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Security in e-healthcare applications such as Telemedicine is crucial in safeguarding patients' sensitive data during transmission. The proposed system measures the patient's health parameters, such as body temperature and pulse rate, using LM35 and pulse sensors, respectively. The sensor data and the patient's medical image are encrypted in the Raspberry Pi 3 B + processor using Python's proposed text and medical image encryption scheme. The encrypted data is transmitted via the Thing Speak cloud and received by another Raspberry Pi at the receiver to decrypt the cipher data. The flask webserver can view the decrypted data by the doctor at the other end. This IoT implementation of secure Electronic Health Record (EHR) transmission employs text and medical image encryption schemes using a Combined Chaotic System (CCS). The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. Then, selective shuffling between the cut-off points breaks the statistical relationship between the neighbouring pixels. Finally, the intra and inter-pixel diffusion is carried out using bit permutation and bit-wise XOR operation to create a highly random cipher image. The initial seed for inter-pixel diffusion is obtained from the hash of intra-pixel diffused images to resist chosen plain text and cipher text attacks. The efficiency of the developed medical image encryption algorithm is tested against various attack analyses. The results and the security analyses validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Cement ball mills in the finishing stage of the cement industries consume the highest energy in the cement manufacturing stage. Therefore, suitable controllers that result in good productivity and product quality with reduced energy consumption are required for the cement ball mill grinding process to increase the profit margins. In this study, generalised predictive controllers (GPC)have been designed for the cement ball mill grinding operation using the model obtained from the step response data taken from the industrially recognized simulator. The servo and regulatory responses are analysed with and without constraints by implementing the designed GPC under the closed loop. The error metrics for GPC and conventional controllers are also analysed. The designed GPC for the cement ball mill grinding process outperforms the traditional controller in error metrics. [ABSTRACT FROM AUTHOR]

In the present scenario of Nano communication technology, Quantum Cellular Automata (QCA) is a demanding Quantum concept to communicate information using quantum dots. This paper presents a novel QCA architecture employing majority gates in cross-coupled and cross-oriented structures in QCA designer platform. The selection of the structures in-turn depends on the minimum number of QCA cell usage. In the proposed methodology, an optimised Random Key Generator (RKG) has been constructed using QCA by employing a Linear Feedback Shift Register (LFSR) as a select input to the 2:1 MUX. The MUX generates a random key by selecting either Galois Ring Oscillators (GRO) or Fibonacci Ring Oscillators (FRO). Further, the proposed scheme reduces the Flip flop usage into EXOR gates and wire as compared to the conventional design. The clock circuitry used in the QCA platform would decide the initial seed to the FROs, GROs and the LFSR circuitry in QCA which makes the output Random Key Sequence truly unpredictable. The generated random numbers are used as a key to encode the pixels in the image which enhances the security. Further, the encoded image pixels are encrypted using Deoxyribo Nucleic Acid (DNA) encryption algorithm to render confusion, permutation and diffusion by adopting various rule sets. Image encryption metrics like correlation, entropy, Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) were computed. Also, power and parameter analysis of the proposed QCA were estimated. From the computed metrics, it is proven that the proposed Nanostructure can be the potential aspirant for the future secured Nano communication. [ABSTRACT FROM AUTHOR]

Pneumonia, a prevalent respiratory disease, affects millions globally. Accurate diagnosis and early detection are essential for managing and treating pneumonia. In recent years, machine learning and visual analysis technologies have shown promise for detecting pneumonia from therapeutic imageries such as chest X-rays. The dataset is collected from a Kaggle and contains X-ray scans of lungs from people of all ages. This dataset includes 5,856 labelled images, of which 4,273 are positive for pneumonia and 1,583 are negative. The data set is preprocessed using data augmentation techniques such as rotation, shifting, shearing, flipping and fill mode. The preprocessed data is trained using a masked neural network (MNN). The essential features are extracted from the last layer of MNN, and then the K-nearest neighbor (KNN) classify the chest X-rays to detect Pneumonia. This study developed a mask generation technique, dropout regularisation, and classifiers to train a model with 98.07% accuracy and minimal losses. This approach could lead to faster and more accurate pneumonia diagnoses, ultimately improving patient outcomes. Our research shows that transfer learning of KNN with MNN can effectively analyse chest X-rays to detect pneumonia. [ABSTRACT FROM AUTHOR]

Nowadays, digital medical images have become an essential source for the grand success of e-health technology. At the same time, the massive storage also plays a vital role. One of cloud storage's main objectives is the affordable and easily accessible storage of vast amounts of multi-structured data. The cloud paradigm gives an illusion of infinite storage of data. The future of Cyber-Physical Systems (CPS) relies upon technologies like cloud computing to thrive. However, the major lacuna is data security. This paper deals with the Confidentiality Integrity Availability (CIA) aspects required for cloud-based medical image repositories. Since it is for the medical image, the Region of Interest (RoI) is separated, and the integrity check is applied for RoI. A two-tier security for the medical image has been proposed, including an additional security layer for RoI. A 3-D Lorenz chaotic attractor has been used to generate the key where the keyspace is widely increased. Deoxyribonucleic Acid (DNA) based image diffusion in different stages of cryptosystem offered an average entropy of 7.98042 and a correlation of 0.002864 for RoI only and for ciphered medical image an average entropy of 7.99724 and a correlation of − 0.00063. Text encryption is performed over metadata to ensure the privacy of client authentication. Encrypted metadata and 320 bits have been generated for the RoI part embedded in an image's Non-Region of Interest (NRoI) part in the random pixel indexes obtained using a 1D Tent map. This proposed approach also gives a Graphical User Interface developed using Python 3.8 to support non-technical persons or medical practitioners. The proposed security framework provides a complete CIA triad for medical image repositories in the cloud. [ABSTRACT FROM AUTHOR]

To safeguard the content from malicious devices and users and to provide authentication and security to the transmitted content, it becomes mandatory to devise a combined watermarking and encryption algorithm. This paper addresses this concern by developing an algorithm to authenticate and encrypt the image before transmitting it safely. The algorithm was established using a watermarking technique. The watermarking was done by transforming the image into coefficients and embedding it in high-frequency components. The watermarked image was encrypted using a key generated using a combined logistical tent map and converted to DNA sequences. The sequences are stored and transmitted as such. At the receiver end, the image was recovered and de-watermarked. The label used for the watermark was compared with the obtained watermark at the receiver end. The proposed algorithm conforms to the required CIA triad (confidentiality, integrity, availability). The algorithm was tested for images in the Standard Test images database. On average, for an encrypted image, the peak signal to noise ratio (PSNR) was 9.1934 dB and had an entropy of 7.999 bits. The correlation coefficient of 0.0001, 0.0019, and 0.0003 for horizontal, vertical and diagonal directions was obtained. The watermark could be satisfactorily recovered from the cover, even with the addition of noise. The extracted watermark had a PSNR above 30 dB, with normalized cross-correlation (NCC) above 0.9, bit error rate (BER) below 0.1 and structural similarity index metric (SSIM) above 0.9. These results make the proposed algorithm suitable for authentication and encryption. [ABSTRACT FROM AUTHOR]

As an alternative to IC engine driven automobiles, electric vehicles are emerging as attractive means of transportation. Taking into account the different operating conditions of an automobile, viz. acceleration, coasting and regenerative braking, BLDC motor-based electric drive system is considered. A BLDC motor-based drive system has many advantages including independent variable speed and variable torque operation along with regeneration capability. In addition to the main battery to supply motive power, an auxiliary battery at lower voltage is normally provided for meeting loads like front and back lights, wiper control, window operations, interior lighting, music system etc. Hence it is possible to charge the auxiliary battery by recouping the kinetic energy of the automobile during braking interval through a DC–DC boost converter. In this paper, automobile dynamics including road friction, aerodynamic forces and transmission, are considered for calculating the shaft torque of the BLDC motor, while following a specified speed-time characteristic and designing a drive system and its controller for closely following the above profile. The problem is formulated by integrating all the above aspects and solved using multiple feedback loops and developing two alternate controllers, viz. MPC and PI controllers, to cover the above three modes of operation. A simulation schematic containing the above functional blocks as subsystems has been created and integrated for the simulation of the entire system, and the results are presented. The results indicate that auxiliary battery regeneration scheme makes significant energy capture possible. Experimental work on a laboratory set up consisting of a BLDC motor drive system, and ARM CORTEX M4F Microcontroller board has been carried out to validate the simulation results. [ABSTRACT FROM AUTHOR]

The general focus of this work is to design an area-optimised full adder and utilise it to lay out a low-power arithmetic unit that can be helpful for microprocessors. A traditional full adder with 28 transistors has been devised with 10 transistors of an equal amount of PMOS and NMOS, guaranteeing the proper switching activity. The proposed full adder has one XOR gate and two 2:1 multiplexers in which the XOR gate has been customised with 4 transistors using pass transistor logic (PTL). In contrast, the gate diffusion input (GDI) technique has been used to alter the multiplexer design. The combination of the GDI and PTL brings a novelty to the full adder circuit, through which the design required only 10 transistors to perform adding operations. The proposed full adder design has been constructed against ten different complementary metal oxide semiconductor processing technologies, namely 0.6 µm, 0.8 µm, 0.12 µm, 1.2 µm, 0.18 µm, 0.25 µm, 0.35 µm, 50 nm, 70 nm and 90 nm. Pre- and post-layout simulations evidence the accuracy of the results in which the design consumes 1.843 µW of power with 0.605 ns as the worst-case delay on 90 nm technology. Further, the full adder has been extended as an adder/subtractor unit of 4 bits, with the power delay product as 0.1285 × 10− 18 J for the critical delay of 1.095 ns. The proposed design has been compared against the various full and approximate adders. The full adder has a 12.99% power reduction over the existing low-power adder and a 58.4% power reduction over the 28 transistors. This ensures that the proposed adder outperforms the traditional design and the state of the artwork. [ABSTRACT FROM AUTHOR]

Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented preemptively through a host of proven cryptographical solutions. The presented work integrates adaptive key generation, neural-based confusion and non-XOR, namely DNA diffusion, which offers a more extensive and unique key, adaptive confusion and unpredictable diffusion algorithm. Only authenticated users can store this encrypted image in cloud storage. The proposed security framework uses logistics, tent maps and adaptive key generation modules. The adaptive key is generated using a multilayer and nonlinear neural network from every input plain image. The Hopfield neural network (HNN) is a recurrent temporal network that updates learning with every plain image. We have taken Amazon Web Services (AWS) and Simple Storage Service (S3) to store encrypted images. Using benchmark evolution metrics, the ability of image encryption is validated against brute force and statistical attacks, and encryption quality analysis is also made. Thus, it is proved that the proposed scheme is well suited for hosting cloud storage for secure images. [ABSTRACT FROM AUTHOR]

With the growing demand for digitalization, multimedia data transmission through wireless networks has become more prominent. These multimedia data include text, images, audio, and video. Therefore, a secure method is needed to modify them so that such images, even if intercepted, will not be interpreted accurately. Such encryption is proposed with a two-layer image encryption scheme involving bit-level encryption in the time-frequency domain. The top layer consists of a bit of plane slicing the image, and each plane is then scrambled using a chaotic map and encrypted with a key generated from the same chaotic map. Next, image segmentation, followed by a Lifting Wavelet Transform, is used to scramble and encrypt each segment's low-frequency components. Then, a chaotic hybrid map is used to scramble and encrypt the final layer. Multiple analyses were performed on the algorithm, and this proposed work achieved a maximum entropy of 7.99 and near zero correlation, evidencing the resistance towards statistical attacks. Further, the keyspace of the cryptosystem is greater than 2128, which can effectively resist a brute force attack. In addition, this algorithm requires only 2.1743 s to perform the encryption of a 256 × 256 sized 8-bit image on a host system with a Windows 10 operating system of 64-bit Intel(R) Core(TM) i5-7200U CPU at 2.5 GHz with 8 GB RAM. [ABSTRACT FROM AUTHOR]

The cement kiln is one of the major energy-intense processes that need efficient controllers to minimise fuel consumption, enhance clinker production, and improve cement quality. A significant volume of research has been reported on modelling and controlling the cement kiln process, making it an ongoing research problem. Hence, there is a need for a comprehensive review to provide insight into cement kiln control strategies, which have been presented in this work. A brief introduction to cement kiln operation is presented to understand the complexities and challenges of controller design. A literature survey on cement kiln modelling indicates both traditional and intelligent techniques. Box Jenkins and Elman-based neural network models are reported to capture cement kiln dynamics accurately. The process model, controller, and kiln control parameters reported in various literature are analysed and presented. It indicates the predominant use of Model Predictive Control (MPC) to control the Burning Zone Temperature (BZT) of the cement kiln. MPC is one of the APC technology which can accommodate practical constraints in the form of bounds and provide optimal control action making it the preferred controller for cement kilns. Receding horizons, MPC weights, and prediction models are the major parameters that govern MPC control performance. Various tuning strategies to tune these MPC parameters have been reported in the literature. [ABSTRACT FROM AUTHOR]

Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. In this work, robust respiratory disease classification using breathing sounds (RRDCBS) is implemented by extracting multiple features from sounds, creating multiple modelling techniques, and experimental identification of diseases using appropriate testing procedures for multi-class and binary classification of respiratory diseases. Decision level fusion of features for Vector quantisation (VQ) modelling technique has provided 100% accuracy for classifying five respiratory diseases and healthy subjects. Decision level fusion of indices on the features has provided 100% accuracy for VQ, support vector machine (SVM), and K-nearest neighbour (KNN) modelling techniques to perform binary classification of the respiratory disease against healthy data sound. Deep recurrent and convolutional neural networks are also evaluated for multiple/binary classification of respiratory diseases. [ABSTRACT FROM AUTHOR]

Current medical technology evolves massive reports such as electronic patient records and scanned medical images; such reports are needed to be stored securely for future references. Existing storage systems are not feasible for massive data storage. Fortunately, cloud storage services meet the demand through their properties such as scalability and availability. Cloud computing is encouraged by amazing web innovation and modern electronic contraptions. Medical images can be stored in the cloud area, but most of the cloud service providers keep the client data in the plain text format. Cloud users need to take the responsibility to preserve the medical data with their strategy. Most of the existing image encryption solutions are vulnerable to the chosen-plaintext attack because the increasing power of computers and ingenuity of hackers are opening up more and more cracks in this mathematical armour. This paper proposes Hopfield neural network (HNN)-influenced image encryption technique to withstand against various attacks which optimize and improvise system through continuous learning and updating. These methods provide a critical security feature that adapts itself for day-to-day miracles of the real world. In this scheme, the back propagation neural network has been employed to generate image-specific keys that increase the resiliency against hackers. The generated keys are used as an initial seed for confusion and diffusion sequence generation through HNN. [ABSTRACT FROM AUTHOR]

Smart healthcare systems play a vital role in the current era of Internet of Things (IoT) and Cyber-Physical Systems (CPS); i.e. Industry 4.0. Medical data security has become the integral part of smart hospital applications to ensure data privacy and patient data security. Usually, patient medical reports and diagnostic images are transferred to the specialist physician in other hospitals for effective diagnostics. Therefore, the transmission of medical data over the internet has attained significant interest among many researchers. The three main challenges associated with the e-healthcare systems are the following: (1) ensuring authentication of medical information; (2) transmission of medical image and patient health record (PHR) should not cause data mismatch/detachment; and (3) medical image should not be modified accidentally or intentionally as they are transmitted over the insecure medium. Thus, it is highly essential to ensure the integrity of the medical image, especially the region of interest (ROI) before taking any diagnostic decisions. Watermarking is a well-known technique used to overcome these challenges. The current research work has developed a watermarking algorithm to ensure integrity and authentication of the medical data and image. In this paper, a novel watermarking algorithm is designed based on Integer Wavelet Transform (IWT), combined chaotic map, recovery bit generation and SHA-256 to address the objective as mentioned earlier. The paper's significant contribution is divided into four phases, namely, watermark generation and data embedding phase, authentication phase, tamper detection phase and localisation and lossless recovery phase. Experiments are carried out to prove that the developed IWT-based data embedding scheme offers high robustness to the data embedded in region of non-interest (RONI), detects and localises the tampered blocks inside ROI with 100% accuracy and recovers the tampered segments of ROI with zero MSE. Further, a comparison is made with the state-of-art schemes to verify the sternness of the developed system. [ABSTRACT FROM AUTHOR]

In this growing era, a massive amount of digital electronic health records (EHRs) are transferred through the open network. EHRs are at risk of a myriad of security threats, to overcome such threats, encryption is a reliable technique to secure data. This paper addresses an encryption algorithm based on integer wavelet transform (IWT) blended with deoxyribo nucleic acid (DNA) and chaos to secure the digital medical images. The proposed work comprises of two phases, i.e. a two-stage shuffling phase and diffusion phase. The first stage of shuffling starts with initial block confusion followed by row and column shuffling of pixels as the second stage. The pixels of the shuffled image are circularly shifted bitwise at the first stage of diffusion to enhance the security of the system against differential attack. The second stage of diffusion operation is based on DNA coding and DNA XOR operations. The experimental analyses have been carried out with 100 DICOM test images of 16-bit depth to evaluate the strength of the algorithm against statistical and differential attacks. By the results, the maximum entropy has been obtained an average of 15.79, NPCR of 99.99, UACI of 33.31, and larger keyspace of 10140, which infer that our technique overwhelms various other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The recent advancement in multimedia technology has undoubtedly made the transmission of objects of information efficiently. Interestingly, images are the prominent and frequent representations communicated across the defence, social, private and aerospace networks. Image ciphering or image encryption is adopted as a secure medium of the confidential image. The utility of soft computing for encryption looks to offer an uncompromising impact in enhancing the metrics. Aligning with neural networks, a Hopfield attractor-based encryption scheme has proposed in this work. The parameter sensitivity, random similarity and learning ability have been instrumental in choosing this attractor for performing confusion and diffusion. The uniqueness of this scheme is the achievement of average entropy of 7.997, average correlation of 0.0047, average NPCR of 99.62 and UACI of 33.43 without using any other chaotic maps, thus proposing attack-resistant image encryption against attackable chaotic maps. [ABSTRACT FROM AUTHOR]

Medical images are widely used for diagnostic and therapeutic purposes during the detection of abnormalities in various organs of the human body. A huge number of medical images are handled every day by the hospitals as well as medical practitioners. Medical images need a strong mechanism for rightful patient identification and confidential sharing. The proposed work addresses both these issues through watermarking, encryption and hardware storage. In the proposed work, the important credentials of the patient such as their treatment history, ID and thumb impression are integrated in the form of a 256 × 256 watermark image. This watermark is embedded into the DICOM image of size 512 × 512 using Singular Value Decomposition on Integer Wavelet Transform domain. The watermarked DICOM image was further encrypted using Tent map and Lü chaotic attractors. Further, Stratix FPGA has been used to carry the bitstream format of encrypted images. The extracted watermark achieves a PSNR of 30 dB after subjecting to 17 different noise and image processing attacks on the encrypted image. For a payload of 0.25, the normal cross-correlation reached is 0.99. Keyspace of the encrypted DICOM image is 10136 achieving an average entropy of 15.68 and near-zero correlation. The proposed solution also overcomes the False Positive Problem. Various error metrics and statistical analyses have been performed to validate the robustness of the proposed secure medical image carrier. [ABSTRACT FROM AUTHOR]

Recently, the protection and transferring of medical images among peers in the established communication link has become a significant security threat. In this paper, to curtail the threats posed on the open communal channel, RNA diffused Quantum Chaos (RQC) encryption algorithm for colour Digital Imaging and Communications in Medicines (DICOM) image is proposed for the first time. It employs entropy estimation and updates it in the key stream generation and thereby avoids the limitation in traditional encryption schemes of scrambling the position of the pixels before diffusion. The proposed encryption scheme uses Novel Enhanced Quantum Representation (NEQR) and qubit arrangement to store the grayscale value of every pixel in the DICOM image. Using the key generated from the chaotic map, the image is diffusedusing the quantum Controlled-NOT(CNOT)gate. Further, to enrich the diffusion process, Deoxyribonucleic Acid (DNA) transcript Ribo Nucleic Acid (RNA) is used to diffuse the quantum bits in the image matrix with its self-complementary sequence generation. The diffused image is permuted by incorporating the circular shift operation. The efficiency of the proposed algorithm has been validated by using encryption quality metrics. [ABSTRACT FROM AUTHOR]

Image encryption is being employed as an important security provider to facilitate the communication of confidential images over various confidential networks. In this work, a RGB image encryption procedure based on Chaotic and Cellular Automata (CA) attractors is proposed. Lorenz, Lü and Rule 42 of CA have been used as encryption mediums in red, green and blue planes respectively. Besides scrambling and XORing operations on secret image, a random synthetic image has also been used for diffusion on the three planes. Cyclone II FPGA EP2C35F672C6 has been utilized to generate the low correlation yielding random synthetic image aided by beat frequency detection using PLLs and diffused bit generation process. The proposed approach satisfies the various statistical parameters and offers tangible resistance to differential, occlusion and chosen plain text attacks on RGB images. [ABSTRACT FROM AUTHOR]

Digital Imaging and Communications in Medicine (DICOM) is one among the significant formats used worldwide for the representation of medical images. Undoubtedly, medical-image security plays a crucial role in telemedicine applications. Merging encryption and watermarking in medical-image protection paves the way for enhancing the authentication and safer transmission over open channels. In this context, the present work on DICOM image encryption has employed a fuzzy chaotic map for encryption and the Discrete Wavelet Transform (DWT) for watermarking. The proposed approach overcomes the limitation of the Arnold transform—one of the most utilised confusion mechanisms in image ciphering. Various metrics have substantiated the effectiveness of the proposed medical-image encryption algorithm. [ABSTRACT FROM AUTHOR]

The scintillating technological advancements have redefined the process of communication around the world. Banking, purchases, investments, emails, bill payments, etc. are being managed through online communications and needless to mention the linkage of these internet serves with embedded gadgets. A microcontroller being the low-cost solutions for real-time embedded applications has to handle rigid security algorithms for information security paradigm. The high level of sensitivity in chaos-based systems is highly suitable for the design of encryption schemes due to the randomness offered by them. The minimal memory resource and speed are the factors restricting the use of microcontrollers for implementing chaotic schemes that encrypt image data. This paper presents the design of a chaos-based image encryption algorithm with lightweight properties and its optimised implementation on a 32-bit microcontroller. This work also includes parameters related to the analysis of the security level and performance of the microcontroller that was missed to concentrate by the authors on their similar schemes reported in the literature. The level of safety of the proposed algorithm has been analysed via key sensitivity analysis, encryption quality analysis, randomness analysis, differential analysis, statistical analysis, visual analysis and attack analysis. Additionally, the results of performance analysis regarding smaller memory footprint and better throughput of proposed algorithm guarantee its suitability for real-time embedded applications. [ABSTRACT FROM AUTHOR]

This paper presents a detailed security analysis of the research article on the digital image encryption scheme entitled "Reversible Logic Cryptography Design (RLCD) with Linear Feedback Shift Register (LFSR) key" (Karunamurthi S, and Natarajan VK, Microprocessors and Microsystems, 2019). Although the inadequate length of its 4-bit LFSR key makes the scheme extremely vulnerable to quick brute force attack, analyzing the various error metrics concerning the security of the encrypted images, this scheme provides statistically pleasing results. The major shortcoming identified on this RLCD-LFSR scheme is the traceable patterns that appear on its encrypted images due to the absence of confusion to break the pixels' correlation. In addition to the chosen plaintext attack, edge detection based cryptanalysis proposed in this paper to be sufficient to crack the RLCD-LFSR scheme. The enhancement made by the insertion of a confusion module in RLCD-LFSR scheme wipes out the perceptible patterns and edges from the encrypted images to resist the attacks. The failure of enhanced RLCD-LFSR under NIST tests confirms the flaws in the design of the Reversible Logic Gate (RLG) based diffusion process and its ineffectiveness for image encryption. Besides the security analysis, the performance of RLCD-LFSR scheme and the proposed improved version of the same is implemented on a 32-bit microcontroller to evaluate their suitability for real-time embedded applications. [ABSTRACT FROM AUTHOR]

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R 2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

With the onset of any pandemic, the medical image database is bound to increase. These medical images are prone to attack by hackers for their medical data and patient health information. To safeguard these medical images, a new algorithm is proposed. The algorithm involves secretly embedding the patient identification number into the medical image and encrypting the medical image, protecting the patient's identity and the patient's medical condition from hackers. The encryption algorithm involved a single stage of confusion and two stages of diffusion. The confusion operation was performed using the key generated by the Bülban map. The first stage of diffusion was done in the transform domain, using 5/3 transformation. The second diffusion stage was performed in the spatial domain by altering the pixel values using the key. The algorithm was tested on over 30 DICOM (Digital Imaging and Communications in Medicine) images taken from Open Science Framework (OSF), a public database for COVID-19 patients. The algorithm could resist the statistical attacks upon analysis, providing a PSNR of 7.084 dB and entropy of 15.9815 bits for the cipher image. The correlation coefficients for the cipher image were 0.0275, -0.0027, 0.018 in horizontal, vertical and diagonal directions. The keyspace was 2 ((M-1) ×N)×16 , with M the number of rows and N the number of columns in the image. The key sensitivity was high. The test results and metric analysis prove that the algorithm is an effective one for embedding and encryption., (© 2022. International Federation for Medical and Biological Engineering.)

Today's technological era, the booming desire for e-healthcare has inflated the attention towards the security of data from cyber attacks. As the digital medical images are transferred over the public network, there is a demand to shield an adequate level of protection. One of the prominent techniques is encryption which secures the medical images. This paper recommends a DICOM image encryption based upon chaotic attractors on frequency domain by integer wavelet transform (IWT) and fused with deoxyribonucleic acid (DNA) sequence on the spatial domain. The proposed algorithm uses a chaotic 3D Lorenz attractor and logistic map to generate pseudo-random keys for encryption. The algorithm involves subsequent stages, i.e. permutation, substitution, encoding, complementary and decoding. To endorse the resistance of the proposed algorithm, various analyses have been examined for 256 × 256 DICOM images by achieving an average entropy of 7.99, larger keyspace of 10 238 and non-zero correlation. The overall results confirm that the proposed algorithm is robust against the brute force attacks. Graphical abstract.

Growing demand for e-healthcare across the globe has raised concerns towards the secure and authentication enhanced medical image sharing. One of the services offered by health informatics in hospitals include an user interface through the Local Area Network (LAN) for enabling storage and access of medical records. In this paper, a security enhanced DICOM image sharing over a LAN addressing confidentiality, integrity and authentication has been proposed. Initially, the AES encrypted patient history was combined along with the thumb impression and Quick Response (QR) code of patient ID as watermark. This watermark was encrypted employing Integer Wavelet Transform (IWT), chaotic map and attractors with confusion-diffusion operations. Further, the encrypted watermark was embedded in the selected Region Of Non-Interest (RONI) pixels of DICOM image. Username & unique password credentials, Face identification and FPGA generated One Time Password (OTP) form the three layer authentication scheme for secure DICOM image access through the LAN. Web publishing medium of storing secured DICOM images in cloud has also been addressed in this work. To validate the proposed hybrid crypto-watermarking system, parameters such as key sensitivity, key space, correlation, entropy, histogram, cropping attack, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) were performed and the results obtained have proved the strength of the proposed algorithm against brute force, statistical and cropping attacks., (Copyright © 2018 Elsevier Inc. All rights reserved.)

In this technological era, it is highly essential to protect the digital medical data from the fraud and forgery as they are transmitted over the public channel. Also with the increased data traffic, it is hard to transmit the entire bulky medical data. New methods have come into the scene to reduce the traffic while maintaining the sufficient level of security. Partial encryption is one of the methods which selectively encrypt the bulky medical image. Meanwhile, if the same medical image is needed to be reused for another diagnosis, then it is recommended to protect the entire medical image. This paper proposes a hybrid encryption scheme based on deoxyribo nucleic acid and chaotic maps, which can be adaptable for both selective and full medical image encryption. The proposed algorithm uses multiple chaotic maps in single process to generate the highly random keys for encrypting the color digital imaging and communications in medicine image. The algorithm comprises three phases, namely, permutation, encoding, and diffusion. In all the phases, the selection of specific rule set depends on the key sequences produced from the combined chaotic system. Experimental results are carried out to validate the resistance of the developed algorithm toward statistical, differential, and brute force attacks.

This paper proposes a novel encryption scheme based on combining multiple chaotic maps to ensure the safe transmission of medical images. The proposed scheme uses three chaotic maps namely logistic, tent and sine maps. To achieve an efficient encryption, the proposed chao-cryptic system employs a bio-inspired crossover and mutation units to confuse and diffuse the Digital Imaging and Communications in Medicine (DICOM) image pixels. The crossover unit extensively permutes the image pixels row-wise and column-wise based on the chaotic key streams generated from the Combined Logistic-Tent (CLT) system. Prior to mutation, the pixels of the crossed over image are decomposed into two images with reduced bit depth. The decomposed images are then mutated by XOR operation with quantized chaotic sequences from Combined Logistic-Sine (CLS) system. In order to validate the sternness of the proposed algorithm, the developed chao-cryptic scheme is subjected to various security analyses such as statistical, differential, key space, key sensitivity, intentional cropping attack and chosen plaintext attack analyses. The experimental results prove the proposed DICOM cryptosystem has achieved a desirable amount of protection for real time medical image security applications., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

A reconfigurable hardware architecture for the implementation of integer wavelet transform (IWT) based adaptive random image steganography algorithm is proposed. The Haar-IWT was used to separate the subbands namely, LL, LH, HL, and HH, from 8 × 8 pixel blocks and the encrypted secret data is hidden in the LH, HL, and HH blocks using Moore and Hilbert space filling curve (SFC) scan patterns. Either Moore or Hilbert SFC was chosen for hiding the encrypted data in LH, HL, and HH coefficients, whichever produces the lowest mean square error (MSE) and the highest peak signal-to-noise ratio (PSNR). The fixated random walk's verdict of all blocks is registered which is nothing but the furtive key. Our system took 1.6 µs for embedding the data in coefficient blocks and consumed 34% of the logic elements, 22% of the dedicated logic register, and 2% of the embedded multiplier on Cyclone II field programmable gate array (FPGA).

Modern day information age is enriched with the advanced network communication expertise but unfortunately at the same time encounters infinite security issues when dealing with secret and/or private information. The storage and transmission of the secret information become highly essential and have led to a deluge of research in this field. In this paper, an optimistic effort has been taken to combine graceful graph along with integer wavelet transform (IWT) to implement random image steganography for secure communication. The implementation part begins with the conversion of cover image into wavelet coefficients through IWT and is followed by embedding secret image in the randomly selected coefficients through graph theory. Finally stegoimage is obtained by applying inverse IWT. This method provides a maximum of 44 dB peak signal to noise ratio (PSNR) for 266646 bits. Thus, the proposed method gives high imperceptibility through high PSNR value and high embedding capacity in the cover image due to adaptive embedding scheme and high robustness against blind attack through graph theoretic random selection of coefficients.