bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibody–Exatecan Conjugates with a Novel Self-immolative Moiety Overcome Resistance in Colon and Lung Cancer
Authors: Weng Weining , Meng Tao , Zhao Qianqian , Shen Yi , Fu Guoxiang , Shi Jing , Zhang Yue , Wang Zhaohui , Wang Mingqiao , Pan Rong , Ma Linjie , Chen Caiwei , Wang Lijun , Zhou Biao , Zhang Hui , Pu Junyi , Zhang Jianjian , Hu Yi Peter , Hua Guoqiang , Qian Yu , Liu Shu-Hui , Hu Wenhao , Meng Xun
Published: January 2023

Antibody–drug conjugates (ADC) using DNA topoisomerase I inhibitor DXd/SN-38 have transformed cancer treatment, yet more effective ADCs are needed for overcoming resistance. We have designed an ADC class using a novel self-immolative T moiety for traceless conjugation and release of exatecan, a more potent topoisomerase I inhibitor with less sensitivity to multidrug resistance (MDR). Characterized by enhanced therapeutic indices, higher stability, and improved intratumoral pharmacodynamic response, antibody–T moiety–exatecan conjugates targeting HER2, HER3, and TROP2 overcome the intrinsic or treatment resistance of equivalent DXd/SN-38 ADCs in low-target-expression, large, and MDR+ tumors. T moiety–exatecan ADCs display durable antitumor activity in patient-derived xenograft and organoid models representative of unmet clinical needs, including EGFR ex19del/T790M/C797S triple-mutation lung cancer and BRAF/KRAS–TP53 double-mutant colon cancer, and show synergy with PARP/ATR inhibitor and anti–PD-1 treatment. High tolerability of the T moiety–exatecan ADC class in nonhuman primates supports its potential to expand the responding patient population and tumor types beyond current ADCs.

Significance:

ADCs combining a novel self-immolative moiety and topoisomerase I inhibitor exatecan as payload show deep and durable response in low-target-expressing and MDR+ tumors resistant to DXd/SN-38 ADCs without increasing toxicity. This new class of ADCs has the potential to benefit an additional patient population beyond current options.

See related commentary by Gupta et al., p. 817.

This article is highlighted in the In This Issue feature, p. 799
Microinjury-Induced Tumor Necrosis Factor-α Surge Stimulates Hair Regeneration in Mice
Authors: Jin Guang-Ri , Hwang Su Bin , Park Hyeon Ju , Lee Bog-Hieu , Boisvert William A.
Published: January 2023

Introduction: Tumor necrosis factor (TNF)-α released after follicular injury such as that caused by plucking plays a role in the activation of hair regeneration. Microneedle (MN) treatment is applied to the scalp to increase permeability and facilitate the delivery of any number of compounds. Because the MN treatment causes injury to the epidermis, albeit minor, we reasoned that this treatment would lead to a temporary TNF-α surge and thereby promote hair regeneration. Methods: To investigate the effects of MN-treatment-induced microinjury and TNF-α on hair growth, we used C57BL/6N mice which were divided into six experimental groups: three groups of 1) negative control (NC), 2) plucking positive control (PK), and 3) MN therapy system (MTS) mice; and three groups identical to above were treated with a TNF-α blocker for 3 weeks: 4) NCB, 5) PKB, and 6) MTSB group. Results: After injury, TNF-α surge occurred on day 3 in the PK group and on day 6 in the MTS group. Wnt proteins and VEGF expression were markedly increased in the PK group on day 3 and on day 6 in the MTS group compared to the NC group. Following wound healing, only MTS and PK groups displayed thickened epidermis and longer HF length. Within the 2 weeks following treatment, the rate of hair growth was much slower in the injured mice treated with the TNF-α blocker. Conclusion: Our findings indicate that microinjury stimulates the wound-healing mechanism via TNF-α/Wnt/VEGF surge to induce hair growth, and that blocking TNF-α inhibits this growth process.
Short-Term and Long-Term Morbidity after Radical Cystectomy in Patients with NMIBC and Comparison with MIBC: Identifying Risk Factors for Severe Short-Term Complications
Authors: Haas Maximilian , Knobloch Charlotte , Mayr Roman , Gierth Michael , Pickl Christoph , Engelmann Simon , Denzinger Stefan , Burger Maximilian , Breyer Johannes , Holbach Sonja
Published: January 2023

Introduction: RC represents a viable treatment option for certain NMIBC patients. However, studies investigating morbidity in the context of RC for NMIBC are scarce. The goal of the current study was to assess and compare morbidity after RC performed in patients with NMIBC and patients with MIBC and to identify risk factors for severe short-term complications. Methods: Medical records of 521 patients who underwent RC for bladder cancer were retrospectively reviewed. Patients were divided into patients with NMIBC and patients with MIBC. The groups were compared and risk factors for severe complications were identified. Results: RC for NMIBC was performed in 123 patients (23.6%). Histological upstaging was seen in 47 NMIBC patients (38.2%) and in 231 MIBC patients (58%, p < 0.001). OS was 29.8% and CSS was 15.5%. Both endpoints were higher for RC for MIBC (p < 0.001). More complications affecting the urinary diversion were seen with RC for NMIBC (p = 0.033) and more continent urinary diversions (p = 0.040) were performed in those patients. Obesity (p = 0.008), a higher ASA score (p = 0.004), and preoperative medical drug anticoagulation (p = 0.025) were risk factors for severe short-term morbidity after both, RC for NMIBC and for MIBC. Conclusion: Patients who underwent RC for NMIBC are exposed to a comparably high perioperative risk than patients with MIBC. RC seems to be a viable treatment option for certain NMIBC patients with a significant histological upstaging in both groups. In patients with obesity, a high ASA score, and with medical drug anticoagulation, the indication for surgery should be confirmed especially strict and possible treatment alternatives should be considered particularly thorough.
New Frontiers in Sepsis-Induced Acute Kidney Injury and Blood Purification Therapies: The Role of Polymethylmethacrylate Membrane Hemofilter
Authors: Stasi Alessandra , Franzin Rossana , Caggiano Gianvito , Losapio Rosa , Fiorentino Marco , Alfieri Carlo , Gesualdo Loreto , Stallone Giovanni , Castellano Giuseppe
Published: January 2023

Acute kidney injury (AKI) is a common consequence of sepsis with a mortality rate of up to 40%. The pathogenesis of septic AKI is complex and involves several mechanisms leading to exacerbated inflammatory response associated with renal injury. A large body of evidence suggests that inflammation is tightly linked to AKI through bidirectional interaction between renal and immune cells. Preclinical data from our and other laboratories have identified in complement system activation a crucial mediator of AKI. Partial recovery following AKI could lead to long-term consequences that predispose to chronic dysfunction and may also accelerate the progression of preexisting chronic kidney disease. Recent findings have revealed striking morphological and functional changes in renal parenchymal cells induced by mitochondrial dysfunction, cell cycle arrest via the activation of signaling pathways involved in aging process, microvascular rarefaction, and early fibrosis. Although major advances have been made in our understanding of the pathophysiology of AKI, there are no available preventive and therapeutic strategies in this field. The identification of ideal clinical biomarkers for AKI enables prompt and effective therapeutic strategy that could prevent the progression of renal injury and promote repair process. Therefore, the use of novel biomarkers associated with clinical and functional criteria could provide early interventions and better outcome. Several new drugs for AKI are currently being investigated; however, the complexity of this disease might explain the failure of pharmacological intervention targeting just one of the many systems involved. The hypothesis that blood purification could improve the outcome of septic AKI has attracted much attention. New relevant findings on the role of polymethylmethacrylate-based continuous veno-venous hemofiltration in septic AKI have been reported. Herein, we provide a comprehensive literature review on advances in the pathophysiology of septic AKI and potential therapeutic approaches in this field.
Langfristentwicklungen in der Kinder- und Jugendrheumatologie
Authors: Haas Johannes-Peter , Minden Kirsten
Published: January 2023

Im Gebiet der Kinderrheumatologie gab es in den letzten Jahrzehnten immense Fortschritte, die sowohl die Diagnostik, als auch die Therapie nachhaltig verbessert haben. Obwohl erst seit 2003 in Deutschland offiziell als Zusatzbezeichnung anerkannt, stehen heutzutage über 200 Kinder- und Jugendrheumatologen (d.h. 1,4 Kinderrheumatologen pro 100 000 Kinder) für die Erkennung und Behandlung von rheumatischen Erkrankungen bei Kindern und Jugendlichen bundesweit zur Verfügung. Neue Erkenntnisse in der Pathogenese rheumatischer Erkrankungen und die sich stetig weiterentwickelnde genetische Diagnostik haben das rheumatische Krankheitsspektrum und die Behandlungsmöglichkeiten dramatisch erweitert. Internationale Forschungsnetzwerke und eine spezielle Gesetzgebung für die Entwicklung von pädiatrischen Medikamenten führten zur Zulassung von zahlreichen neuen Rheumamedikamenten, deren Sicherheit im klinischen Alltag seit der Jahrtausendwende systematisch in Deutschland untersucht wird. Maßnahmen zur Sicherung der Versorgungsqualität wurden implementiert, Standardinstrumente zur Bewertung der Krankheitsaktivität und Krankheitslast aus Patientensicht eingeführt sowie Initiativen zur Verbesserung der Versorgung Betroffener (z.B. die ProKind-Initiative) auf den Weg gebracht. Diese Veränderungen haben die Prognose und Lebensperspektive rheumakranker Kinder und Jugendlicher verbessert, wenngleich noch weiterer Optimierungsbedarf besteht.
Alopecia areata imitiert die frontale fibrosierende Alopezie
Authors: de Carvalho Lara Trindade , Izhakoff Nicole , Meah Nekma , Sinclair Rodney
Published: January 2023

Alopecia areata (AA) ist eine autoimmune, nicht vernarbende Alopezie mit unterschiedlichen klinischen Mustern, am häufigsten eine fleckenhaft auftretende Veränderung. Wir berichten über einen Fall von AA mit ungewöhnlicher Beteiligung des frontalen Haaransatzes, ähnlich der frontalen fibrosierenden Alopezie (FFA), einer Form der vernarbenden Alopezie. Dermatoskopische Befunde und das Ansprechen auf die Behandlung favorisierten die Diagnose von AA. Diese Ergebnisse unterstreichen die große Rolle der Trichoskopie sowie die Bedeutung der Einbeziehung von AA in die Differenzialdiagnose für FFA.
Assessment of Periodontal Status in Patients with Depression: A Cross-Sectional Study
Authors: Kaushik Aparna , Tanwar Nishi , Tewari Shikha , Sharma Rajinder Kumar , Jangid Purushottam
Published: January 2023

Objective: Depression leads to behavioral and systemic changes making individuals more susceptible to inflammatory diseases. This study was conducted to assess the periodontal status of patients with clinically diagnosed mild and moderate depression. Subjects and Methods: This cross-sectional study included 135 participants. Test group 1 (n = 45) consisted of patients clinically diagnosed with mild depression, test group 2 (n = 45) included patients with moderate depression and the control group (n = 45) included non-depressive participants. Sociodemographic characteristics and periodontal parameters were recorded. Results: Plaque levels and gingival inflammation were significantly (p < 0.05) higher in mild and moderate depression patients than in controls. Significant greater number of sites with bleeding on probing, increasing probing pocket depth (PPD), sites with PPD 4–5 mm, ≥6 mm, attachment loss 3–4 mm, and high prevalence with grade C periodontitis were observed in moderate depression patients, compared to patients with mild depression and healthy controls. On applying partial correlation, periodontal parameters were positively correlated with depression, while a negative correlation was found with income status. On regression analysis, bleeding on probing as a dependent variable was also associated with depression. Conclusions: Patients with moderate depression showed high periodontal destruction and inflammation as compared to those with mild depression. Further, deep pockets were associated with depressive patients. Periodontal care is required in such patients so that the progression of periodontal diseases can be prevented at the earliest.
Maternal Immune Activation by Polyinosinic-Polycytidylic Acid Exposure Causes Cerebral Cortical Dysgenesis through Dysregulated Cell Cycle Kinetics of Neural Stem/Progenitor Cells
Authors: Sasaki Marie , Mitsuhashi Takayuki , Goto Fumiko , Shibata Shinsuke , Kubo Ken-ichiro , Oku Shinju , Owashi Akihiro , Takahashi Takao
Published: January 2023

Maternal immune activation reportedly causes dysregulation of the cell cycle in stem cells and impairment of higher cortical function in rodents. Furthermore, in humans, maternal immune activation during the first to second trimester of pregnancy is strongly correlated with increased incidence of autism spectrum disorder in the offspring. Here, we show that in utero exposure to polyinosinic-polycytidylic acid (poly (I:C)) in mice during the early phase of neuronogenesis increases the probability of differentiation (quiescent fraction [Q fraction]) of neural stem/progenitor cells (NSPCs) without change in the length of cell cycle. This abnormal increase in the Q fraction is assumed to reduce the peak population size of NSPCs, resulting in the thinning of the neocortex in offspring because of the reduced production of neurons. Furthermore, the neocortex of poly (I:C)-exposed mice does not exhibit a layer-specific reduction in radial thickness, possibly because of increased apoptosis caused by poly (I:C) exposure during all stages of cortical development. These results suggest that maternal immune activation by poly (I:C) exposure may affect neocortical histogenesis by altering the cell cycle kinetics of NSPCs. In addition, the timing and amount of poly (I:C) exposure during pregnancy may have profound effects on cerebral cortical histogenesis.
Genetic Study in Pheochromocytoma: Is It Possible to Stratify the Risk of Hereditary Pheochromocytoma?
Authors: Araujo-Castro Marta , Mínguez Ojeda César , García Sanz Iñigo , Calatayud Maria , Hanzu Felicia , Mora Mireia , Vicente Almudena , Blanco Carrera Concepción , de Miguel Novoa Paz , López García María del Carmen , Lamas Cristina , Manjón-Miguélez Laura , del Castillo Tous María , Rodríguez de Vera Pablo , Barahona San Millán Rebeca , Recasens Mónica , Tomé Fernández-Ladreda Mariana , Valdés Nuria , Gracia Gimeno Paola , Robles Lazaro Cristina , Michalopoulou Theodora , Parra Ramírez Paola , Marazuela Mónica , Álvarez Escolá Cristina , García Centeno Rogelio
Published: January 2023

Introduction: It is estimated that 30–40% of patients with apparently sporadic pheochromocytomas (PHEOs) have an inherited predisposition syndrome. The aim of our study was to develop a predictive model of hereditary PHEO based on the clinical, hormonal, and radiological features present at the diagnosis of patients with PHEOs. Methods: A retrospective multicenter cohort study of patients with PHEOs with available genetic study from 18 tertiary hospitals. Clinical, biochemical, and radiological features were used to build a multivariate logistic regression model. The estimation of all possible equations was used to select the model with the best diagnostic accuracy (lower Akaike index). Results: A total of 245 patients were included: 169 (69.0%) patients with sporadic PHEOs and 76 (31%) with hereditary PHEOs. The parsimonious predictive model with the highest diagnostic accuracy for the prediction of hereditary PHEO combined the variables age, non-cardiovascular disease, urinary norepinephrine levels, and tumor size. The area under the ROC curve of this model was 0.800 (0.705–0.887). Based on the predictive model, the probability of hereditary PHEO in patients older than 60 years with cardiovascular disease, high levels of urinary norepinephrine and unilateral PHEOs >60 mm was <2%. And if the age was above 80 years, lower than 1%. The probability of sporadic PHEO linearly increased with age (MH Test for linear Trend: χ2 (1) = 30.05; p < 0.001). Conclusion: In certain populations such as old patients with cardiovascular disease, with high levels of urinary norepinephrine and large tumors in which the probability of hereditary PHEO is very low, genetic testing could be avoided in the absence of specific suspicion.
Post-Traumatic Hypopituitarism: A Neglected Consequence of Traumatic Brain Injury
Authors: Kałas Maria , Miksiewicz Marta , Kowalke Adrianna , Siemiński Mariusz
Published: January 2023

Post-traumatic hypopituitarism (PTHP), described for the first time in 1918, used to be neglected and only considered a rare consequence of traumatic brain injury (TBI). The research conducted in the past 20 years, however, elucidated that it has been significantly underestimated. A PubMed search was conducted in order to find literature on the topic of PTHP. Efforts were made to identify the wide point of view on this problem, from the historical perspective to the most recent data. The pathogenesis of PTHP is heterogenous, and various hypotheses concerning the etiology of this condition have been proposed. Unrecognized and untreated PTHP has negative socioeconomic consequences and influences the quality of life. Although a few attempts to create a screening algorithm have already been performed, there is still no clear answer regarding follow-up. The prevalence of PTHP, a rare consequence of TBI, has been significantly underestimated in the past 20 years. The issue is multifaceted so in order to make reliable guidelines a collaboration of specialists from different fields is required. Due to the increasing prevalence of TBI, and because patients after initial treatment at emergency department usually remain under control of a neurologist, both neurologists and emergency medicine specialists should be aware of clinical picture and mechanisms of PTHP.
Protection coordination analysis applied at biogas power generation plant
Authors: Siregar Yulianta , Tjumar Wiwanto , Mubarakah Naemah , Dinzi Riswan
Published: January 2023

Biogas from liquid waste from palm oil processing, palm oil mill effluent (POME), can be utilized in biogas power plants as a source of renewable energy (PLTBg). The PLTBg electrical system is equipped with a coordinated protection system. Then, the protection system must also maintain the continuity of electrical service in parts that are not affected by disturbances. Coordination of the protection system is essential. In this research, the electrical transient analysis program (ETAP) carries out the short circuit current analysis, and the coordination of overcurrent protection is constructed from its inverse-definite minimum time characteristics. The analyzed data contributed to selecting the right protection devices. A combination of overcurrent protection, directional protection, and frequency protection change rate supported a reliable electrical power system for a biogas power generation plant as distributed generation. The result shows that modern microprocessor-based protection relays support several protection features in one device and can be integrated into a supervisory control and data acquisition (SCADA)-controlled protection system to enhance their capabilities.
Quality of performance evaluation of ten machine learning algorithms in classifying thirteen types of apple fruits
Authors: Hassan Nashaat M. Hussain , Ramadan Gamal Elshoky Basma , M. Mabrouk A. M.
Published: January 2023

Recently, computer vision technology has become essential for the automatic, accurate, and fast classification of fruits. Actually, there are many challenges in separating the types of fruits that are somewhat similar, such as apples, pears, and peaches. However, the challenges become more difficult if the separation is on different varieties of the same fruit. While the difficulty doubles if the classification takes place with a large number of different varieties of the same fruit. Most of the literature which is presented in this regard, and which is relied on the use of machine learning techniques lacked the following: first; the focus was on certain technologies such as k-nearest neighbor (KNN), support vector machine (SVM) without looking at many other machine learning techniques. Second; the literature was concerned only with measuring the accuracy of the techniques that are used, without looking at the relationship between the accuracy and processing speed (computation times). This manuscript aims to study and analyze the results of measuring accuracy and computation times for ten machine-learning techniques in order to identify and classify thirteen types of apples. After studying and analyzing the results, many observations were made, which will be referred to in the results section.
Enhancing the security of quality of service-oriented distributed routing protocol for hybrid wireless network
Authors: Husam Mahdi Miaad , Adel Ibrahim Ibrahim
Published: January 2023

Merging the wireless infrastructure network with the wireless mobile ad-hoc networks constitutes hybrid wireless networks (HWNs). Quality of service (QoS) demands are available with the help of HWNs. However, these networks are subjected to many types of attacks because of their open wireless medium. To enhance the security of HWNs, it is necessary to provide secure routing protocols. Several routing protocols have been proposed for HWNs, one of them is the quality of service-oriented distributed (QOD) routing protocol. In this paper, two security mechanisms have been proposed for the QOD protocol. The first mechanism is used to protect transmitted data in the network using asymmetric and symmetric cryptography. The second mechanism has been proposed to enhance the security of the QOD routing protocol using keyed hash message authentication code (HMAC). The second security mechanism assumed that there is a secret key shared between each pair of neighbor nodes. Also, asymmetric cryptography is used to exchange the secret key. The secret key is used to include the message authentication code (MAC) for each message exchanged between the neighbor nodes. A network simulator NS2 is used to simulate our proposed schemes.
Dynamic source routing protocol with transmission control and user datagram protocols
Authors: Thuneibat Saed , Al Sharaa Buthayna
Published: January 2023

Dynamic source routing protocol (DSR) is a common routing protocol in wireless network without infrastructure, called ad-hoc network, DSR used just above internet protocol (IP) at the network layer. The upper transport layer provides reliability by transmission control protocol (TCP) and user datagram protocol (UDP). The choice between DSR/TCP and DSR/UDP is an actual issue for network designers and engineers. The question arises: which one provides better quality of service (QoS) parameters, less delay and jitter, greater throughput, and data rates. This paper focuses on the study and analysis of DSR and comparison of DSR/TCP and DSR/UDP by simulation in network simulator (NS2) environment. Another comparison of DSR and ad hoc on-demand distance vector (AODV) is provided. Design and simulation of the protocols in ad hoc network accurately describe the behavior in real system and QoS parameters are obtained.
Prototype and monitoring system of phasor measurement unit based on the internet of things
Authors: Sulistyowati Riny , Agus Sujono Hari , Chandra Riawan Dedet , Seto Wibowo Rony , Ashari Mochamad
Published: January 2023

This research resulting the method to reduce phasor measurement unit (PMU) amount and optimization of PMU replacement using a combination of Integer linear k-means. The first step of modeling is using a lot of PMUs that are optimized at Bendul Merisi network using integer linear k-means clustering for achieving an optimum solution of amount and replacement of PMU to be installed. The second step is estimating the uninstalled bus's power and voltage. PMU is using modified adaptive neuro-fuzzy inference system (ANFIS) of hybrid particle swarm optimization (PSO)-genetic algorithm (GA). The third step is to test and simulate the hardware design of the research for offline and online data. Research also tested network transmission of Java–Bali 500 kV. Designed simulation can calculate the active and reactive power of each bus in clusters so the total active and reactive power of each cluster can be known. Device tests to transmit data using internet of things (IoT) from a laboratory scale during 7 days have an average of 2.8 seconds while the field test required an average of 10.416 seconds during 24 hours.
Performance analysis of coded MIMO OFDM with subcarrier power modulation
Authors: Naji Fadhel Amneen , Jassim Harbi Yahya
Published: January 2023

This study investigates a new technique known as "orthogonal frequency division multiplexing with subcarrier power modulation" (OFDM-SPM) when compounded with a "multiple-input multiple-output" (MIMO) system. This combination of the two wireless technologies offers additional benefits and characteristics, such as increased spectrum efficiency (SE), increased energy efficiency (EE), and decreased transmission latency, among others. However, as compared to a traditional MIMO-OFDM system, the system's bit error rate (BER) show degradation. In this paper, turbo code is proposed as a coding technique to enhance the system performance with three-power reallocation policies (PRPs). The simulation results show that the BER and throughput performance has gotten better, where the throughput is doubled and the BER achieves about 10 dB gain by utilizing the proposed methods and compare to the classical MIMO OFDM, which makes the system more suitable for use in future wireless systems.
A study on high dimensional big data using predictive data analytics model
Authors: Krishnadoss Nivethitha , Kumar Ramasamy Lokesh
Published: January 2023

A massive bulk of data is being created due to digitalisation in various industries, including medical, manufacturing, sales, internet of things (IoT) devices, the web, and businesses. To find data patterns for data attributes machine learning (ML) algorithms are used. In this fast-growing world, we can see that data is generated in abundance by people, machines, and corporations. With the increase in computer science market, researchers are integrating heterogeneous and diverse data into accurate patterns by applying machine learning algorithms and complex strategies on data sets. The overabundance of high-dimensional big data has made it more difficult for scientists to extract important information from these data efficiently. Conventional data mining approaches are ineffective when dealing with large amounts of data. As big data increase exponentially, predictive analytics has become widely known. To evaluate a large number of data patterns, data driven technology predictive big data analytics (PBA) can be used and ML algorithms to investigate the present and future data based on the records of data patterns. In this research paper, predictive analysis on big data has been proposed using the splitting random forest (SRF) methodology with help of hyperparameter optimization and dimension reduction technique.
Biometric face recognition method using graphics processing unit system
Authors: A. Mohamad Alshiha Abeer , Wajid Al-Neama Mohammed , R. Qubaa Abdalrahman
Published: January 2023

The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.
Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin
Authors: Ramdhani Yudi , Apra Dhia Fauziah , Alamsyah Doni Purnama
Published: January 2023

Grapes are one of the fruit plants that grow that propagate in certain fields. Grapes can be processed into juice, wine, raisins, and so on. Raisins are dried grapes. Raisins have a distinctive taste and aroma. Raisins are a concentrated and nutritious source of carbohydrates, containing antioxidants, potassium, fiber and iron. To increase the accuracy value, the optimize selection genetic algorithm (GA) is used. This research was conducted modeling using the support vector machine (SVM) and SVM algorithms based on optimize selection GA by using the raisin (raisin varieties) dataset obtained from the UCI machine learning repository. The research dataset is divided into training data and testing data. The data sharing will be carried out using the cross validation and split validation operators. Data validation with 10-Fold-validation on the SVM algorithm has the best level of performance among 5 other algorithms such as; Naïve Bayes, K-nearest neighbor (K-NN), decision tree (DT), neural network, and random forest (RF). The SVM algorithm produces accuracy and area under the curve (AUC) values of 87.11% for accuracy and 0.928 for AUC. Optimization in this study using optimize selection GA. SVM based on optimize selection GA produces accuracy and AUC values of 87.67% for accuracy and 0.930 for AUC.
Consensus-based secure and efficient compressive sensing in a wireless network sensors environment
Authors: S. Patil Nandini , Parveen Asma
Published: January 2023

Compressive sensing provides the optimal paradigm for data aggregation approach in wireless sensor networks as it holds the characteristics of low traffic cost and avoids the large communication overhead through a traditional approach. However, security is still considered one of the important, in recent years consensus-based approach has been one of the security highlights in compressive sensing. Moreover, a consensus is developed only with the help of genuine nodes. In this research work, a novel integrated-consensus-based secure compressive sensing (CSCS) is proposed which aims to develop a secure consensus environment to secure the network. Integrated-CSCS comprises several modules; at first certain variables are assigned to each node participating in the network. Later an algorithm is developed where each node evaluates the assigned neighbor variable and updates its states. Moreover, this phenomenon leads to the identification of genuine and compromised nodes. The proposed model is evaluated by considering the efficiency and security parameters; efficiency evaluation is carried out through energy consumption, and several node failures where as security is evaluated by considering the ability to identify the genuine node and malicious or corrupt node.
Facial action coding-based facial sub-structures for anxiety emotion classification
Authors: Praditsangthong Rawinan , Bhattarakosol Pattarasinee
Published: January 2023

Most stroke patients usually have problems in communication and body movement, such as speaking, sitting, walking, and picking up items. Moreover, the number of caregivers is smaller than the number of stroke patients. Nevertheless, these patients need 24-hour caregivers for the patients’ safety. Therefore, the objective of this research is to determine patterns of anxiety emotion via facial expressions from the sub-structures on the face, such as the inner brow raiser, brow lower, lid raiser, and lip part. Random samples of 360 facial images from horror-thriller movies based on the internet movie database (IMDb) website were selected. Then, 68 facial landmarks for classifying the emotions were applied to each facial image. The differences in these 68 positions before and after the changed emotions were used as the emotional indicators. Furthermore, these different values are applied to implement a decision tree with all the boundaries of the sub-structures disclosed as a suitable classification model for emotion detection. Consequently, the accuracy when applying this decision tree with other facial images is 83.33%.
Identification scheme of false data injection attack based on deep learning algorithms for smart grids
Authors: Ezzulddin Merza Marwah , H. Hussein Shamil , I. Ali Qutaiba
Published: January 2023

This paper presents the artificial intelligence (AI) techniques based on the deep learning algorithms to diagnose false data injection (FDI) attacks to smart grids with the measurement data in real-time. The power and data flow between end-user consumers and all components of the advanced metering infrastructure (AMI) and supervisory control and data acquisition (SCADA) system in the SG is bidirectional flow by advanced communication networks. For all the advantages of the SG come with, they remain at risk to a series of many potential threats and ongoing attacks. The conditional-deep-belief-network (CDBN) architecture is employed to un-observable FDI attacks which pass the state-vector-estimator (SVE) mechanisms. The IEEE 118 bus, and IEEE 300 bus power system have been used to evaluate our detection scheme. Finally, the suggested CDBN scheme is compared with other detection such as artificial neural network (ANN) and support vector machine (SVM). It is observed that the simulation result shows that suggested detection methods can attain a high accuracy of unobservable FDI attacks.
Effect of harmonics on reduction of unbalance in three-phase four wire composite network
Authors: Durgayandi Ravichandran , Narayanan Muruganantham
Published: January 2023

The unbalanced current drawn by the composite loads in small-scale industries is mainly due to single-phase loads. Any operation of single-phase loads will cause unbalance of current and produce distortion due to non-linear loads. The neutral current is increased and voltage at the load end is reduced due to an increase in unbalanced linear loads. Industries find it difficult to balance the load and control the neutral current. To overcome the problem, this work aims to add a passive network in different configurations along with shunt active power filter. A set of linear and non-linear loads are considered in the network and the results are discussed in this paper. The L and C values are designed to compensate for the required reactive power and the canceling of negative and zero sequence current quantities due to unbalance in the circuit. This paper also reviewed the effect of active filter RMS current at an unbalanced load current.
Search and classify topics in a corpus of text using the latent dirichlet allocation model
Authors: Iparraguirre-Villanueva Orlando , Sierra-Liñan Fernando , Herrera Salazar Jose Luis , Beltozar-Clemente Saul , Pucuhuayla-Revatta Félix , Zapata-Paulini Joselyn , Cabanillas-Carbonell Michael
Published: January 2023

This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.
Approach by modeling to generate an e-commerce web code from laravel model
Authors: Rahmouni M'hamed , Bouzaidi Mouad , Mbarki Samir
Published: January 2023

The world of web is constantly evolving. Today, we no longer speak of a website but of a web application. The growing difficulty of designing web applications has given rise to solutions and tools. The framework is one of them. Providing a serious framework for development by offering strict development rules, as well as generic and out-of-the-box components, PHP laravel framework is one of them. This paper aims to present the different stages of modeling and development of an enterprise resource planning (ERP) system that will be implemented by PHP laravel by defining the main concepts involved in this information system modeling and development process. To lead and implement the end-to-end development of the said system, we apply the model-driven architecture (MDA) approach. This approach is based on atlas transformation language (ATL). The result of this paper is an Ecore file, a reliable MVC2 web model of e-commerce ERP, which will be the input file to generate the aforementioned system code. To validate this approach, we implemented a case study. The result of this work is very satisfying. Thus, we arrived to generate all necessary elements for this ERP code generation by respecting the MVC2 model and the PHP coding.
Convolutional neural network for the detection of Parkinson disease based on hand-draw spiral images
Authors: Alniemi Omar , F. Mahmood Hanaa
Published: January 2023

Parkinson’s disease (PD) is a chronic and increasing sickness that hits hundreds-thousands of people globally. Patients who are infected by PD have been proven to show some common symptoms such as slowness of movement, tremors, and freezing of gait. One of the most popular exams to detect the PD is to use the handwritten assessment tool, where the individuals are asked to draw spirals on a template paper. Therefore, this study proposes a convolutional neural network algorithm for detecting the PD by utilizing the hand-draw spiral images. In the present study, balanced spiral images dataset has been utilized for both categories (i.e., Parkinson and healthy). The dataset contains 102 samples as a total number of spiral images (i.e., 51 Parkinson and 51 healthy). Moreover, numerous evaluation measurements were utilized in order to assess the proposed approach such as recall, precision, accuracy, F-measure, specificity, Matthew's correlation coefficient (MCC), and G-mean. Based on the outcomes of the experiments, the proposed approach achieves 93.33% accuracy, 86.67% specificity, 88.24% precision, 100.00% recall, 93.75% F-measure, 93.93% G-mean, and 87.45% MCC. The proposed approach demonstrates promising outcomes in the detection of PD. As well as the proposed convolutional neural network (CNN) approach was outperformed all its comparatives regarding the classification accuracy rate.
A machine learning approach for driver identification
Authors: Khan Md. Abbas Ali , Hanif Ali Mohammad , Haque Fazlul , Habib Md. Tarek
Published: January 2023

Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.
Integrated approaches in a morphological analyzer of the Arabic language
Authors: Iazzi Said , Iazzi Abderrazak , Gueddah Hicham , Yousfi Abdellah , Bellafkih Mostafa
Published: January 2023

This article presents a systemof a morphological analyzer of the Arabic language, by integrating several approaches and the viterbi algorithm. First approach is based on database for all thesurface patterns in the Arabic language, second approach is Buckwalter Arabic morphological analyzer and the last approach is based on finite state automaton. With the integration of correspondence tables between affixes in these approaches. The combination between these approaches in our analyzer is very important. Our analyzer is tested on a morphological corpus of 200,000 words, which generalize the words of the Arabic language. The effectiveness of the proposed approaches is demonstrated experimentally and the results obtained are comparable to the state of the art. Moreover, it shows the interest and the advantages of integrating these approaches are to improve our morphological analyzer.
NOVA-a virtual nursing assistant
Authors: Bidve Vijaykumar , Virkar Amit , Raut Prajakta , Velapurkar Samruddhi
Published: January 2023

The majority of people are medically unqualified to research or comprehend the severity of their ailments or symptoms. Natural language processing plays a critical role in healthcare in this area. These chatbots collect patient health data and, based on that data, provide more relevant information to patients about their physical ailments, as well as advise next steps. Artificial intelligence (AI)-powered healthcare chatbots are useful in the medical industry for supporting patients and directing them to the most appropriate resources. Chatbots are more useful for online searches that users or patients conduct when they are searching for answers to their health-related questions. With this application, a user can make health requests via text message and might also get relevant health suggestions/recommendations through it. This Chatbot is developed to be both educational and conversational. Chatbot delivers medical information, such as symptoms and remedies for diseases. Patients’ personal and medical information is stored in a database for further study, and patients receive real-time advice from experts. AI-powered apps in healthcare have experienced a significant increase in recent days. As a result, office wait times are reduced, saving money and energy. Patients may be learning medical information and assisting at their own pace and location.
Faster region-based convolutional neural network for plant-parasitic and non-parasitic nematode detection
Authors: Angeline Natalia , Husna Shabrina Nabila , Indarti Siwi
Published: January 2023

Nematodes represent very abundant and the largest species diversity in the world. Nematodes, which live in a soil environment, possess several functions in agricultural systems. There are two huge groups of soil nematodes, a non-parasitic nematode, which contributes positively to ecological processes, and a plant-parasitic nematode, which cause various disease and reduces yield losses in the agricultural system. Early detection and classification in the agricultural area infected with plant-parasitic nematode and interpreting the soil level condition in this area required a fast and reliable detection system. However, nematode identification is challenging and time-consuming due to their similar morphology. This study applied a pre-trained faster region-based convolutional neural network (RCNN) for plant-parasitic and non-parasitic nematodes detection. These deep learning-based object detection models gave satisfactory results as the accuracy reached 87.5%.
Federated learning for scam classification in small Indonesian language dataset: an initial study
Authors: Chen Michael , Kusuma Halim Dareen
Published: January 2023

Most digital phishing or scam trick users into fraudulent links and is more effective against users with low technology literacy, like in Indonesia. Machine learning is widely used for scam classification, but most require sending the messages to a centralized server. This induces privacy concern as messages might contain private data. Federated learning (FL) was proposed to allow user devices to train models locally without sending data to server. In this work, we examined the use of FL with gated recurrent unit (GRU) model for classifying scam messages in Indonesian language with small dataset. We provided two FL-based baseline models (FedAvg and daisy-chained algorithms) and a dataset for scam classification in Indonesian language. We examined the models based on these performance metrics; precision, recall, F1, selectivity, and balanced accuracy. Despite the performance, we pointed out characteristics of the FL algorithms and the hyperparameters for this use case as pointers towards fine-tuning these baseline models. Overall, the FL model with FedAvg algorithm performed better in all metrics except recall.
Fuzzy C-means clustering based on micro-spatial analysis for electricity load profile characterization
Authors: Senen Adri , Putri Tri Wahyu Oktaviana , Jamani Jasrul Jamani , Supriyanto Eko , Anggaini Dwi
Published: January 2023

As the rising of electricity demand, electricity load profile characterization (ELPC) is the integral aspect in planning, operating system, and distribution network development. The approach in the existing ELPC is still relatively macro in nature and does not involve other aspects outside the electricity variable, so the results tend to be biased for areas experiencing rapid land use changes. Therefore, this paper proposes an ELPC approach based on micro-spatial. Microspatial analysis is done by dividing area in the form of the smallest grids involving various electrical, demographic, geographic and socio-economic variables, which are then grouped using adaptive clustering based on fuzzy C-means (FCM). The adaptive clustering algorithm is proven to be able to determine the degree of membership of each grid data against each cluster with the ability to determine the number of clusters automatically according to the attribute data provided. The ELPC results which consist of 5 clusters are then analyzed using descriptive statistic, plotted, and mapped to obtain more accurate and realistic load characteristics in accordance with the pattern and geographical conditions of the region, so that the results can be used as a reference in load forecasting, network development, and distributed generation (DG) integration.