Abstract Adversarial machine learning has exposed critical vulnerabilities in Artificial Intelligence-based Windows Portable Executable (PE) malware detection. A well-crafted small perturbation to a PE malware binary can cause it to be misclassified as goodware. Adversarial Training (AT) is one of the most effective defenses; however, it is not always sufficient alone and often suffers from the robustness–accuracy trade-off. This study proposes Adversarial Training with Synthetic Augmentation (ATS), a novel defense methodology that augments unrealistic synthetic adversarial examples into the standard adversarial training, produced using the Conditional Tabular GAN (CTGAN). The robustness and resilience of Random Forest, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) classifiers were evaluated against five realistic Windows PE adversarial attacks: Full DOS, EXTEND, SHIFT, FGSM-padding, and GAMMA. Results show that the ATS methodology consistently outperformed AT in enhancing robustness across all attacks and classifiers while maintaining or improving clean accuracy and F1-score. SHAP-based interpretability analysis further reveals that ATS reduces dependence on attack-sensitive low-level features and increases attention to stable PE features. Overall, ATS provides a model-agnostic enhancement to standard AT, effectively reducing false negatives without compromising clean accuracy.

Malware refers to malicious software or a component of software intended for malicious purposes. The manual analysis and detection of malicious software is challenging due to its complexity. Thus, several automated solutions have become popular for real-time malware detection. A spread-out approach consists of generating images from the samples bytecode and giving them to convolutional neural networks (CNNs), which are used either as classifiers or feature extractors for further classification algorithms. These systems perform extremely well when trained and tested on partitions of the same dataset. However, cross-dataset tests and malware detection verification on emerging real-world samples are required in the real-world context. This is a crucial challenge when probing the robustness of the systems and models. This paper proposes CNN-AutoMIC,a robust automated approach to extract features from malware images. CNN-AutoMIC employs a specific CNN architecture to extract features, followed by an autoencoder-based compressor that reduces features to two fundamental components. The two-dimensional projection of these components is the basis of the predictions performed by the K-nearest neighbors (K-NN) algorithm. Moreover, the observable placement of new samples on the obtained scatter plot makes it possible to explain why the AI-based system produced a certain prediction. It was benchmarked against several CNN-based models and a Vision Transformer. They were trained on the Malevis dataset and cross-dataset evaluated on four different real-world datasets. CNN-AutoMIC outperformed the competitors for each classification performance metric, while requiring a reasonable training and prediction time. In addition, it achieves a promising Akaike information criterion (AIC) score, indicating its efficiency in terms of model complexity.

The widespread use of artificial intelligence deep neural networks (DNNs) in fields such as medicine and engineering necessitates understanding their decision-making processes. Current explainability methods often produce inconsistent results and struggle to highlight essential signals influencing model inferences. This paper introduces the Evolutionary Independent Deterministic Explanation (EVIDENCE) theory, a novel approach offering a deterministic, model-independent method for extracting significant signals from black-box models. EVIDENCE theory, grounded in robust mathematical formalization, is validated through empirical tests on diverse datasets, including COVID-19 audio diagnostics, Parkinson's disease voice recordings, and the George Tzanetakis music classification dataset (GTZAN). Practical applications of EVIDENCE include improving diagnostic accuracy in healthcare and enhancing audio signal analysis. For instance, in the COVID-19 use case, EVIDENCE-filtered spectrograms fed into a frozen Residual Network with 50 layers (ResNet50) improved precision by 32 % for positive cases and increased the Area Under the Curve (AUC) by 16 % compared to baseline models. For Parkinson's disease classification, EVIDENCE achieved near-perfect precision and sensitivity, with a macro average F1-Score of 0.997. In the GTZAN, EVIDENCE maintained a high AUC of 0.996, demonstrating its efficacy in filtering relevant features for accurate genre classification. EVIDENCE outperformed other Explainable Artificial Intelligence (XAI) methods such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class-Activation Mapping (GradCAM) in almost all metrics. These findings indicate that EVIDENCE not only improves classification accuracy but also provides a transparent and reproducible explanation mechanism, crucial for advancing the trustworthiness and applicability of AI systems in real-world settings.

Deep learning (DL) has been demonstrated to be a valuable tool for analyzing signals such as sounds and images, thanks to its capabilities of automatically extracting relevant patterns as well as its end-to-end training properties. When applied to tabular structured data, DL has exhibited some performance limitations compared to shallow learning techniques. This work presents a novel technique for tabular data called adaptive multiscale attention deep neural network architecture (also named excited attention). By exploiting parallel multilevel feature weighting, the adaptive multiscale attention can successfully learn the feature attention and thus achieve high levels of F1-score on seven different classification tasks (on small, medium, large, and very large datasets) and low mean absolute errors on four regression tasks of different size. In addition, adaptive multiscale attention provides four levels of explainability (i.e., comprehension of its learning process and therefore of its outcomes): 1) calculates attention weights to determine which layers are most important for given classes; 2) shows each feature's attention across all instances; 3) understands learned feature attention for each class to explore feature attention and behavior for specific classes; and 4) finds nonlinear correlations between co-behaving features to reduce dataset dimensionality and improve interpretability. These interpretability levels, in turn, allow for employing adaptive multiscale attention as a useful tool for feature ranking and feature selection.

As the occurrence of Denial of Service and Distributed Denial of Service (DoS/DDoS) attacks increases, the demand for effective defense mechanisms increases. Recognition of such anomalies in the computer network is commonly performed through network-based intrusion detection and prevention systems (NIDPSs). Although NIDPSs allow the interception of all known attacks, they are not robust to the continuing variation over time of DoS/DDoS anomalies. The machine learning (ML) paradigm provides algorithms that can effectively reduce concept drift due to the evolution of cyber threat data patterns. These methodologies can be exploited for creating effective rules suitable for popular NIDPS engines such as Suricata. This paper proposes a new algorithm called Anomaly2Sign, which automatically produces rules for Suricata through an automatic Decision Tree (DT)-based generation process. The DT is trained on both anomalous and legitimate traffic, allowing the generation process to select anomaly features that can be mapped within the generated rule structure. Additionally, the DT hyperparameters are tuned at execution time to generate a minimal ruleset capable of detecting the largest number of anomalous packets. The proposed algorithm achieves classification metrics in the range of 99.7%–99.9% using the BOUN-DoS and BUET-DDoS datasets, outperforming the compared ML classifiers, i.e., Logistic Regression, Support Vector Machine, and Multi-Layer Perceptron. Furthermore, the leveraged DT model requires a shorter training and prediction time than the previously cited benchmark classifiers. To enforce the selection of the DT model, an analysis of model complexity is undertaken, including the evaluation of the Akaike Information Criterion (AIC) score. As a result of such an evaluation, the DT model achieved the lowest AIC score among the compared approaches denoting its low complexity. Finally, Anomaly2Sign has been compared with Syrius, i.e., an alternative state-of-the-art automatic NIDPS rules generator, obtaining better performance for detection rate and execution time.

Packet classification activity performed by a FireWall (FW) introduces high latency in network communications due to the computation time required to check whether any packet matches one of the FW rules. Such a classification process is done by sequentially checking the list of rules until a match is found or the end of the list is reached. Given the complexity of FW rules in some environments, this latency could become relevant. This problem is addressed by ordering the list of FW rules to minimize the classification latency, where the rules with higher activation frequencies are placed accordingly starting from the top of the list. This is not always feasible because dependency constraints between rules could exist: swapping the positions of dependent rules results in a loss of the integrity of the implemented security policy. For this reason, the FW rule ordering problem belongs to the realm of constrained combinatorial optimization. This paper proposes a two-stage algorithm to address this problem. The first stage performs an innovative topological sorting algorithm aimed at finding an optimal ordering for the constrained rules, taking into account the fact that rule activation frequencies are influenced by inter-packet arrival time, which typically obeys Zipf's law. The second stage employs a genetic algorithm to find the optimal ordering of all rules within the list. The proposed approach is evaluated using different filtering lists of different complexity provided by ClassBench. A comparison with other state-of-the-art algorithms addressing the same problem is performed. Furthermore, the performance analysis is extended employing an exact optimization method. The results obtained show the effectiveness of the proposed algorithm in minimizing packet classification latency, while a short reordering time is required.

Recognition of known malicious patterns through signature-based systems is unsuccessful against malware for which no known signature exists to identify them. These include not only zero-day but also known malicious software able to self-replicate rewriting its own code leaving unaffected its execution, namely metamorphic malware. YARA is a popular malware analysis tool that uses the so-called YARA-rules, which are built to match malicious contents within files or network packets analyzed by an Anti-Virus engine. Sometimes such content is expressed in the form of a byte-signature, i.e., a sequence of operational machine-level code. However, these can be bypassed since malware obfuscation techniques can change these sequences, rewriting them in several equivalent forms. This paper presents YAMME, a YARA-byte-signatures Metamorphic Mutation Engine to strengthen rules against some malware obfuscation techniques deployed in metamorphic mutation engines. First, it rewrites YARA-bye-signatures in several equivalent ways, as a metamorphic mutation engine would do. Second, an optimization phase exploits the YARA-rules syntax constructs to provide several rules formats, making them suitable for different real-world application requirements. YAMME rules have been evaluated on MWOR, G2, NGVCK, and MetaNG datasets, resulting in a better detection rate than that achieved by YARA-rules generated through AutoYara. Furthermore, an analysis of computational overhead required by different YAMME rules formats validates the low impact introduced by the mutation engine at the YARA-rules level.

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegenerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.

In this extended version of this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The original system achieved an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. The implementation of features from the kinematic theory of rapid human movement and its sigma-lognormal model together with classic features increased the overall accuracy of the system to 0.947 F1 score. In addition, multi-class classification was performed with the aim of classifying neurodegenerative disease severities. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, its novelties are on phases segmentation, experimental setup and the application of kinematic theory of rapid human movements to sit-to-stand videos for neurodegenerative disease assessment.

This work considers the Internet of Things (IoT) and machine learning (ML) applied to the agricultural sector within a real-working scenario. More specifically, the aim is to punctually forecast two of the most important meteorological parameters (solar radiation and the rainfall) to determine the amount of water needed by a specific plantation under different contour conditions. Three different state-of-the-art ML approaches, coupled with boosting techniques, have been adopted and compared to obtain hourly forecasting. Real-working conditions are referred to the situation in which training data are missing for a specific weather station near the specific field to be irrigated. A simple but effective approach, based on correlation between available weather stations, is considered to cope with this problem. Results, evaluated considering different metrics as well as the execution time, demonstrate the viability of the proposed solution in real IoT working scenario in which these forecasting are input data to successively evaluate irrigation needing.

On-line signatures can be acquired adopting specifically devoted stylus-pad system as well as with general purpose tablet/smartphone. The resulting acquired signal is strongly influenced by the device itself as well as by the user interaction modality. It is evident that it is desirable to identify the most stable features for verification aims. In this work, a set of 39 function feature domains is investigated in terms of stability adopting the weighted Direct Matching Points approach. Experiments have been performed on the e-biosign dataset providing many evidences related to the different scenarios.

In a society with increasing age, the understanding of human falls it is of paramount importance. This paper presents a Decision Support System whose pipeline is designed to extract and compute physical domain's features achieving the state of the art accuracy on the Le2i and UR fall detection datasets. The paper uses the Kinematic Theory of Rapid Human Movement and its sigma-lognormal model together with classic physical features to achieve 98% and 99% of accuracy in automatic fall detection on respectively Le2i and URFD datasets. The effort made in the design of this work is toward recognition of falls by using physical models whose laws are clear and understandable.

On-line signature verification involves the use of many different features or domains. The most stable domains for a signer are analysed in this paper. For this purpose, stable domains are calculated with the weighted Direct Matching Points (ωDMP), which is a relaxed version of the classical DMP technique. In addition to the direct coupling, ωDMP also considers the information contained in the 1:N couplings from the Dynamic Time Warping algorithm. Using the ωDMP technique, state-of-the-art verification results are obtained, showing the capacity to outperform previous DMP techniques to calculate the local stability model of signers.

Education may play a key role in developing “cognitive reserve” against neurodegenerative dementia. In this work, we investigate for the first time if handwriting dynamics can serve as a quantitative indicator of this reserve. We carried out an exploratory study involving a sample of mild cognitive impairment (MCI) subjects, with high and low education respectively, and a sample of healthy elder controls. We asked them to perform three complex handwriting tasks on a digitizing tablet: drawing a clock; copying a check; writing a spontaneous sentence. Dynamic measures of the handwriting were then analyzed both with an unsupervised and a supervised machine learning approach. The results we obtained suggest that: (i) handwriting of MCI subjects with high reserve is quite similar to that of controls; (ii) handwriting of MCI subjects with lower reserve is easier to be distinguished from the other two. Dynamic handwriting analysis could provide a novel methodology to elucidate the still unknown mechanisms underlying brain resilience.

In the present paper, we propose an evolutionary approach to address interoperability issues in multi-device signature verification, based on transformation mappings automatically tuned by a genetic algorithm. These mappings are meant to decrease dissimilarities between signatures acquired through different devices and with different modalities (stylus/finger). The effectiveness of the proposed method was evaluated on the e-BioSign data set. Our proposal achieved an average relative improvement of 26% of EER, for the case of skilled forgeries, compared to baseline results.

Water scarcity is one of the main issues that agriculture must face since an increase is expected not only in developing countries but also in southern Europe with Italy featuring a long-term annual average estimated in 1.909 m3per inhabitant. To deal with this problem, the EcoLoop project presented in this work proposes an ICT system able to collect, aggregate and analyze IoT data, with the aim to foster reuse of wastewater and optimize water usage in agriculture. A Decision Support System (DSS) acts on wastewater plants managing the irrigation and fertilization strategies, the reservation queues and the network distribution exploiting smart sensors, semantic ontologies and machine learning technologies.

The papers in this special section focus on handwriting and drawing processes for user-centered systems. The papers provide a wide and updated overview of the frontier of research in the field of humancentered systems based on drawing and handwriting processing. Through the papers, some of the most relevant directions of further research are highlighted with specific attention to components related to human–machine interaction. The Guest Editors hope that this issue brings forth the importance of automated systems related to automatic processing of drawing and handwriting.

This paper presents an experimental analysis on the use of semi-supervised learning in the handwritten digit recognition field. More specifically, two new feedback-based techniques for retraining individual classifiers in a multi-expert scenario are discussed. These new methods analyze the final decision provided by the multi-expert system so that sample classified with a confidence greater than a specific threshold is used to update the system itself. Experimental results carried out on the CEDAR (handwritten digits) database are presented. In particular, error rate, similarity index and a new correlation score among them are considered in order to evaluate the best retraining rule. For the experimental evaluation, an SVM classifier and five different combination techniques at abstract and measurement level have been used. Finally, the results show that iterating the feedback process, on different multi-expert systems built with the five combination techniques, one retraining rule is winning over the other respect to the best correlation score.

This paper presents a new approach for zoning design based on a class-adaptive technique in which the optimal zoning method is defined for each class. For this purpose, in the zoning design stage, a multi-objective genetic algorithm was used to determine, for each class, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests were carried out in the field of handwritten digit and character recognition. The results show that the new class-adaptive zoning methods proposed in this paper are superior to the set-adaptive methods presented in the literature.

In the networked society, in which a multitude of different devices can be used for signature acquisition, specific research is still needed to determine the extent to which features of an input signature depend on the characteristics of the signature acquisition process. In this paper an experimental investigation is carried out on constrained signatures, which were acquired using writing boxes with different areas and shapes. The paper discusses different behaviour of dynamic and static features with respect to the writing boxes.

This paper presents a new approach for online signature verification that exploits the potential of local stability information in handwritten signatures. Different from previous models, this approach classifies a signature using a multidomain strategy. A signature is first split into different segments based on the stability model of a signer. Then, according to the stability model, for each segment, the most profitable domain of representation for verification purposes is detected. In the verification stage, the authenticity of each segment of the unknown signature is evaluated in the most profitable domain of representation. The authenticity of the unknown signature is then determined by combining local verification decisions. The study was carried out on the signatures in the SUSIG database, and the experimental results, thus, obtained confirm the effectiveness of the proposed approach, when compared with others in the literature.

The creation of training set, for pattern recognition, is a difficult, expensive and time consuming task because it requires the efforts of experienced human annotators. On the other hand, unlabeled data can be obtained cheaply, but there are few ways to use them. Semi-Supervised learning uses both labeled and unlabeled data for classification task. In this paper we propose three methods to apply semi-supervised learning to re-train individual classifiers in a multi-expert scenario. More specifically, these methods are focused on a feedback-based process and on the acceptance threshold that defines what data must be selected for feedback. For the experimental results, carried out on the CEDAR (handwritten digits) database, a SVM classifier and two different combination techniques at measurement level have been used. The results show the effectiveness of the proposed approach with respect to Self-Training and Co-Training algorithms.

In recent years, biometric-based authentication systems have been widely used in many applications which require reliable identification scheme. Among others, handwritten signature is one of the most interesting biometric means, that is being considered with renewed interest. This paper presents some of the most relevant advances in the field of offline signature identification and highlights some directions for further research.

In this paper a new on-line signature verification technique is proposed. Differently from previous works, this approach classifies a signature using a multi-domain strategy. In particular, based on the stability model of each signer, the signature is split into different segments and for each segment the most profitable domain of representation for verification purpose is detected. In the verification stage, Dynamic Time Warping (DTW) is used to evaluate the genuinity of each segment of the unknown signature, using the specific domain of representation. The experimental results, carried out on signatures of the SUSIG database, demonstrate the effectiveness of the proposed approach when compared to other approaches in literature.

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

The aim of this paper is to explore the properties of a new zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Extensive experiments have been conducted on the MNIST dataset to investigate strengths and weakness of the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results with any type of features. Furthermore, the proposed zoning method, jointly with a suitable choice of features, allows a low complexity classifier to reach excellent performances both in terms of accuracy and speed.

A new approach for static signature verification is presented in this paper. The approach uses optical flow to estimate local stability among signatures. In the enrollment stage, optical flow is used to define a stability model of the genuine signatures for each signer. In the verification stage, the stability between the unknown signature and each one of the reference signatures is estimated and consistency with the stability model of the signer is evaluated. The experimental results, carried out on the signatures in the GPDS database, demonstrate the effectiveness of the new approach.

This paper presents a new approach to optimal zoning design. The approach uses a multi-objective genetic algorithm to define, in a unique process, the optimal number of zones of the zoning method along with the optimal zones, defined through Voronoi diagrams. The experimental tests, carried out in the field of handwritten digit recognition, show the superiority of new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

Three different strategies in order to re-train classifiers, when new labeled data become available, are presented in a multi-expert scenario. The first method is the use of the entire new dataset. The second one is related to the consideration that each single classifier is able to select new samples starting from those on which it performs a missclassification. Finally, by inspecting the multi expert system behavior, a sample misclassified by an expert, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. This paper provides a comparison of three approaches under different conditions on two state of the art classifiers (SVM and Naive Bayes) by taking into account four different combination techniques. Experiments have been performed by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training samples, as well as by the specific combination decision schema and by classifiers in the ensemble.

Classifier combination is a powerful paradigm to deal with difficult pattern classification problems. As matter of this fact, multi-classifier systems have been widely adopted in many applications for which very high classification performance is necessary. Notwithstanding, multi-classifier system design is still an open problem. In fact, complexity of multi-classifiers systems make the theoretical evaluation of system performance very difficult and, consequently, also the design of a multi-classifier system. This paper presents a new approach for the design of a multi-classifier system. In particular, the problem of feature selection for a multi-classifier system is addressed and a genetic algorithm is proposed for automatic selecting the optimal set of features for each individual classifier of the multi-classifier system. The experimental results, carried out in the field of handwritten digit recognition, demonstrate the effectiveness of the proposed approach.

In handwritten character recognition, zoning is one of the most effective approaches for features extraction. When a zoning method is considered, the pattern image is subdivided into zones each one providing regional information related to a specific part of the pattern. The design of a zoning method concerns the definition of zoning topology and membership function. Both aspects have been recently investigated and new solutions have been proposed, able to increase adaptability of the zoning method to different application requirements. In this paper some of the most recent results in the field of zoning method design are presented and some valuable directions of research are highlighted.

This paper addresses the use of multi-objective optimization techniques for optimal zoning design in the context of handwritten digit recognition. More precisely, the Non-dominant Sorting Genetic Algorithm II (NSGA II) has been considered for the optimization of Voronoi-based zoning methods. In this case both the number of zones and the zone position and shape are optimized in a unique genetic procedure. The experimental results point out the usefulness of multi-objective genetic algorithms for achieving effective zoning topologies for handwritten digit recognition.

Recently, research in handwritten signature verification has been considered with renewed interest. In fact, in the age of e-society, handwritten signature still represents an extraordinary means for personal verification and the possibility of using automatic signature verification in a range of applications is becoming a reality. This paper focuses on some of the most remarkable aspects the field and highlights some recent research directions. A list of selected publications is also provided for interested researchers.

This paper presents a new technique for the analysis of stability in static signature images. The technique uses an equimass segmentation approach to non-uniformly split signatures into a standard number of regions. Successively, a multiple matching technique is adopted to estimate stability of each region, based on cosine similarity. The GPDS database has been considered for the experimental test. The results demonstrate the validity of the novel approach and highlight some directions for further research.

Abstract This paper describes a Speaker Verification System based on the use of multi resolution classifiers in order to cope with performance degradation due to natural variations of the excitation source and of the vocal tract. The different resolution representations of the speaker are obtained by considering multiple frame lengths in the feature extraction process and from these representations a single Pseudo‐Multi Parallel Branch (P‐MPB) Hidden Markov Model is obtained. In the verification process, different resolution representations of the speech signal are classified by multiple P‐MPB systems: the final decision is obtained by means of different combination techniques. The system based on the Weighted Majority Vote technique considerably outperforms baseline systems: improvements are between 15% and 38%. The execution time of the verification process is also evaluated and it proves to be very acceptable, thus allowing the use of the approach for applications in real time systems.

This letter introduces a new score normalization technique - based on Dynamic Time Warping (DTW) - for output integration in multiclassifier systems. More precisely, DTW is used to match the score cumulative distribution of each individual classifier against a standard cumulative distribution. The warping function allows optimal alignment of the scores provided by the individual classifiers with the scores on the standard cumulative distribution. Furthermore, in order to adapt the normalization process to the behavior of the individual classifiers and to the decision fusion rule, a new class of fuzzy cumulative distributions is introduced and a genetic approach is used to select the optimal distribution to be used as standard cumulative distribution for score normalization. The experimental tests report better results for the fuzzy normalization technique than for those obtained with other approaches present in the literature.

In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine-in a unique process-the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.

In pattern recognition tasks it is frequent that new (labeled) data became available as the specific application scenario evolves. When a multi expert system (ME) is adopted, the collective behavior of classifiers can be used to select the most profitable samples in order to update the knowledge base. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if that sample produces a misclassification by the ensemble of classifiers. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific samples are selected by the individual classifier. Successful results have been obtained by considering the CEDAR (handwritten digit) database, moreover it is also shown how they depend by the specific combination decision schema, as well as by data distribution.

In Handwritten Character Recognition, zoning is rightly considered as one of the most effective feature extraction techniques. In the past, many zoning methods have been proposed, based on static and dynamic zoning design strategies. Notwithstanding, little attention has been paid so far to the role of function-zone membership functions, that define the way in which a feature influences different zones of the pattern. In this paper the effectiveness of membership functions for zoning-based classification is investigated. For the purpose, a useful representation of zoning methods based on Voronoi Diagram is adopted and several membership functions are considered, according to abstract -- , ranked- and measurement-levels strategies. Furthermore, a new class of membership functions with adaptive capabilities is introduced and a real-coded genetic algorithm is proposed to determine both the optimal zoning and the adaptive membership functions most profitable for a given classification problem. The experimental tests, carried out in the field of handwritten digit recognition, show the superiority of adaptive membership functions compared to traditional functions, whatever zoning method is used.

In zoning-based classification, a membership function defines the way a feature influences the different zones of the zoning method. This paper presents a new class of membership functions, which are called fuzzy-membership functions (FMFs), for zoning-based classification. These FMFs can be easily adapted to the specific characteristics of a classification problem in order to maximize classification performance. In this research, a real-coded genetic algorithm is presented to find, in a single optimization procedure, the optimal FMF, together with the optimal zoning described by Voronoi tessellation. The experimental results, which are carried out in the field of handwritten digit and character recognition, indicate that optimal FMF performs better than other membership functions based on abstract-level, ranked-level, and measurement-level weighting models, which can be found in the literature.

This paper addresses the problem of membership function selection for zoning-based classification. Different types of membership functions are considered based on abstract-level, ranked-level and measurement-level models and their effectiveness is estimated under different Voronoi-based zoning methods. The experimental tests, carried out in the field of hand-written numeral recognition, show that the best results are obtained when measurement-level models based on exponential models are used as membership functions.

In this paper a new system for dynamic signature verification is presented. It is based on the consideration that each region of an handwritten signature can convey personal characteristics in diverse domains. Therefore, a multi-expert approach is considered in which each stroke of the signature is evaluated in the most profitable domain of representation. The experimental results demonstrate the effectiveness of the proposed approach.

Zoning is a widespread technique for hand-written character recognition. When a zoning method is considered, the pattern image is subdivided into zones each one providing regional information related to a specific part of the pattern. This paper presents an overview of zoning methods. Through the paper, both static and dynamic zonings are addressed and the most recent approaches for zoning design are discussed, based on genetic algorithms and well-suited zoning representation techniques. Finally, the role of membership functions in zoning-based classification is focused, according to abstract-level, ranked-level and measurement-level weighting models.

The evaluation of combination methods for multi-classifier systems is a difficult problem. In many cases multi-classifier combination methods are too complex to be formally studied and the experimental approach is the unique possible strategy. Of course, in order to simulate a multitude of real working conditions, sets of artificial classifiers with diverse characteristics must be generated. This paper presents an effective technique for generating sets of artificial classifiers with different characteristics both at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). In the experimental tests, sets of artificial classifiers simulating different working conditions are generated and the performances of abstract-level combination methods are estimated. The results points out the effectiveness of the new technique for generating sets of artificial classifiers with different characteristics and their usefulness in estimating the performances of combination methods.

This paper focuses the role of membership functions in zoning-based classification. In fact, the effectiveness of a zoning methods depends not only on the way in which the pattern image is partitioned by the zoning, but also on the criteria adopted to define the way in which a feature influences the diverse zones. For this purpose, an experimental investigation is presented, that focuses the most valuable way in which a features spreads its influence on the zones of the pattern image. The experimental tests have been carried out in the field of handwritten digit recognition, using the numeral digits of the CEDAR database. The result points out the membership function has a paramount relevance on the classification performance and demonstrate that the exponential model outperforms other membership functions.

This paper addresses the problem of multi-classifier system evaluation by artificially generated classifiers. For the purpose, a new technique is presented for the generation of sets of artificial abstract-level classifiers with different characteristics at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). The technique has been used to generate sets of classifiers simulating different working conditions in which the performance of combination methods can be estimated. The experimental tests demonstrate the effectiveness of the approach in generating simulated data useful to investigate the performance of combination methods for abstract-level classifiers.

Classifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system strongly depends on the decision fusion strategies. In this field, one of the most significant aspects concerns output normalization,when classifiers decisions are provided at measurement level. This paper presents a new approach for output normalization that uses dynamic time warping (DTW). Some experimental tests have been carried out in the field of handwritten digit recognition. The proposed approach is superior to other output normalization algorithms in the literature.

Multi-classifier approach is a widespread strategy used in many difficult classification problems.Traditionally, in a multi-classifier approach, a classification decision based on the combination of a multitude of classifiers is expected to outperform the decisions of each individual classifier. Therefore, in a multi-classifier systems, the potential of the whole set of classifiers is only exploited at the level of the final decision, in which the contributions of all classifiers is used by combining their individual decisions.This paper shows a feed-back based multi-classifier system in which the multi-classifier approach is used not only for providing the final decision, but also for improving the performance of the individual classifiers, by means of a closed-loop strategy.The experimental tests have been carried out in the field of hand-written numeral recognition. The result demonstrates the effectiveness of the proposed approach and its superiority with respect to traditional approach.

In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

In pattern recognition, zoning is one of the most effective methods for extracting distinctive characteristics from patterns. So far, many zoning methods have been proposed, based on standard partitioning criteria of the pattern image. In this paper, a new technique is presented for zoning design. Zoning is considered as the result of an optimization problem and a genetic algorithm is used to find the optimal zoning that minimizes the value of the cost function associated to the classification. For this purpose, a new description of zonings by Voronoi diagrams is used, which is found to be well suited for the genetic technique. The experimental tests, carried out in the field of handwritten numeral and character recognition, show that the proposed technique leads to zonings superior to traditional zoning methods

This paper presents a multi-expert system for dynamic signature verification. The system uses a stroke-oriented description of signatures well-suited for multi-expert approach. Each stroke is analysed in multiple representation domains to verify locally both the shape and dynamics of a signature. A two-level scheme for decision combination is used to combine local decisions. At the first level soft- and hard- combination rules are used to combine decisions from different representation domains. At the second level simple and weighted averaging is used to combine decisions from different parts of the signature. 1.

This paper presents a new technique to improve the combination of classification decisions obtained from local analysis of patterns. Specifically, a genetic algorithm is used to determine the optimal weight vector to balance the local decisions in the combination process. The experimental results, carried out in the field of hand-written numeral recognition, demonstrate the effectiveness of the new technique. 1.

Computer document treatment generally requires specific techniques to handle documents. Among the others one of great interest is resizing. The effect of resizing is more evident when thin lines are included in the document. In this case a high degradation and consequently great loss of information occurs. In this paper different reduction techniques, in terms of computational load and information loss referred to graphics and technical drawings, are compared. Experimental results, related to document treatments, are included in the paper. The results are obtained using a threshold technique optimized for line drawing reduction.

This paper presents an effective procedure to select the reference specimens for a signature verification system. Specifically, from the analysis of local stability in handwritten signatures, a suitable measure is proposed to determine the capability of different sets of signatures in supporting effective verification. The measure uses a correlation-based criterium which detects and recovers non-linear time distortions in different specimens. In the experimental test, the selected set of signatures has been used for reference in a system for dynamic signature verification based on a multi-expert verification strategy. The experimental results points out the capability of the new technique in selecting effective reference signatures.

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system. Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system. Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

In this paper a multi-expert signature verification system is presented. The system has been specifically designed for applications in the field of bankcheck processing. For this purpose, it combines three different algorithms for signature verification. A wholistic approach is used in the first algorithm, a component-oriented approach is used in the second and third algorithms. The second algorithm is based on a structure-based procedure, the third algorithm uses a highly-adaptive neural network. The three algorithms are combined in the multi-expert system by a voting strategy.

A new algorithm for the recognition of the numerical amount on bankchecks is presented. Mainly, it results from the modification of the well known Baptista-Kulkarni algorithm (1988). The improvements proposed are based on the recognition by parts approach, which promises a better than human character recognition ability in a context free situation. It is shown that with this algorithm a misrecognition rate of lower than 1% can be reached with a reasonably high rejection rate, using numerals extracted from a common database. Better performances could be obtained using numerals extracted from Italian bankchecks, due to the greater attention that people pay to writing amounts on them.

This paper presents a complete procedure for the segmentation of handwritten numeric strings. The procedure uses an hypothesis-then-verification strategy in which multiple segmentation algorithms based on contiguous row partition work sequentially on the binary image until an acceptable segmentation is obtained. At this purpose a new set of algorithms simulating a "drop falling" process is introduced. The experimental tests demonstrate the effectiveness of the new algorithms in obtaining high-confidence segmentation hypotheses.

Signing is a complex and highly variable process. It results from simple bodily motions of a ballistic nature whose combined effects produce the fundamental components of the signature. Therefore, signature verification can be performed by adopting local verification strategies for the detection of personal characteristics in fundamental components. In this paper, an on-line component-oriented signature verification system is presented. During the training phase, the set of fundamental components of each signer is derived and a suitable component-oriented knowledge-base is created by an automated technique. The verification is accomplished through a step-wise process. In the first step the structural organization of the signature is checked. In the second step each component is examined using spectral analysis. The experimental result shows the effectiveness of this approach compared with other techniques in the literature.

A complete system for automatic processing of bankchecks is presented. It consists of several modules, some of them work in parallel, others follow a serial scheme. They are devoted to machine-printed numeral recognition, layout processing, handwritten digit amount recognition, handwritten worded amount recognition, amount validation, and signature verification. Integrated approaches and crosscheck strategies are used to meet the severe constraints required by the application.>

In this paper a new thinning algorithm for binary patterns is presented. The algorithm is based on an iterative controlled removal procedure working on entire regions of the pattern. The thinning process permits to obtain skeletons retaining important specificities of the pattern useful for robust structural descriptions. The experimental results carried out on typewritten and handwritten characters point out the efficacy of the algorithm with respect to other techniques in literature.

A technique for number comparison in the residue number system is presented, and its theoretical validity is proved. The proposed solution is based on using a diagonal function to obtain a magnitude order of the numbers. In a first approach the function is computed using a suitable extra modulus. In the final implementation of the technique the extra modulus has been inserted in the set of moduli of the residue system, avoiding redundancy. The technique is compared with other approaches.>

A transputer-based parallel machine for handwritten character recognition is proposed. An algorithm based on structural features and on a tree classifier was used to accomplish the pre-classification of the unknown sample in order to speed up the recognition process. The algorithm for the final classification is based on the description of the strokes through Fourier descriptors. The learning phase is accomplished through a man-machine interactive process. The proposed system can expand its knowledge base. A special representation of this knowledge base is proposed in order to record a great amount of data in a suitable way. A fast multistroke handwritten isolated character recognition system is presented. The test of this system was performed on a PC based prototype while the realization of a parallel transputer based working machine is in progress. Experimental results obtained applying these machines to handwritten numerals recognition are reported.>

The most interesting recent results of the research in the field of handwritten character and cursive script recognition are discussed and the most promising directions for further researches are focused on.>

A statistical analysis on the classification of ambiguous characters as accomplished by humans recognizers is presented. For this purpose some statistical measurements to evaluate ambiguity are proposed. The experimental results carried out taking into account handwritten numerals, make evident some important aspects of human behaviour in pattern classification.>