Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.

Epileptic sseizure’s detection and classification is a challenging task as it happens for a short duration. Machine Learning techniques provide the way to enable faster and more accurate diagnosis and treatment. This paper investigates the use of ten mainly utilized ML models, including LR, NB, KNN, SGD, SVM, Random Forest, DT, ET, Gradient Boosting, and XGBoost, to evaluate and compare their performance in detecting and classifying seizures and non-seizures classes. This paper has used benchmark Bonn time series EEG dataset for seizure detection. The evaluation matrices F1-score, precision, recall, and accuracy were 100

Cloud storage security remains a major challenge owing to threats related to confidentiality, integrity, and unauthorized access. In this study, a novel secure cloud storage system is proposed by integrating Lorenz 3D chaotic key generation, AES-256-GCM authenticated encryption, and blockchain verification based on PBFT. High-entropy keys were generated using the Lorenz chaotic system and then passed through SHA-256 to create secure 256-bit AES keys. The data is encrypted using the AES-256-GCM algorithm and stored in the cloud, and integrity metadata is stored in a blockchain that offers tamper detection, auditability, and Byzantine fault tolerance. The experimental results demonstrate strong cryptographic performance with near-ideal entropy (7.99), a high avalanche effect (50.05%), successful NIST randomness validation, and low blockchain overhead. The results of the security analysis prove resistance against brute force, statistical, differential, replay, known plaintext, chosen ciphertext, and Byzantine attacks. The proposed framework offers a secure, efficient, and reliable multilayered security solution for cloud storage systems.

The Internet of Medical Things (IoMT) enables real-time health monitoring through wireless sensors and embedded computing, but raises critical concerns regarding patient data confidentiality in the era of quantum computing. Classical public-key cryptosystems, including RSA and ECC, remain vulnerable to Shor's algorithm, which efficiently solves integer factorization and discrete logarithm problems on quantum computers. Although the National Institute of Standards and Technology (NIST) has standardized post-quantum cryptographic algorithms that are resistant to quantum cyber threats, the available solutions are either computationally burdensome for resource-constrained IoMT devices or compromise the authentication of the end-to-end verification chain via proxy-based approaches. This article proposes a zero-trust-based post-quantum framework that leverages the power of edge-based computing offloading while maintaining the cryptographic verification chain. The architectural design employs CRYSTALS-Kyber-512 for key encapsulation, CRYSTALS-Dilithium-2 for dual-layer digital signatures, and AES-256-GCM for symmetric key encryption. The quantum-secure edge server (QSES) carries out computationally expensive post-quantum computations, while digital signatures from devices are verifiable at application servers, thereby allowing for nonrepudiation, which is critical for medical liability applications. These security proofs show existential unforgeability under adaptive chosen message attacks (EUF-CMAs) in the quantum random oracle model (QROM). They are based on the standard assumptions of module-LWE and module-SIS hardness and were checked again using the Scyther tool and a full end-to-end security analysis. Performance evaluation demonstrates 1.87-ms client-side computation with 8416 bytes communication overhead, achieving faster processing than full on-device post-quantum implementations while maintaining end-to-end authentication properties absent in existing edge-offloading schemes.

In the last two decades we have seen massive growth in the area of quantum technology. Quantum computers can provide solution to certain classes of problem that classical computers fail to solve. Today quantum computers are used in various fields to solve difficult optimization problems and critical prediction problem in finance, artificial intelligence, and medicine. Hence a greater number of optimized quantum algorithms are being developed. Different quantum computing frameworks help us to program and develop the algorithms on real quantum hardware and simulators. In this paper we are presenting detailed study of different quantum computing tools and programming environments. We demonstrated some basic circuit using these quantum frameworks on real quantum hardware, simulators and discussed different quantum libraries and platform for different task.

Data science is becoming increasingly important in the current day for the analysis of medical data in order to enable detection and classification for improved diagnosis. Large volumes of data typically require more processing time and hardware resources, which limits the ability to quickly classify X-rays and ECGs, and generate accurate, high-quality images. This gap can be filled by using QML approach to the medical field. This approach may lead to notable improvements in error rate prevention, execution efficiency, and parameter optimization. Quantum machine learning advances the future of medical image processing and gives important insights for the interdisciplinary study of quantum systems and medical imaging. QML-based predictive models examine patient histories, genetic information, and drug response to create highly accurate personalized healthcare plans. The most recent advancements in medical image classification and image synthesis utilizing quantum machine learning are examined in this work, with a focus on notable benefits, challenges, and possible future directions.

Current era is witnessing rapid growth in data and fast data processing methods. In the past few decades, artificial intelligence has matured exponentially and has been applied in many areas such as medicine, transport, finance, and many more. Classical machine learning methods are resource intensive and usually training models takes time. Quantum machine learning can be a alternate solution to this problem and can be used to solve complex data problems. Quantum machine learning utilizes parallel computing capabilities of quantum computers. Quantum machine learning methods can be used for image classification, which is an important task in data science. Here, we have proposed a quantum convolutional neural network for color image classification, where we have applied a quantum convolutional and pooling layers to reduce the number of features without losing crucial information. We have used the proposed method to classify color image of the CIFAR-10 dataset. We have compared the validation accuracies and loss during training for both classical and quantum convolutional neural network. Our results show that we have achieved similar accuracy with less number of trainable parameters. We believe that this study will open new avenue for high dimensional data classification using quantum convolutional neural network in current and beyond NISQ era.

Cloud computing applications are becoming more popular as a result of several benefits, such as reliability, storage management, and data accessibility. Data saved in the cloud environment can be accessed at any time and from any location via network access. The enormous volume of user data sharing raises the likelihood of assaults, and unauthorized users have easy data access. Because of its distributed and cohesive nature, blockchain technology improves cloud security. The cryptographic methodologies employed in blockchain for hash generation across blocks boost data security. The blockchain-based security technologies enable strong data security in the cloud environment. As a result, the hybrid elliptic curve, Elgamal technique with blockchain, is provided for application.

Cloud storage has revolutionized data storage and access, offering a convenient and scalable solution for storing large amounts of data. However, it also raises issues related to security and privacy, especially after storing the sensitive information on remote servers. In this context, attribute-based encryption (ABE) has added substantial consideration by means of a solution to enforce precise access control on encrypted information. A notable form of ABE is Ciphertext-Policy Attribute-Based Encryption (CP-ABE), allowing the data owner to state access rules depends on attributes (such as roles, permissions, or other user-specific factors). The application of Elliptic Curve Cryptography (ECC) in CP-ABE enhances the system’s efficiency and security. On top of this, integrating blockchain technology can provide decentralized and immutable record-keeping, which enhances the transparency, trust, and integrity of the encryption and decryption processes. A Blockchain-based CP-ABE system using ECC provides a robust, secure, and decentralized solution for managing access control to encrypted cloud storage. The integration of blockchain ensures transparency, immutability, and auditability, while ECC offers significant security with lower computational burden. By combining these technologies, we can build an effective and safe framework for protecting sensitive information in cloud environments, ensuring that access to and decryption of the information is restricted to authorized users only based on well-defined access policies.

Handling imbalanced datasets is crucial for improving the efficiency of machine learning techniques in healthcare applications. In epilepsy datasets, seizure instances are often underrepresented compared to non-seizure instances, which negatively impacts the effectiveness of classification algorithms. This study investigates the use of SMOTE-ENN and SMOTE-Tomek sampling methods to address data imbalance and evaluates their effect on the performance of various state-of-the-art classifiers. A comparative analysis of minority class prediction revealed that SMOTE-Tomek achieved higher recall for Logistic Regression, KNN, SVM, SGD, and Random Forest, while SVM achieved the best F1-score. Perfect recall and F1-scores (1.0) were observed for Decision Tree, Extra Trees, Gradient Boosting, and XGBoost classifiers after applying both sampling techniques. Experiments were conducted on the standard public Bonn EEG dataset, using ten classifiers for binary seizure detection. Overall, Decision Tree, Gradient Boosting, and XGBoost achieved 100

As the demand for cloud storage systems increases, ensuring the security and integrity of cloud data becomes a challenge. Data uploaded to cloud systems are vulnerable to numerous sorts of assaults, which must be handled appropriately to avoid data tampering issues. In addition, quantum computers are expected to be introduced soon, which may face multiple security issues by destroying all traditional cryptosystems. This work introduces a quantum-resistant blockchain centered data integrity verification system with the use of several techniques. Initially, the keys and signatures are generated by the users with the help of the lattice-based blind signature algorithm (L_BSA), which is a combination of lattice cryptography and a blind signature algorithm. From the generated random keys, the most optimal key is then selected by the Puzzle Optimization Algorithm (POA), which is then made available to the encryption phase. Then, the upgraded Merkle tree-assisted vacuum filter (Vac-UMT) algorithm is executed to accomplish the encryption task. Then the data are converted into blocks using blockchain technology and uploaded to the cloud. When receiving the audit requests, the verification process is carried out, and the evidence report is generated for the users. The proposed work is simulated in JAVA and assessed with the UNSW-NB15 dataset, and the outcomes demonstrated that the system is highly efficient and secure.

Task scheduling and resource utilization have always been among the most critical issues for high performance in heterogeneous computing. The heterogeneity of computation costs on a given set of computing elements and the communication costs among computing elements increase the complexity of the scheduling problem. Extensive research proves that the list-based task scheduling algorithms generate the most efficient schedules for complex workflow applications in the heterogeneous computing environment. The workflow applications comprise thousands of interconnected tasks with dependencies. In the last decades, various list-based scheduling algorithms have been proposed to achieve some kinds of performance objectives such as minimization of makespan and energy consumption and maximization of resource utilization and reliability. In this article, various list-based workflow scheduling algorithms have been reviewed from the last two decades with the assumption of heterogeneous computing systems being used as the underlying computing infrastructure. This review process categorizes the algorithms based on scheduling objectives. For a better analysis of the algorithms, each algorithm is compared with other algorithms based on its objectives, merits, comparison metrics, workload type, experimental scale, experimental environment, and results compared. Finally, experimental analysis of seven state-of-art algorithms has been conducted on randomly generated workflow to understand the working of list-scheduling algorithms. The main purpose of this article is to give proper direction to new researchers who are willing to work in workflow scheduling in heterogeneous computing environments.

Nowadays, the need for cloud computing has increased due to the exponential growth in information transmission. Cybercriminals are persistent in their efforts to breach cloud environments, even with security measures in place to protect data stored in the cloud. To address this challenge, an enhanced authentication approach is needed for enhanced security. In order to protect user privacy and anonymity in cloud environments, the study presents a novel technique called Hyperelliptic Curve-based Anonymous Ring Signature (HCARS). Moreover, Blockchain technology is utilized to securely record timestamps and cryptographic keys. The hashing functions in the Blockchain system employ SHA 256 and SHA 512 algorithms. Furthermore, utilizing Ring Learning with Error (RLWE) problems, an Nth degree Truncated Polynomial Ring Units (NTRU)-Based Fully Homomorphic Encryption (NTRU-FHE) Scheme encrypts sensitive data and ensures its integrity. A comparative study between the proposed method and current approaches is done through experimental verification utilizing Java. The results demonstrate that the proposed approach outperforms existing techniques, achieving an encryption time of 6.75 s for an input size of 75 and a decryption time of 5.128 s for the same input size. Similarly, the signature generation time is 125 ms for 100 received messages, block generation time of 10.8 s for 450 blocks, throughput of 98 MB/sec for a record size of 16,384, and total computational time of 403 ms for 20 messages. The results demonstrate the superior performance of the HCARS approach, with significantly reduced encryption, decryption, and signature generation times, as well as improved throughput and computational efficiency. Securing the security and privacy of cloud-based systems in the face of changing cyber threats has been made much easier with the help of the HCARS approach.

INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems. OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted. METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud. RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies. CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.

Cloud services provide an optimal form of demand based data outsourcing. The large amount of user data sharing increases the possibility of attacks, and unauthorized users get easy data access to the data. Blockchain technology provides better security in the cloud based on its distributed and highly cohesive nature. In order to enhance the block chain based encryption process, the second work intends to propose a blockchain based hybrid optimized cryptography scheme for secure cloud storage. At first, key generation is performed using the ECC approach in the cloud. In cloud user registration, keys and data are needed, and the cloud will provide the user ID. Then, the optimal key selection is performed by using flamingo search optimization (FSO). The public and the private key is selected by using this optimization approach. Afterwards, data encryption is performed using the Elgamal scheme on the owner side. This hybrid lightweight elliptic Elgamal based encryption (HLEEE) approach in key generation and data encryption process increases data security. After the authentication process, the cloud controller maintains the blockchain to protect the data and signatures of the users by generating the hash in blocks. An optimal hash generation is performed using the SHA-256 approach in the blockchain. The generated hash value, encrypted data and timestamp are stored in each block to provide more security. Finally, blockchain validation is performed using the proof of authority (PoA) approach.

In the last two decades machine learning and quantum computing have seen massive growth in terms of application and implementation. Quantum computers can provide the solution to certain classes of problem that classical computers fail to solve. Hence a greater number of optimized quantum algorithms are being developed. Today quantum computers are used in various fields to solve difficult problems like optimization and critical prediction problems in finance, artificial intelligence, and medicine. Quantum computing is based on the laws of quantum mechanics and uses superposition and entanglement properties to solve computation complexities in analyzing large data. Quantum machine learning is a new discipline for machine learning. In this survey, we will explore algorithms for quantum machine learning tasks. We will study parametrized quantum circuits for multi-class classification. This chapter also shows that quantum machine learning and near-term quantum devices open new doors for fast learning and enhanced classification.