| 1. |
|
|
| 2. |
- Jannu, Srikanth, et al.
(författare)
-
Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things
- 2024
-
Ingår i: IEEE Transactions on Artificial Intelligence. - Piscataway : IEEE. - 2691-4581. ; 5:3, s. 1077-1086
-
Tidskriftsartikel (refereegranskat)abstract
- One of the most prominent research areas in information technology is the Internet of things (IoT) as its applications are widely used such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-the-art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics such as residual energy of the network, network lifetime, and the number of live IoT nodes. © 2022 IEEE
|
|
| 3. |
- Qu, Zhiguo, et al.
(författare)
-
QB-IMD : A secure medical data processing system with privacy protection based on quantum blockchain for IoMT
- 2024
-
Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:1, s. 40-49
-
Tidskriftsartikel (refereegranskat)abstract
- Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This paper innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy. © IEEE
|
|
| 4. |
- Qu, Zhiguo, et al.
(författare)
-
QMFND : A quantum multimodal fusion-based fake news detection model for social media
- 2024
-
Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 104
-
Tidskriftsartikel (refereegranskat)abstract
- Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. The effects of various parameters are further investigated. The QCNN not only has good expressibility and entangling capability but also has good robustness against quantum noise. The code is available at © 2023 Elsevier B.V.
|
|
| 5. |
- Siddiqui, Amna Jabbar, et al.
(författare)
-
Serum metallomics reveals insights into the associations of elements with the progression of preleukemic diseases toward acute leukemia
- 2024
-
Ingår i: Biology Methods and Protocols. - : Oxford University Press. - 2396-8923. ; 9:1
-
Tidskriftsartikel (refereegranskat)abstract
- Acute leukemia (AL) is a critical neoplasm of white blood cells with two main subtypes: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). This study is focused on understanding the association of the preleukemic disease aplastic anemia (APA) with ALL and AML at metallomic level, using healthy subjects as a control. In this study, a validated and efficient inductively coupled plasma-mass spectrometry/MS-based workflow was employed to profile a total of 13 metallomic features. The study encompassed 41 patients with AML, 62 patients with ALL, 46 patients with APA, and 55 age-matched healthy controls. The metallomic features consisted of eight essential elements (Ca, Co, Cu, Fe, Mg, Mn, Se, and Zn) and five non-essential/toxic elements (Ag, Cd, Cr, Ni, and Pb). Six out of the 13 elements were found to be substantially different (P < .05) using absolute concentrations between serum samples of AL (ALL and AML) and preleukemia (APA) patients in comparison with healthy subjects. Elements including magnesium, calcium, iron, copper, and zinc were upregulated and only one element (chromium) was downregulated in serum samples of disease when compared with healthy subjects. Through the utilization of both univariate tests and multivariate classification modeling, it was determined that chromium exhibited a progressive behavior among the studied elements. Specifically, chromium displayed a sequential upregulation from healthy individuals to preleukemic disease (APA), and ultimately in patients diagnosed with ALL. Overall, metallomic-based biomarkers may have the utility to predict the association of APA with ALL.
|
|
| 6. |
- Sufyan, Ali, et al.
(författare)
-
Monolayer TiC—A high-performance Dirac anode with ultralow diffusion barriers and high energy densities for Li-ion and Na-ion batteries
- 2024
-
Ingår i: Applied Surface Science. - : Elsevier B.V.. - 0169-4332 .- 1873-5584. ; 642
-
Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional Dirac materials have stimulated substantial research interest as binder-free anodes in metal-ion batteries, owing to their ultrahigh electronic conductivity, large specific area, and higher energy density. Here, using first-principles density functional theory calculations, we have investigated the feasibility of monolayer TiC as a potential anode material for Li/Na-ion batteries. The results indicate that monolayer TiC exhibits excellent dynamical and thermal stability. The electronic structure of monolayer TiC shows semimetallic characteristics with a Dirac cone at the M high symmetry point and the formation of Ti or C vacancies transforms the Dirac cone into a nodal loop or a nodal surface, respectively. Thus, monolayer TiC possesses superior electrical conductivity, which can be further enhanced by the formation of Ti or C vacancies in the material. Furthermore, the calculated adsorption energy values of -0.85 and -0.46 eV for Li-ion and Na-ion, respectively, indicate that Li/Na atom adsorption over monolayer TiC is a favorable process. The density of states plots show that after the adsorption of a single Li/Na atom, monolayer TiC maintains its metallic state, which is advantageous for the diffusion of stored electrons. Most remarkably, monolayer TiC exhibits energy densities of 2684 and 2015 mWh/g for Li and Na, respectively, which are significantly higher than commercial graphite and most other 2D anode materials. The fully loaded TiC anode exhibits excellent cycle stability with volume expansions as low as 0.13 and 0.11%, for Li and Na, respectively. Furthermore, an ultrafast diffusivity with low energy barriers of 0.02 and 0.10 eV is found in monolayer TiC for Li-ion and Na-ion, respectively, which suggests that it has an excellent charge/discharge capability. These exceptional properties make monolayer TiC an excellent candidate as an anode material for Li-ion and Na-ion batteries. Finally, SiC(111) has been proposed as a candidate substrate for monolayer TiC due to its minimal lattice mismatch.
|
|
| 7. |
- Sufyan, Ali, et al.
(författare)
-
V4C3 MXene: a Type-II Nodal Line Semimetal with Potential as High-Performing Anode Material for Mg-Ion Battery
- 2024
-
Ingår i: ChemSusChem. - : John Wiley & Sons. - 1864-5631 .- 1864-564X. ; 17:7
-
Tidskriftsartikel (refereegranskat)abstract
- We have used density functional theory simulations to explore the topological characteristics of a new MXene-like material, V4C3, and its oxide counterpart, assessing their potential as anode materials for Mg-ion batteries. Our research reveals that V4C3 monolayer is a topological type-II nodal line semimetal, protected by time reversal and spatial inversion symmetries. This type-II nodal line is marked by unique drumhead-like edge states that appear either inside or outside the loop circle, contingent upon the edge ending. Intriguingly, even with an increase in metallicity due to oxygen functionalization, the topological features of V4C3 remain intact. Consequently, the monolayer V4C3 has a topologically enhanced electrical conductivity that amplifies further upon oxygen functionalization. During the charging phase, a remarkable storage concentration led to a peak specific capacity of 894.73 mAh g−1 for V4C3, which only decreases to 789.33 mAh g−1 for V4C3O2. When compared to V2C, V4C3 displays a significantly lower specific capacity loss due to functionalization, demonstrating its superior electrochemical properties. Additionally, V4C3 and V4C3O2 exhibit moderate average open-circuit voltages (0.54 V for V4C3 and 0.58 V for V4C3O2) and energy barriers for intercalation migration (ranging between 0.29–0.63 eV), which are desirable for anode materials. Thus, our simulation results support V4C3 potential as an efficient anode material for Mg-ion batteries.
|
|
| 8. |
- Tiwari, Prayag, 1991-, et al.
(författare)
-
Quantum Fuzzy Neural Network for multimodal sentiment and sarcasm detection
- 2024
-
Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 103, s. 1-14
-
Tidskriftsartikel (refereegranskat)abstract
- Sentiment and sarcasm detection in social media contribute to assessing social opinion trends. Over the years, most artificial intelligence (AI) methods have relied on real values to characterize the sentimental and sarcastic features in language. These methods often overlook the complexity and uncertainty of sentimental and sarcastic elements in human language. Therefore, this paper proposes the Quantum Fuzzy Neural Network (QFNN), a multimodal fusion and multitask learning algorithm with a Seq2Seq structure that combines Classical and Quantum Neural Networks (QNN), and fuzzy logic. Complex numbers are used in the Fuzzifier to capture sentiment and sarcasm features, and QNN are used in the Defuzzifier to obtain the prediction. The experiments are conducted on classical computers by constructing quantum circuits in a simulated noisy environment. The results show that QFNN can outperform several recent methods in sarcasm and sentiment detection task on two datasets (Mustard and Memotion). Moreover, by assessing the fidelity of quantum circuits in a noisy environment, QFNN was found to have excellent robustness. The QFNN circuit also possesses expressible and entanglement capabilities, proving effective in various settings. Our code is available at https://github.com/prayagtiwari/QFNN. © 2023 Elsevier B.V.
|
|
| 9. |
|
|
