Debasis Gountia

The existing computer-aided design approaches for Digital Microfluidic Biochips focus on optimising execution time and electrode utilisation, but overlook security verification overhead as a synthesis constraint. The advanced detection method, based on ResNet-50 deep neural networks with 98% accuracy, requires three to four orders of magnitude more computational capacity (3.8 GFLOPS) than the embedded biochip controllers. Existing reactive recovery approaches suffer from prohibitive latency, with re-synthesis algorithms requiring seconds to 33 minutes on desktop processors, causing timing violations incompatible with real-time bioassay constraints. We present a security-aware computer-aided design framework, called FASP (Fault-Aware Synthesis and Partitioning), that integrates the computational cost of verification as a design-time synthesis constraint through hardware and software partitioning to enable effective placement of the detection method. The formulation of a multi-objective optimisation problem for security integration across diverse computing substrates is presented, along with a synthesis algorithm with O ( k · r · n log n ) complexity. The algorithm performs critical path analysis, selective module replication, and communication fabric synthesis to adapt the verification pipeline configuration to meet real-time deadlines and reliability requirements before deployment. Experimental results show this method yields an average verification success rate of 84.44% (over 5.59% better than the nearest baseline placements) across system scales from 4 to 324. The proposed method achieves synthesis times of 16–50 ms on average for redundancy levels 3–6, while an overall average latency of 128.86 ms enables the integration of verification directly into the design-time phase.

This research paper investigates the efficiency of cryptographic algorithms within graph-based encryption models such as star graph, focusing on their computational performance and security robustness. In this study, we analyze the performance of RSA and ElGamal cryptographic algorithms by evaluating time and space complexity across various file types, including text, image, audio, and data of different sizes. The encryption process is modeled using graph structures such as the Star graph, along with other well-known algorithms like A^*, Dijkstra, Bellman-Ford, and Floyd-Warshall for comparative analysis and performance benchmarking. Consequently, this research conducts a comparative analysis of RSA and ElGamal cryptographic algorithms by applying them to mixed data, including binary, text, and image files. The CPU's internal clock was employed to record the execution time of encryption and decryption operations, facilitating the assessment of time complexity for both algorithms. The CPU's internal memory was employed to monitor and record memory usage during the encryption and decryption operations performed on mixed datasets. Accordingly, the evaluation of the encryption algorithms was conducted using criteria such as encryption time, decryption time, and throughput to determine their relative performance. In evaluating cryptographic approaches, factors such as response time, confidentiality, bandwidth, and integrity are considered. Experimental results indicate that RSA demonstrates superior time efficiency and resource utilization, whereas the ElGamal algorithm exhibits greater memory efficiency and resourcefulness. This study evaluates RSA and ElGamal encryption on text, image, audio, and data files of varying sizes using graph-based models. The Star graph algorithm is adopted for its simplicity and low computational cost, and its performance is compared against A^*, Dijkstra, and Bellman-Ford algorithms. Results show that the Star model offers near-optimal paths with significantly reduced processing time, demonstrating high confidence in efficiency for lightweight encryption tasks. We have added computational performance, logical confidence, and optimality conditions of the proposed Star-based encryption model. The Star algorithm, integrated with RSA/ElGamal encryption, is benchmarked against classical pathfinding algorithms like A^*, Dijkstra, and Bellman-Ford, commonly used for routing and shortest-path computations. Computational performance of (i) the worst-case time complexity of star algorithm (proposed) is O(b^d) where b is the branching factor and d is the depth and space complexity is O(E + V) where E is the number of edges and V is the number of nodes, high (central node access) traversal efficiency, excellent (centralized graph encoding) suitability for graph-based encryption, very high structural simplicity, (ii) the worst case time complexity of A^* Algorithm is O(b^d) where b is the branching factor and d is the depth and space complexity is O(E + V) where E is the number of edges and V is the number of nodes, high (with good heuristic) traversal efficiency, good (needs proper graph abstraction) suitability for graph-based encryption, moderate structural simplicity, (iii) the average case time complexity and best case time complexity of Dijkstra's Algorithm is O((V + E) log V) and the worst case time complexity is O((V²) logV) and space complexity is O(V) where V is the number of vertices, moderate to high traversal efficiency, fair (efficient in weighted graphs) suitability for graph-based encryption, moderate structural simplicity, and (iv) Bellman-Ford Algorithm is O(V^*E) time complexity and O(V) space complexity where V is the number of vertices and E is the number of edges, low traversal efficiency, limited (computationally expensive) suitability for graph-based encryption, moderate structural simplicity.

The emerging technology of Lab-on-Chips has made a profound impact in the field of healthcare, biochemistry, and molecular biology involving key tasks such as clinical trials, drug therapy, DNA-sequencing, among other tasks. In particular, digital microfluidic biochips have found versatile applications because of the simplicity of operations and low operational cost. Unfortunately, the ease of programmability and controllability in these biochips open doors to severe infringement of privacy and security, which in turn jeopardizes the trustworthiness of bioprotocols. The insertion of Trojans in these biochips may result in deadly outcomes and thus, ensuring the security of biochips has become a major challenge so as to prevent malicious alterations, data theft, cybercrime, sabotage, breach of confidence, and vandalism. Conventional techniques for Trojan detection such as side-channel analysis, runtime scan, and reverse engineering suffer from many shortcomings that compromise time, cost, and the reliability of bioprotocols. In this paper, we present new countermeasures to protect digital microfluidic biochips against Trojan attacks based on image classification of the running bioprotocol. A deep-learning approach is employed to classify the observed snapshot sequences as good or infected. Our results on five real-life bioprotocols demonstrate that the proposed classifier achieves 98.52% accuracy while preserving the security and functionalities of the digital microfluidic biochip.

Correcting grammatical errors in various language contexts is a crucial and challenging task in the field of natural language processing, commonly referred to as Multilingual Grammatical Error Correction. This paper elaborates the Adversarial Temporal Graph Convolution Model (AT-GCM), which combines the capabilities of MT-5, adversarial learning, and temporal graph convolutional neural network (t-GCN) to achieve accurate progress in multilingual grammatical error correction. The inherent capability of MT-5 to process multiple languages simultaneously serves as a powerful embedding generator for the purpose of multilingual error correction. The t-GCN is employed for the purpose of navigating the temporal context and interdependencies present within words. The assumption that modeling the dynamic interactions among words within the context of temporal relationships improves precision, particularly in languages with complex sentence structures, is supported by research. The utilization of adversarial learning techniques can enhance the generalization capabilities of the model across various language pairings, effectively addressing the challenges associated with low-resource languages. A comprehensive analysis is carried out on a diverse, multilingual dataset comprising various languages, viz. English, Russian, German, Czech, Arabic, and Romanian. The experimental results present significant improvements in grammatical error correction performance compared to state-of-the-art models. Our approach effectively resolves grammatical errors in various linguistic contexts by utilizing a combination of MT-5, adversarial learning, and t-GCN.

Among all the modern technological advances, digital microfluidic biochip has been extending a salient solution to healthcare and bio-laboratories with the pledge of high sensitivity and reconfigurability. Such biochip devices fulfill the requirement of a faster testing kit for the detection of different novel diseases, which is indispensable in the market due to the tremendously disrupted scenario of healthcare systems. To eliminate erroneous testing, the current scope of digital microfluidic biochips is widened as a viable testing method using various bioprotocols with a reduced cost in developing countries. This paper addresses the existing security challenges and operational faults in the identification mechanism of proteins such as severe acute respiratory syndrome coronavirus 2 spike protein in state-of-the-art digital microfluidic biochips. We are the first to propose a safety detection solution along with a fault identification algorithm using an inductive transfer learning model. Experimental results of the proposed model register a threshold accuracy of 98% while applying the own dataset. This work will provide a better security-enabled fault-free safety assurance framework against attack and fault identification with better accuracy in digital microfluidic biochips for the detection of different diseases and many other healthcare diagnoses, without any overhead of completion time for bioprotocols.