accuracy of a Bagged EnsembleFull TextEugene Pinsky and Siddhant Shah, Boston University, USAAnalyzing Fashion Trends Using Hierarchical Clustering and Temporal AnalysisFull TextShivani Parab and Eugene Pinsky, Boston University, USAQuantile Regression with Q1/Q3 Anchoring: A Robust Alternative for Outlier-Resistant ModelingFull TextXiaoying Zeng and Eugene Pinsky, Boston University, USAAn Adaptive Hierarchical Tree-Based Clustering Approach to Outlier Detection in ETF-Focused Financial Time-SeriesFull TextShlok Mandloi, Aryaman Jalali, and Eugene Pinsky, Boston University, USADetection of Alzheimer’s Disease using Bidirectional LSTM and Attention MechanismsFull TextMehdi Ghayoumi and Kambiz Ghazinour, SUNY, USAAnalysis of Unsupervised Clustering Algorithms and Impact of Dimensionality Reduction: A Data Driven ApproachFull TextPalak Narula, Adobe Inc., IndiaRelated JournalInternational Journal of Artificial Intelligence & Applications (IJAIA)Editor In ChiefNatarajan Meghanathan, Jackson State University, USAAssociate EditorTatiana Tambouratzis, University of Piraeus, GreeceEditorial Board MembersVerma A.K, Thapar University, IndiaAbdulkadir Ozcan,Karatay University, TurkeyArman Sargolzaei,Florida International University, USAArvind Kumar,Amity university Noida, IndiaAshutosh Kumar Dubey,Trinity Institute of Technology & Research, India...for more