Risk prediction is quite fundamental in enhancing decision-making in hybrid project management conditions that are uncertain and continuously changing operating conditions. This paper hypothesizes an explainable ensemble machine learning model to be used in accurately predicting the risk levels of projects based on publicly available Project Management Risk Raw Dataset retrieved through Kaggle. Several learning algorithms are incorporated in the proposed framework coupled with a smart aggregation approach to increase predictive stability without losing interpretability. The experimental analysis shows that the given model has the overall classification rate of 96.0% with precision and recall of 95.8% and 95.6%, respectively, which is higher compared to the traditional machine learning models, i.e., Logistic Regression, Support Vector machine, and Random Forest. The model also achieves an average AUC value of 0.96 which implies great discrimination of low-risk, median-risk and high-risk types of projects. Explainability analysis indicates that there is a significant association between project characteristics and risk outcomes, which enables the support of transparent decision making. The findings substantiate the claim that the suggested structure enhances the reliability of risk identification and allows a proactive response in the form of management interventions in complicated project settings.

This paper investigated the Bitcoin blockchain and other crypto currencies confidential transaction techniques for cryptographic commitment in a zero-knowledge proof system. A cryptocurrency transaction on a blockchain contains information about the sender and the receiver transaction is public. Therefore, in order to handle our sensitive data like transaction amounts should be hidden. In this paper, we study a new novel quantum non-interactive zero-knowledge proof (QNIZK) for Quantum Range Proof (QRP). Our design of both components improves the efficiency and quantum circuit without requiring a trusted setup over prior work. Since quantum range proof is more efficient in performing q-bit measurements and explains the algorithm as well as quantum state measurement. Quantum Range Proof requires an inner product in Hilbert complex vector space to measure wave function |ψ├⟩┤ in two different quantum states |0├⟩ or ┤ |1├⟩┤ for the verifier. Thus, proving the commitment value is in the range [0 ≤ k-1], we also describe witness is measured in terms of different quantum states in non-polynomial space complexity is 0√N verification time, if the quantum circuit is unstructured search. A typical experiment shows the verification time in milliseconds for the generation of Proof for Quantum Circuits in computing.

Machine Learning (ML) systems are now widely used across various fields such as hiring, healthcare, and criminal justice, but they are prone to unfairness and discrimination, which can have serious consequences for individuals and society. Although various fairness testing methods have been developed to tackle this issue, they lack the mechanism to continuously monitor ML system behaviour at runtime. In this study, a runtime verification tool called BiasTrap is proposed to detect and prevent discrimination in ML systems. The tool combines data augmentation and bias detection components to create and analyse instances with different sensitive attributes, enabling the detection of discriminatory behaviour in the ML model. The simulation results demonstrate that BiasTrap can effectively detect discriminatory behaviour in ML models trained on different datasets using various algorithms. Therefore, BiasTrap is a valuable tool for ensuring fairness in ML systems in real-time.

Blockchain technology is incredibly popular nowadays which is based on a distrusted ledger technology (DLT) and decentralized database that stores encrypted blocks of data in transparency to the public. In this paper, we proposed a Quantum Range Proof a new non-interactive zero-knowledge (NIZK) proof protocol containing logarithmically small proof that lacks a trusted system. A NIZK argument is provided for the satisfy ability of a quantum circuit containing quantum range proof complexities that logarithmically grow in the quantum circuit size. The witness complexities a referred to as probability distribution measurement and for a quantum circuit containing N-dimensional complex space (α , β ) , the soundness property of our argument convinces a verifier with the probability of quantum range proof. A novel argument system is an effective non-interactive zero knowledge of opening witness that lies between inner product spaces over the spin in N-dimension complex space. The inner product space requires logarithmic time complexity to find the witness in quantum range proof for both verifier and prover. In addition to this, a commitment schema is developed to attain a non-polynomial probability distribution and the witness at an arbitrary point in quantum state in a demonstrable manner is revealed. The efficiency of quantum range proof is particularly well suited for the non-polynomial probability distribution and trustless nature of blockchain.

Modern software systems are becoming more intricate, making identification of risks in the software requirement phase— a fundamental aspect of the software development life cycle (SDLC)—complex. Inadequate risk assessment may result in the malfunction of a software system, either in the development or production phase. Therefore, risk prediction plays a crucial role in software requirements, serving as the first step in any software project. Hence, developing adaptive predictive models that can offer consistent and explainable insights for handling risk prediction is imperative. This study proposes novel ensemble class balanced nested dichotomy (EBND) fuzzy induction models for risk prediction in software requirement. Specifically, the proposed EBND models employ a hierarchical structure consisting of binary trees featuring distinct nested dichotomies that are generated randomly for each tree. Thereafter, we use an ensemble principle to refine rules generated from the resulting binary tree. The predictive efficacy of the suggested EBND models is further extended by introducing a data sampling method into their prediction process. The inclusion of the data sampling method acts to mitigate the underlying disparity in the class labels that may affect its prediction processes. The efficacy of the EBND models is then evaluated and compared to current solutions using the open-source software risk dataset. The observed findings revealed that the EBND models demonstrated superior predictive capabilities when compared to the conventional models and state-of-the-art methodologies. Specifically, the EBND models achieved an average accuracy threshold value of 98%, as well as high values for the f-measure metric.

This research paper focuses on developing a model that learns and emulates the personalized musical style of composers. Pitch estimation of music signals is a fundamental problem in automatic transcription systems, with applications in music information retrieval and automated musicological analysis. While pitch estimation for monophonic music signals is considered solved, estimating the pitch of multiple concurrent sources remains challenging. This paper explores the challenges and techniques involved in monophonic pitch detection, highlighting the differences from polyphonic detection and emphasizing the need for improved accuracy. Additionally, the paper discusses the potential of using machine learning algorithms to synchronize music in real-time with a composer’s input, enabling interactive improvisation. The project’s scope includes the development of algorithms for pitch detection, synchronization, and AI-based melody generation, with applications ranging from music composition assistance to interactive performance systems.

As machine learning software increasingly shapes crucial decisions in our daily lives, ensuring the fairness of these decisions is paramount. Individual fairness guarantees non-discrimination based on protected attributes, such as race or gender. Discriminatory instances reveal individual discrimination included in machine learning software. Existing methods for detecting individual discrimination often rely on initial "seed" instances, which are data points selected from the dataset that have more likelihood of exhibiting discrimination. These seed instances are then used as the basis for generating more discriminatory instances. While effective, this approach may inadvertently overlook discrimination embedded within seemingly fair non-seed instances. To overcome this limitation, this paper proposes FairBS, an approach that utilizes non-seed instances to generate discriminatory instances using counterfactual analysis. FairBS first constructs an explainer based on dataset input features and a model under test. It then generates an input instance and checks it for discrimination. If the input instance is non-discriminatory, FairBS uses the explainer to create counterfactual examples of that instance, by causing minimal perturbation to its feature values, which then produce other instances with opposite predictions. Extensive experiments on five datasets and five machine learning models demonstrate that our proposed approach outperforms state-of-the-art methods in both efficiency and effectiveness across all datasets and models. Our approach generates an average of x 13.44 more discriminatory instances at x 14.51 faster speed compared to existing seed-based methods. These findings indicate that FairBS expands the boundaries of fairness testing beyond the discriminatory seed instances, providing a powerful tool that can be used by software engineers to better ensure fairness in machine learning software.

The widespread adoption of ML systems across critical domains like hiring, finance, and healthcare raises growing concerns about their potential for discriminatory decision-making based on protected attributes. While efforts to ensure fairness during development are crucial, they leave deployed ML systems vulnerable to potentially exhibit discrimination during their operations. To address this gap, we propose a novel framework for on-the-fly tracking and correction of discrimination in deployed ML systems. Leveraging counterfactual explanations, the framework continuously monitors the predictions made by an ML system and flags discriminatory outcomes. When flagged, post-hoc explanations related to the original prediction and the counterfactual alternatives are presented to a human reviewer for real-time intervention. This human-in-the-loop approach empowers reviewers to accept or override the ML system decision, enabling fair and responsible ML operation under dynamic settings. While further work is needed for validation and refinement, this framework offers a promising avenue for mitigating discrimination and building trust in ML systems deployed in a wide range of domains.

Blockchain technology may be a recent advancement and offers a ground-breaking technique for keeping the knowledge for an extended time and completing transactions like knowledge management, knowledge handling, performing arts functions, associated establishing trust in an open atmosphere. Most of them are considering block chain as a technology innovation significantly for cryptography and cyber security with systems like bitcoin, IoT, sensible Grids and etc., albeit this technology proofs its name and has received ton and ton of growing interests in multiple dimensions, the safety and security of the block chains are still in analysis whereas deploying block chain in versatile domains and environments. This work elaborates a comprehensive summary of the safety and privacy of block chain from knowledge management perspective. attention platform is employed for implementation and testing. Around 20,000 records are being taken and valid mistreatment the projected algorithmic program. Initially, a block chain is created using distributed information, which tracks an ever-changing list of trading records by organising them into a hierarchic chain of block. Because attention knowledge necessitates greater security, a peer-to-peer overlay network is used to create and maintain the block chain, which is secured through the intelligent and suburbanized use of cryptography with crowd computing. ALM algorithmic program projected has been increased with applicable knowledge possession by providing 2 issue authentications with an accuracy of 95%.The projected system may be a decentralized system and thence guaranteeing higher knowledge transparency and auditability. Security and privacy properties are being ensured by providing fine grained access management on the highest of the ALM encoding. Hence, Security and privacy problems with current scenario are addressed well with a high-level access management rulesets.

Technological advancements in Information and Communication Technology (ICT) have transformed the computing paradigm, introducing various communication channels, with the Internet of Things (IoT) playing a crucial role. The Internet of Medical Things (IoMT) is a specialized category within IoT, enabling medical devices to communicate for sharing sensitive data, and improving patient care. However, these advancements also pose security and privacy challenges, including replay, man-in-the-middle, impersonation, and other attacks. To address these issues, machine learning algorithms are extensively employed in Intrusion Detection Systems (IDS) to dynamically detect and classify attacks at the network and host levels. Researchers have developed numerous supervised and unsupervised algorithms for reliable anomaly detection. The primary challenge lies in adapting IDS models to the dynamic and random behaviour of malicious attacks while designing scalable solutions. This paper explores the use of a Convolutional Neural Network (CNN) with Elephant Herding Optimization to create an effective IDS in the IoMT environment, aiming to classify and predict unforeseen cyberattacks. The CNN model undergoes pre-processing, optimization, and tuning of network parameters using hyperparameter selection methods. Experimental results, comparing the CNN with other machine learning algorithms on a benchmark intrusion detection dataset, demonstrate that the proposed model outperforms existing approaches. The CNN model exhibits a 17% increase in accuracy and a 35% decrease in time complexity, facilitating faster alerts to prevent post-effects of intrusion in sensitive cloud data storage.

With the advent of technology and use of latest devices, they produce voluminous data. Out of it, 80% of the data are unstructured and remaining 20% are structured and semi-structured. The produced data are in heterogeneous format and without following any standards. Among heterogeneous (structured, semi-structured and unstructured) data, textual data are nowadays used by industries for prediction and visualization of future challenges. Extracting useful information from it is really challenging for stakeholders due to lexical and semantic matching. Few studies have been solving this issue by using ontologies and semantic tools, but the main limitations of proposed work were the less coverage of multidimensional terms. To solve this problem, this study aims to produce a novel multidimensional reference model using linguistics categories for heterogeneous textual datasets. The categories in such context, semantic and syntactic clues are focused along with their score. The main contribution of MRM is that it checks each tokens with each term based on indexing of linguistic categories such as synonym, antonym, formal, lexical word order and co-occurrence. The experiments show that the percentage of MRM is better than the state-of-the-art single dimension reference model in terms of more coverage, linguistics categories and heterogeneous datasets.

Deep reinforcement learning agents need to be trained over millions of episodes to decently solve navigation tasks grounded to instructions. Furthermore, their ability to generalize to novel combinations of instructions is unclear. Interestingly however, children can decompose language-based instructions and navigate to the referred object, even if they have not seen the combination of queries prior. Hence, we created three 3D environments to investigate how deep RL agents learn and compose color-shape based combinatorial instructions to solve novel combinations in a spatial navigation task. First, we explore if agents can perform compositional learning, and whether they can leverage on frozen text encoders (e.g. CLIP, BERT) to learn word combinations in fewer episodes. Next, we demonstrate that when agents are pretrained on the shape or color concepts separately, they show a 20 times decrease in training episodes needed to solve unseen combinations of instructions. Lastly, we show that agents pretrained on concept and compositional learning achieve significantly higher reward when evaluated zero-shot on novel color-shape1-shape2 visual object combinations. Overall, our results highlight the foundations needed to increase an agent's proficiency in composing word groups through reinforcement learning and its ability for zero-shot generalization to new combinations.

Nowadays, People watch movies to unwind from their hectic lives.However, Due to the vast selection of films that are available worldwide, choosing a movie to watch is not a simple task.This system combines collaborative filtering with content-based filtering.wherein this system of recommendations suggests a film to the user depending on the material which identified by user's hidden pattern furthermore based on a user's watching pattern similarity with another.In this article, Machine learning based recommender application system would be constructed using Hybrid filtering then would be deployed using Heroku.In this system, Machine learning based recommender application system with Heroku Deployment will be constructed using a both filtering based on content and collaborative filtering approach.Heroku Deployment makes application deployment simple and better user experience.

State-of-the-art visual grounding models can achieve high detection accuracy, but they are not designed to distinguish between all objects versus only certain objects of interest. In natural language, in order to specify a particular object or set of objects of interest, humans use determiners such as "my", "either" and "those". Determiners, as an important word class, are a type of schema in natural language about the reference or quantity of the noun. Existing grounded referencing datasets place much less emphasis on determiners, compared to other word classes such as nouns, verbs and adjectives. This makes it difficult to develop models that understand the full variety and complexity of object referencing. Thus, we have developed and released the DetermiNet dataset, which comprises 250,000 synthetically generated images and captions based on 25 determiners. The task is to predict bounding boxes to identify objects of interest, constrained by the semantics of the given determiner. We find that current state-of-the-art visual grounding models do not perform well on the dataset, highlighting the limitations of existing models on reference and quantification tasks.

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.

Asset buyers and sellers share lots of their private and financial details with real estate agents and if the information is exposed to malicious users, then they cost a fortune to the person whose information has been sabotaged. In these cases, the brand reputation of the businesses is also diminished. Data security is a technique that comes to the rescue by preventing digitized data from cyberattacks. This study utilizes blockchain architecture to secure real estate information from cybercriminals. Blockchain is a decentralized network where the computers (nodes) interconnected to each other is represented as a chain of blocks that secures the user’s confidential information genuinely. To ensure the overall protection of the real estate agencies information, we propose a geometric octal zones distance estimation optimization algorithm (GOZDE) optimized attribute-based encryption (ABE) (GOZDE-ABE) technique. The GOZDE-ABE increases the throughput of the ABE offering increasing privacy and accuracy levels of the confidential data. We also demonstrate the working of the blockchain-based real estate model for secure data sharing with the consensus and smart contract mechanism designed. The combined concepts of blockchain and the ABE technique become powerful tools to store and securely transfer sensitive data. The blockchain approach ensures data integrity and the ABE concept guarantees access control and authorization. The encrypted real estate documents are stored in the Inter Planetary File Systems (IPFS) and their respective hash files are generated subsequently. These hash files help to determine the exact location of files in the IPFS efficiently. The experimental results are conducted in terms of encryption time, decryption time, ciphertext storage space, storage overhead analysis, etc. The results show that the proposed model improves the security of the business information and high-value money transactions exchanged via blockchain by the GOZDE-ABE technique.

Many studies have been conducted to explore the influence of feature selection (FS) techniques on software defect prediction (SDP) models, with conflicting empirical results and research outcomes. These reported contradictions may be due to relative research limitations, such as types of FS techniques or the size of defect datasets. In the instance of FS methods, it was discovered that selecting a suitable threshold value for picking top-ranked features in FS methods might be a cause of discrepancies in reported findings on SDP. Investigating and assessing the impacts of threshold values for the rank-based filter (RBF) FS techniques, as done in this work, becomes critical. 4 RBF (Chi-square, Correlation, Information Gain, and Relief) methods with 5 thresholds (No FS, log2N, Top20%, Top 30%, and Top 50%) values were investigated with 2 prediction models (Naive Bayes (NB) and Decision Tree (DT)) on 25 software defects datasets. The experimented RBF techniques were selected based on distinct computational features to assure heterogeneity, as well as their performance in the current SDP research. Developed SDP models were evaluated using accuracy and area under the curve (AUC) values while the Scott-KnottESD rank statistical test technique was employed to rank experimented RBF methods with applied threshold values. According to the experimental results, selecting the Top20% of top-ranked features in RBF methods had a greater (positive) impact on the prediction performances of SDP models than other applied threshold values. Furthermore, the outcomes of this study corroborate previous research on the capacity of FS techniques to improve the prediction efficacies of SDP models. Consequently, we urge that FS methods be utilized in SDP tasks. In the case of RBF methods, the Top20% threshold value should be used since it outperforms de-factor log2N and other threshold values. Moreover, findings from this study can be a guide to subsequent SDP studies and further strengthen the tenacity of experimental findings and conclusions in SDP studies.

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.