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From black box AI to XAI in neuro-oncology: a survey on MRI-based tumor detection

Discover Artificial Intelligence 2025年第1期5卷 1-21页

作者： Asmita Mittal, Praveen Department of Computer Engineering and Application GLA University Uttar Pradesh Mathura Bharthia India

Brain tumor detection remains a critical focus in neuro-oncology, requiring precise and efficient diagnostic methods to support timely clinical decisions. Magnetic Resonance Imaging (MRI) is the modality of choice for this task, owing to its exceptional soft tissue contrast and spatial resolution. Recent advancements in deep transfer learning have driven transformative progress in automating tumor detection, achieving notable improvements in accuracy, scalability, and computational efficiency. This paper systematically reviews state-of-the-art transfer learning techniques, including pre-trained neural networks, domain adaptation approaches, fine-tuning methodologies, and ensemble learning frameworks. It also provides an in-depth analysis of widely used MRI datasets, highlighting persistent challenges such as data imbalance, overfitting, and variability across imaging domains. Emerging trends in model optimization, architectural innovation, and generalization strategies are discussed, offering a forward-looking perspective on the future of automated tumor detection. Through a detailed comparative evaluation, this review identifies the strengths and limitations of existing methods, while outlining potential directions for research aimed at developing robust, interpretable, and clinically deployable systems for brain tumor diagnosis. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Integrated fuzzy-based gamma adaptive for image enhancement with color attenuation prior

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Multimedia Tools and applications 2025年第12期84卷 11249-11267页

作者： Harne, Sachin Choubey, Siddhartha Choubey, Abha Department of Computer Application Shri Shankaracharya Group of Institution Chhattisgarh Bhilai India Department of Computer Science and Engineering Shri Shankaracharya Group of Institution Chhattisgarh Bhilai India

Images Quality in the Transportation industry is considered the most significant term and gains more attention among the higher authorities and researchers. Therefore, the accuracy level of the image has been evaluated with IQA (Image Quality Assessment). Most images are substantially affected by ambient lights that affect the quality of the image. In the existing research, the HVS (Human Visual System) has been utilized in order to identify the quality of images that occurred in both night and day time that affects the loss of luminance and information. In the current research, we proposed an integrated fuzzy-based Gamma Adaptive enhanced with Color Attenuation prior (IFGECAP) techniques that help capture the image's local characteristics. Eventually, the performance evaluation of the image whereas both input and output, has been evaluated with Natural image quality evaluator (NIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), structural similarity index measure (SSIM), Signal-to-noise ratio (SNR), and Peak signal-to-noise ratio (PSNR). Hence, the fog removal will be classified as seasonal, such as rainy and foggy, utilizing CNN—Convolutional Neural Network. The proposed hybrid technique—IFGECAP, procured an efficient result when compared with the existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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Automatic Image Acquisition and Matching in Control System of Near-Field Beam Shaping 8

Automatic Image Acquisition and Matching in Control System o...

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8th International Symposium on computer Science and Intelligent Control, ISCSIC 2024

作者： Li, Suicheng Shen, Wenwen He, Zizhou Zhou, Chen Li, Li Luo, Jun Institute of Computer Application Caep Department of Computer Engineering and Science Mianyang China

ISBN: (纸本)9798350380286

In the high power laser system of inertial Confinement fusion (ICF), in order to improve the safe operation flux of the system, near-field beam shaping is necessary to output the laser beam with uniform and smooth intensity distribution. Therefore, how to use optical control systems to automatically and efficiently accomplish near-field beamforming is an important issue. The optical control system based on liquid crystal spatial light modulator can control the beam shape and intensity distribution in real time. The control system of near-field beam shaping algorithm mainly includes the modules of light path control, image loading on liquid crystal spatial light modulator, Charge Coupled Device (CCD) nearfield light spot perception and image processing. There is a lot of data exchange between those modules. In the laser system, the manual operation mainly involves two parts: one is the interaction and data exchange between different modules. In the past work, the image data is collected by the operator on different devices, and then the compensation image is calculated on the computing device, and finally loaded into the display device. The whole process is complex, tedious and repetitive. The other is the image matching of input image on spatial light modulator and near-field light spot. This work requires staff to manually match feature points on computing equipment, and the accuracy of matching is difficult to guarantee, which is very time-consuming and labor-intensive. This paper will address the above problems and propose a distributed network architecture to connect different devices in near-field beam shaping and carry out image acquisition from them. In addition, a new image matching algorithm is proposed to extract and match the feature points of input image on spatial light modulator and near-field light spot. It enables the compensation algorithm to be completed automatically to avoid manual operation. © 2024 IEEE.

关键词： Light modulators

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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa

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International Journal of Applied Decision Sciences 2025年第2期18卷 168-188页

作者： Das, Madhusmita Dash, Rasmita Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In the second stage, loss and accuracy graphs analysis is done and in the third stage, the proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Lung cancer

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Proposing a digital twin-based sustainable water governance system for rural Indian villages

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

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Multimedia Tools and applications 2024年 1-31页

作者： Rout, Narendra Kumar Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT CG Raipur492010 India Department of Computer Science and Engineering MANIT M.P Bhopal462003 India

A content-based image retrieval system (CBIR) needs intensity/weighted importance for individual features of an image to find similar images in the database to achieve better results. Generally, these weights are assigned manually and are difficult to calculate as per the nature of the images. An experienced user may be able to retrieve images with higher accuracy by providing suitable weights for features in comparison to that of an inexperienced user. There is a need to develop a method that is independent of manual assignment of weights. To fulfil that need, an optimization-based pair-wise comparison approach has been developed for reducing the user intervention in the retrieval system and finding the optimal feature weight based on the nature of a benchmark COREL 10kimage database. The proposed work is implemented with different optimization techniques to optimize the pair-wise comparison method known as the Analytic hierarchy process (AHP). For optimization, algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Grey-wolf optimization (GWO), and Jaya algorithm (JAYA) have been used to get optimal weight for image feature. As per the results and observations, the AHP-PSO approach performs promising accuracy in terms of average precision and recall as compared to distinct optimized AHP approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer N

关键词： Molecules

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Deep Learning-Based Detection of Foliar Diseases in Apple Plants Using an Assembled CNN Model 3

Deep Learning-Based Detection of Foliar Diseases in Apple Pl...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sahu, Yatendra Bhargava, Arpita Thakur, Ghanshyam Singh Jain, Rishi IIIT Department of Computer Science and Engineering Bhopal India MANIT Department of Computer Application Bhopal India

ISBN: (纸本)9798350391565

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%. © 2024 IEEE.

关键词： Plant diseases

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