An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

Proposition on Improving Environmental Forensic System in China

In the early period of China,economy developed rapidly at the cost of environment.Recently,it is generally recognized that the heavily polluted environment not only puts a brake on economic development but also paces negative impact on people’health as well as probably next decades of generations.Accordingly,the latest Environmental Protection Law revised in 2014 makes a clear‑cut division of environmental responsibility and regulates stricter penalties of breaching law.As the new environmental law is enforced gradually,environmental forensic is increasingly required in the process of ascertaining facts in judicial proceedings of environmental cases.Based on the outcomes of documentary analysis for all environmental cases judged on the basis of new environmental law,it is concluded that there still exists problems in the present system of environmental forensic.Thus,this paper is aimed to make proposition on improving Chinese environmental forensic system,which involves:(i)promoting capability of EFS to handle professional questions;(ii)develop price mechanism;(iii)multidepartments cooperate to establish unifying and complete EFS system;and(iv)enhance the probative value of results of EFS.Such protocol for amending present regulation on environmental forensic is of significant importance because a quality report of environmental forensic will contribute to provide strong probative evidence of culprits’activity of releasing contaminant into environment,degree of damages for victims,and above all,causality between the behavior of public nuisance and damages.

Differences between British and Japanese perspectives on forensic mental health systems:A preliminary study

AIM To clarify the differences in views on forensic mental health(FMH) systems between the United Kingdom and Japan.METHODS We conducted a series of semi-structured interviews with six leading forensic psychiatrists. Based on a discussion by the research team, we created an interview form. After we finished conducting all the interviews, we qualitatively analyzed their content. RESULTS In the United Kingdom the core domain of FMH was risk assessment and management; however, in Japan, the core domain of FMH was psychiatric testimony. In the United Kingdom, forensic psychiatrists were responsible for ensuring public safety, and psychopathy was identified as a disease but deemed as not suitable for medical treatment. On the other hand, in Japan, psychopathy was not considered a mental illness. CONCLUSION In conclusion, there are considerable differences between the United Kingdom and Japan with regard to the concepts of FMH. Some ideas taken from both cultures for better FMH practice were suggested.

Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

Improving DNA Data Capacity:Forensic Parameters and Genetic Structure Analysis of Jinjiang Han Population with the Microreader^(TM) Y Prime Plus ID System

Objective Population genetic analysis based on genetic markers harbors valuable forensic applications.In this regard,it is informative and imperative to explore Han groups as they are the largest population of China.In particular,there is a largely underrepresented amount of information from recent decades regarding the southeast costal Han Chinese.Therefore,the aim of this study is to investigate the available genetic characteristics of the Han population living in the Jinjiang,Fujian Province,Southeastern China.Methods We sampled 858 saliva samples and used the commercially available Microreader^(TM) Y Prime Plus ID System to identify population data of Y-short tandem repeat(STR)loci of this region.Results A total of 822 different haplotypes were observed.The overall haplotype diversity,discriminatory power and haplotype match probability were 0.9999,0.9999 and 0.0012,respectively.Conclusion Our results showed that the Jinjiang Han population was closely genetically related to Han groups of China.Overall,we identified a set of 37 Y-STRs that are highly polymorphic,and that can provide meaningful information in forensic practice and human genetic research.

Blockchain⁃Based Log Verification System for Cloud Forensics

In this age when most organizations make use of cloud computing,it is important to not only protect cloud computing resources from cyber⁃attacks but also investigate these attacks.During forensic investigations in a cloud environment,the investigators fall on service providers for pieces of evidence like log files.The challenge,however,is the integrity of these logs provided by the service providers.To this end,we propose a blockchain⁃based log verification system called BlogVerifier that uses a decentralized approach to solve forensics issues in the cloud.BlogVerifier extracts logs produced in cloud environments,hashes these logs and stores the hashed values as transactional values on the blockchain.The transactions are then merged into blocks and shared on the blockchain.The proposed system also ensures the continuation of an investigation even when the primary source of a log is compromised by using encryption and smart contracts.The proposed system also makes it possible for any stakeholder involved in the forensic process to verify the authenticity of log files.The performance results show that BlogVerifier can be integrated into the cloud environment without any significant impact on system resources and increase in computational cost.

Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations(developed and developing countries)worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness,authenticity and objectivity of the information obtained.Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks,threat,viruses,intrusion among others going on every day among internet of things.However,it is noted that cybersecurity is based on confidentiality,integrity and validity of data.The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning,computational intelligence,soft computing to cybersecurity and cyber forensics.

Forensic Study on Objective Evaluation of Visual Acuity of Ametropia with the Event-related Potential P3

Objective In this study,we aimed to assess the characteristics of the P3 component from an event-related potential(ERP)that was induced by visual acuity(VA)processing.Furthermore,we sought to provide electrophysiological evidence for the objective evaluation of VA.Methods We recruited 32 participants with myopia-related ametropia.They reported no other ocular diseases and had an uncorrected VA of 4.0 in both eyes.We used the block letter“E”at different visual angles and orientations as the graphic stimuli.The oddball paradigm,consisting of 4 modules,was used for ERP analysis.The standard stimuli of each module were identical,with a visual angle of 1°15′.The visual angles of the target stimuli were 1°15′,55′,24′,and 15′.The VA test was performed on each eye separately for all participants,and all characteristics of the P3 component were analyzed.Results There was no significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 55′group,or between the target stimulation angle 24′group and the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 24′group as well as the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 55′group and the 24′group as well as the 15′group.No significant differences were observed in the P3 amplitude between modules.Conclusion In the oddball paradigm,P3 elicitation indicated a cognitive response to the target stimuli.These data showed that the characteristics of P3 can be used as an objective evaluation of VA.

Comparison Study and Forensic Analysis between Experiment and Coupled Dynamics Simulation for Submerged Floating Tunnel Segment with Free Ends under Wave Excitations

This paper presents dynamic-behavior comparisons and related forensic analyses of a submerged floating tunnel(SFT)between numerical simulation and physical experiment under regular and irregular waves.The experiments are conducted in the 3Dwave tank with 1:33.3 scale,and the corresponding coupled time-domain simulation tool is devised for comparison.The entire SFT systemconsists of a long concrete tunnel and 12 tubular aluminummooring lines.Two numerical simulation models,the Cummins equation with 3D potential theory including second-order wave-body interaction effects and the much simpler Morison-equation-based formula with the lumped-massbased line model,are designed and compared.Forensic analyses for mooring-line adjustments in the simulation are carried out in view of the best representation of the physical system.After that,the measured pre-tension distribution and systemstiffness of twelvemooring lines arewell reproduced in the numericalmodel.Subsequently,the dynamic responses and mooring tensions of the SFT are compared under regular and irregular waves.The measured and simulated results coincide reasonably well for both regular-and irregular-wave conditions.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.

Research on Metaverse Security and Forensics

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment circles.However,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related risks.Metaverse is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities.Through social incentives and governance rules,the metaverse reflects the digital migration of human society.This article will conduct an in-depth analysis of the metaverse from the perspective of electronic data forensics.First,from the perspective of Internet development,the background and development process of the metaverse is discussed.By systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the metaverse.Secondly,from the perspective of metaverse security,the social risk and crime risks of the metaverse are discussed.Then the importance of metaverse forensics is raised.Third,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in depth.Taking smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet forensics.Finally,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation(PP)in Digital forensics(DF)is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and,as a result,are not efficient and support only a keyword search.Moreover,the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB)privacy guidelines.To have an efficient investigation process and meet the increased volume of data,the presented framework is designed based on the selective imaging concept and advanced encryption standard(AES).The proposed framework has two main modules,namely Selective Imaging Module(SIM)and Selective Analysis Module(SAM).The SIM and SAM modules are implemented based on advanced forensic format 4(AFF4)and SleuthKit open source forensics frameworks,respectively,and,accordingly,the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and,as a result,the proposed solution provides a privacy-preserving,efficient forensic imaging and analysis process while having also sufficient methods.Moreover,the AFF4 forensic image,produced by the SIM module,can be analyzed not only by SAM,but also by other well-known analysis tools available on the market.

Internet of Things for Digital Forensics Application in Saudi Arabia

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

Forensics: Collection of Sound Digital Evidence

This summary paper will discuss the concept of forensic evidence and evidence collection methods. Emphasis will be placed on the techniques used to collect forensically sound digital evidence for the purpose of introduction to digital forensics. This discussion will thereafter result in identifying and categorizing the different types of digital forensics evidence and a clear procedure for how to collect forensically sound digital evidence. This paper will further discuss the creation of awareness and promote the idea that competent practice of computer forensics collection is important for admissibility in court.

Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

Need for education of psychiatric evaluation of offenders with mental disorders:A questionnaire survey for Japanese designated psychiatrists

BACKGROUND The management of offenders with mental disorders has been a significant concern in forensic psychiatry.In Japan,the introduction of the Medical Treatment and Supervision Act in 2005 addressed the issue.However,numerous psychiatric patients at risk of violence still find themselves subject to the administrative involuntary hospitalization(AIH)scheme,which lacks clarity and updated standards.AIM To explore current as well as optimized learning strategies for risk assessment in AIH decision making.METHODS We conducted a questionnaire survey among designated psychiatrists to explore their experiences and expectations regarding training methods for psychiatric assessments of offenders with mental disorders.RESULTS The findings of this study’s survey suggest a prevalent reliance on traditional learning approaches such as oral education and on-the-job training.CONCLUSION This underscores the pressing need for structured training protocols in AIH consultations.Moreover,feedback derived from inpatient treatment experiences is identified as a crucial element for enhancing risk assessment skills.

Logical Image Acquisition and Analysis of Android Smartphones

Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possible to acquire a physical or logical image of these devices. Acquiring physical and logical images has advantages and disadvantages compared to each other. Creating the logical image is done at the file system level. Analysis can be made on this logical image. Both logical image acquisition and analysis of the image can be done by software tools. In this study, the differences between logical image and physical image acquisition in Android smartphones, their advantages and disadvantages compared to each other, the difficulties that may be encountered in obtaining physical images, which type of image contributes to obtaining more useful and effective data, which one should be preferred for different conditions, and the benefits of having root authority are discussed. The practice of getting the logical image of the Android smartphones and making an analysis on the image is also included. Although root privileges are not required for logical image acquisition, it has been observed that very limited data will be obtained with the logical image created without root privileges. Nevertheless, logical image acquisition has advantages too against physical image acquisition.

Truth Behind Speech:Forensic Linguistic Analysis on Speech

We are living in a society constructed by many aspects as well as languages.There are many ways to deal with legal cases,language is also an active one among them.As it is proved that resultant of forensic linguistic researches do help around.